Google Real Dumps Practice Exam Questions by Dumpswarp

Google Professional Data Engineer Exam Questions and Answers

Question 1

You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?

Options:

Disable caching by editing the report settings.

Disable caching in BigQuery by editing table details.

Refresh your browser tab showing the visualizations.

Clear your browser history for the past hour then reload the tab showing the virtualizations.

Question 2

Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with Google BigQuery. Which three approaches can you take? (Choose three.)

Options:

Disable writes to certain tables.

Restrict access to tables by role.

Ensure that the data is encrypted at all times.

Restrict BigQuery API access to approved users.

Segregate data across multiple tables or databases.

Use Google Stackdriver Audit Logging to determine policy violations.

Question 3

Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine. The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?

Options:

Run a local version of Jupiter on the laptop.

Grant the user access to Google Cloud Shell.

Host a visualization tool on a VM on Google Compute Engine.

Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.

Question 4

Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?

Options:

Assign global unique identifiers (GUID) to each data entry.

Compute the hash value of each data entry, and compare it with all historical data.

Store each data entry as the primary key in a separate database and apply an index.

Maintain a database table to store the hash value and other metadata for each data entry.

Question 5

An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted. How should you build this pipeline?

Options:

Use federated data sources, and check data in the SQL query.

Enable BigQuery monitoring in Google Stackdriver and create an alert.

Import the data into BigQuery using the gcloud CLI and set max_bad_records to 0.

Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors to another dead-letter table for analysis.

Question 6

You work for a car manufacturer and have set up a data pipeline using Google Cloud Pub/Sub to capture anomalous sensor events. You are using a push subscription in Cloud Pub/Sub that calls a custom HTTPS endpoint that you have created to take action of these anomalous events as they occur. Your custom HTTPS endpoint keeps getting an inordinate amount of duplicate messages. What is the most likely cause of these duplicate messages?

Options:

The message body for the sensor event is too large.

Your custom endpoint has an out-of-date SSL certificate.

The Cloud Pub/Sub topic has too many messages published to it.

Your custom endpoint is not acknowledging messages within the acknowledgement deadline.

Question 7

You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

Options:

Linear regression

Logistic classification

Recurrent neural network

Feedforward neural network

Question 8

You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

Options:

Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.

Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. Reference the column TS instead of the column DT from now on.

Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.

Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.

Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.

Question 9

Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:

# Syntax error : Expected end of statement but got “-“ at [4:11]

SELECT age

FROM

bigquery-public-data.noaa_gsod.gsod

WHERE

age != 99

AND_TABLE_SUFFIX = ‘1929’

ORDER BY

age DESC

Which table name will make the SQL statement work correctly?

Options:

‘bigquery-public-data.noaa_gsod.gsod‘

bigquery-public-data.noaa_gsod.gsod*

‘bigquery-public-data.noaa_gsod.gsod’*

‘bigquery-public-data.noaa_gsod.gsod*`

Question 10

You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?

Options:

Make a call to the Stackdriver API to list all logs, and apply an advanced filter.

In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.

In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.

Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.

Question 11

Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

Options:

Issue a command to restart the database servers.

Retry the query with exponential backoff, up to a cap of 15 minutes.

Retry the query every second until it comes back online to minimize staleness of data.

Reduce the query frequency to once every hour until the database comes back online.

Question 12

Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour.

The data scientists have written the following code to read the data for a new key features in the logs.

BigQueryIO.Read

.named(“ReadLogData”)

.from(“clouddataflow-readonly:samples.log_data”)

You want to improve the performance of this data read. What should you do?

Options:

Specify the TableReference object in the code.

Use .fromQuery operation to read specific fields from the table.

Use of both the Google BigQuery TableSchema and TableFieldSchema classes.

Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.

Question 13

You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

Options:

There are very few occurrences of mutations relative to normal samples.

There are roughly equal occurrences of both normal and mutated samples in the database.

You expect future mutations to have different features from the mutated samples in the database.

You expect future mutations to have similar features to the mutated samples in the database.

You already have labels for which samples are mutated and which are normal in the database.

Question 14

You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time. What should you do?

Options:

Send the data to Google Cloud Datastore and then export to BigQuery.

Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.

Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.

Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.

Question 15

You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patientrecords. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

Options:

Add capacity (memory and disk space) to the database server by the order of 200.

Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.

Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.

Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.

Question 16

You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users’ privacy?

Options:

Grant the consultant the Viewer role on the project.

Grant the consultant the Cloud Dataflow Developer role on the project.

Create a service account and allow the consultant to log on with it.

Create an anonymized sample of the data for the consultant to work with in a different project.

Question 17

You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying data. Which query type should you use?

Options:

Include ORDER BY DESK on timestamp column and LIMIT to 1.

Use GROUP BY on the unique ID column and timestamp column and SUM on the values.

Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.

Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.

Question 18

You are building a model to make clothing recommendations. You know a user’s fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?

Options:

Continuously retrain the model on just the new data.

Continuously retrain the model on a combination of existing data and the new data.

Train on the existing data while using the new data as your test set.

Train on the new data while using the existing data as your test set.

Question 19

Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data. They want to improve this performance while minimizing cost. What should they do?

Options:

Redefine the schema by evenly distributing reads and writes across the row space of the table.

The performance issue should be resolved over time as the site of the BigDate cluster is increased.

Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.

Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.

Question 20

Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other’s data. You want to ensure appropriate access to the data. Which three steps should you take? (Choose three.)

Options:

Load data into different partitions.

Load data into a different dataset for each client.

Put each client’s BigQuery dataset into a different table.

Restrict a client’s dataset to approved users.

Only allow a service account to access the datasets.

Use the appropriate identity and access management (IAM) roles for each client’s users.

Question 21

Flowlogistic’s management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

Options:

Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

Cloud Pub/Sub, Cloud Dataflow, and Local SSD

Cloud Pub/Sub, Cloud SQL, and Cloud Storage

Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

Question 22

Flowlogistic’s CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they’ve purchased a visualization tool to simplify the creation of BigQuery reports. However, they’ve been overwhelmed by all thedata in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

Options:

Export the data into a Google Sheet for virtualization.

Create an additional table with only the necessary columns.

Create a view on the table to present to the virtualization tool.

Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

Question 23

Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.

Which approach should you take?

Options:

Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.

Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.

Use the NOW () function in BigQuery to record the event’s time.

Use the automatically generated timestamp from Cloud Pub/Sub to order the data.

Question 24

Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?

Options:

Store the common data in BigQuery as partitioned tables.

Store the common data in BigQuery and expose authorized views.

Store the common data encoded as Avro in Google Cloud Storage.

Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.

Question 25

Which Google Cloud Platform service is an alternative to Hadoop with Hive?

Options:

Cloud Dataflow

Cloud Bigtable

BigQuery

Cloud Datastore

Question 26

Which of the following is NOT one of the three main types of triggers that Dataflow supports?

Options:

Trigger based on element size in bytes

Trigger that is a combination of other triggers

Trigger based on element count

Trigger based on time

Question 27

Which of the following are feature engineering techniques? (Select 2 answers)

Options:

Hidden feature layers

Feature prioritization

Crossed feature columns

Bucketization of a continuous feature

Question 28

You have a job that you want to cancel. It is a streaming pipeline, and you want to ensure that any data that is in-flight is processed and written to the output. Which of the following commands can you use on the Dataflow monitoring console to stop the pipeline job?

Options:

Cancel

Drain

Stop

Finish

Question 29

How can you get a neural network to learn about relationships between categories in a categorical feature?

Options:

Create a multi-hot column

Create a one-hot column

Create a hash bucket

Create an embedding column

Question 30

When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a ____ proxy.

Options:

HTTPS

VPN

SOCKS

HTTP

Question 31

All Google Cloud Bigtable client requests go through a front-end server ______ they are sent to a Cloud Bigtable node.

Options:

before

after

only if

once

Question 32

Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

Options:

Hive

Pig

YARN

Spark

Question 33

Why do you need to split a machine learning dataset into training data and test data?

Options:

So you can try two different sets of features

To make sure your model is generalized for more than just the training data

To allow you to create unit tests in your code

So you can use one dataset for a wide model and one for a deep model

Question 34

Cloud Dataproc charges you only for what you really use with _____ billing.

Options:

month-by-month

minute-by-minute

week-by-week

hour-by-hour

Question 35

What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?

Options:

create a third instance and sync the data from the two storage types via batch jobs

export the data from the existing instance and import the data into a new instance

run parallel instances where one is HDD and the other is SDD

the selection is final and you must resume using the same storage type

Question 36

Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

Options:

BETWEEN

WHERE

SELECT

LIMIT

Question 37

What is the HBase Shell for Cloud Bigtable?

Options:

The HBase shell is a GUI based interface that performs administrative tasks, such as creating and deleting tables.

The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables.

The HBase shell is a hypervisor based shell that performs administrative tasks, such as creating and deleting new virtualized instances.

The HBase shell is a command-line tool that performs only user account management functions to grant access to Cloud Bigtable instances.

Question 38

What are two of the benefits of using denormalized data structures in BigQuery?

Options:

Reduces the amount of data processed, reduces the amount of storage required

Increases query speed, makes queries simpler

Reduces the amount of storage required, increases query speed

Reduces the amount of data processed, increases query speed

Question 39

Which Java SDK class can you use to run your Dataflow programs locally?

Options:

LocalRunner

DirectPipelineRunner

MachineRunner

LocalPipelineRunner

Question 40

Which of the following is not true about Dataflow pipelines?

Options:

Pipelines are a set of operations

Pipelines represent a data processing job

Pipelines represent a directed graph of steps

Pipelines can share data between instances

Question 41

MJTelco’s Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

Options:

The zone

The number of workers

The disk size per worker

The maximum number of workers

Question 42

Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day’s events. They also want to use streaming ingestion. What should you do?

Options:

Create a table called tracking_table and include a DATE column.

Create a partitioned table called tracking_table and include a TIMESTAMP column.

Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.

Create a table called tracking_table with a TIMESTAMP column to represent the day.

Question 43

MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

Options:

Rowkey: date#device_idColumn data: data_point

Rowkey: dateColumn data: device_id, data_point

Rowkey: device_idColumn data: date, data_point

Rowkey: data_pointColumn data: device_id, date

Rowkey: date#data_pointColumn data: device_id

Question 44

You need to compose visualization for operations teams with the following requirements:

Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)

The report must not be more than 3 hours delayed from live data.

The actionable report should only show suboptimal links.

Most suboptimal links should be sorted to the top.

Suboptimal links can be grouped and filtered by regional geography.

User response time to load the report must be <5 seconds.

You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?

Options:

Look through the current data and compose a series of charts and tables, one for each possiblecombination of criteria.

Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.

Export the data to a spreadsheet, compose a series of charts and tables, one for each possiblecombination of criteria, and spread them across multiple tabs.

Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.

Question 45

MJTelco is building a custom interface to share data. They have these requirements:

They need to do aggregations over their petabyte-scale datasets.

They need to scan specific time range rows with a very fast response time (milliseconds).

Which combination of Google Cloud Platform products should you recommend?

Options:

Cloud Datastore and Cloud Bigtable

Cloud Bigtable and Cloud SQL

BigQuery and Cloud Bigtable

BigQuery and Cloud Storage

Question 46

You need to compose visualizations for operations teams with the following requirements:

Which approach meets the requirements?

Options:

Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.

Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.

Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.

Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.

Question 47

You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.

Which two actions should you take? (Choose two.)

Options:

Ensure all the tables are included in global dataset.

Ensure each table is included in a dataset for a region.

Adjust the settings for each table to allow a related region-based security group view access.

Adjust the settings for each view to allow a related region-based security group view access.

Adjust the settings for each dataset to allow a related region-based security group view access.

Question 48

You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

Options:

Cloud SQL

Cloud Bigtable

Cloud Spanner

Cloud Datastore

Question 49

You are building an application to share financial market data with consumers, who will receive data feeds. Data is collected from the markets in real time. Consumers will receive the data in the following ways:

Real-time event stream

ANSI SQL access to real-time stream and historical data

Batch historical exports

Which solution should you use?

Options:

Cloud Dataflow, Cloud SQL, Cloud Spanner

Cloud Pub/Sub, Cloud Storage, BigQuery

Cloud Dataproc, Cloud Dataflow, BigQuery

Cloud Pub/Sub, Cloud Dataproc, Cloud SQL

Question 50

You are preparing an organization-wide dataset. You need to preprocess customer data stored in a restricted bucket in Cloud Storage. The data will be used to create consumer analyses. You need to follow data privacy requirements, including protecting certain sensitive data elements, while also retaining all of the data for potential future use cases. What should you do?

Options:

Use Dataflow and the Cloud Data Loss Prevention API to mask sensitive data. Write the processed data in BigQuery.

Use the Cloud Data Loss Prevention API and Dataflow to detect and remove sensitive fields from the data in Cloud Storage. Write the filtered data in BigQuery.

Use Dataflow and Cloud KMS to encrypt sensitive fields and write the encrypted data in BigQuery. Share the encryption key by following the principle of least privilege.

Use customer-managed encryption keys (CMEK) to directly encrypt the data in Cloud Storage. Use federated queries from BigQuery. Share the encryption key by following the principle of least privilege.

Answer:

Explanation:

The core requirements are to protect sensitive data elements (data privacy) while retainingalldata for potential future use, and then using this preprocessed data for consumer analyses.

Retaining All Data:This immediately makes option B (remove sensitive fields) unsuitable because it involves data loss.

Protecting Sensitive Data for Analysis & Future Use:Masking is a de-identification technique that redacts or replaces sensitive data with a substitute, allowing the data structure and usability for analysis to be maintained without exposing the original sensitive values. This aligns with protecting data while still making it usable.

Cloud Data Loss Prevention (DLP) API:This service is specifically designed to discover, classify, and protect sensitive data. It offers various de-identification techniques, including masking.

Dataflow:This is a serverless, fast, and cost-effective service for unified stream and batch data processing. It's well-suited for transforming large datasets, such as those read from Cloud Storage, and can integrate with the DLP API for de-identification.

Writing to BigQuery:BigQuery is an ideal destination for an organization-wide dataset for consumer analyses.

Therefore, using Dataflow to read the data from Cloud Storage, leveraging the Cloud DLP API tomask(a form of de-identification) the sensitive elements, and then writing the processed (masked) data to BigQuery is the most appropriate solution. This approach protects privacy for the consumer analyses dataset while the original, unaltered data can still be retained in the restricted Cloud Storage bucket for future use cases that might require access to the original sensitive information (under strict governance).

Let's analyze why other options are less suitable:

Option B:"Remove sensitive fields" means data loss, which contradicts the requirement to retain all data for potential future use cases.

Option C:Encrypting sensitive fields with Cloud KMS and writing them to BigQuery is a valid way to protect data. However, for "consumer analyses," masked data is generally more directly usable than encrypted data. Analysts would typically work with de-identified (e.g., masked) data rather than directly querying encrypted fields and managing decryption keys for analytical purposes. While decryption is possible, masking often provides a better balance of privacy and utility for broad analysis. The question also implies creating a datasetforanalysis, where masking makes the data ready-to-use for that purpose. The original data remains in Cloud Storage.

Option D:Using CMEK encrypts the entire object in Cloud Storage at rest. While this protects the data in Cloud Storage, federated queries from BigQuery would access the raw, unmasked data (assuming decryption occurs seamlessly). This doesn't address the preprocessing requirement of protectingcertain sensitive data elementswithin the data itself for theconsumer analysesdataset. The goal is to create a de-identified dataset for analysis, not just secure the raw data at rest.

[Reference:, Google Cloud Documentation: Cloud Data Loss Prevention > De-identification overview. "De-identification is the process of removing identifying information from data. Cloud DLP uses de-identification techniques such as masking, tokenization, pseudonymization, date shifting, and more to help you protect sensitive data.", Google Cloud Documentation: Cloud Data Loss Prevention > Basic de-identification > Masking. "Masking hides parts of data by replacing characters with a symbol, such as an asterisk (*) or hash (#).", Google Cloud Documentation: Dataflow > Overview. "Dataflow is a fully managed streaming analytics service that minimizes latency, processing time, and cost through autoscaling and batch processing.", Google Cloud Solution: Automating the de-identification of PII in large-scale datasets using Cloud DLP and Dataflow. This solution guide explicitly outlines using Dataflow and DLP API for de-identifying (including masking) data from Cloud Storage and loading it into BigQuery. "You can use Cloud DLP to scan data for sensitive elements andthen apply de-identification techniques such as redaction, masking, or tokenization." and "This tutorial uses Dataflow to orchestrate the de-identification process.", , , ]

Question 51

After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You’ve loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison.

What should you do?

Options:

Select random samples from the tables using the RAND() function and compare the samples.

Select random samples from the tables using the HASH() function and compare the samples.

Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sorting. Compare the hashes of each table.

Create stratified random samples using the OVER() function and compare equivalent samples from each table.

Question 52

You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

Options:

Create a table in BigQuery, and append the new samples for CPU and memory to the table

Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second

Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second

Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.

Question 53

You want to build a managed Hadoop system as your data lake. The data transformation process is composed of a series of Hadoop jobs executed in sequence. To accomplish the design of separating storage from compute, you decided to use the Cloud Storage connector to store all input data, output data, and intermediary data. However, you noticed that one Hadoop job runsvery slowly with Cloud Dataproc, when compared with the on-premises bare-metal Hadoop environment (8-core nodes with 100-GB RAM). Analysis shows that this particular Hadoop job is disk I/O intensive. You want to resolve the issue. What should you do?

Options:

Allocate sufficient memory to the Hadoop cluster, so that the intermediary data of that particular Hadoop job can be held in memory

Allocate sufficient persistent disk space to the Hadoop cluster, and store the intermediate data of that particular Hadoop job on native HDFS

Allocate more CPU cores of the virtual machine instances of the Hadoop cluster so that the networking bandwidth for each instance can scale up

Allocate additional network interface card (NIC), and configure link aggregation in the operating system to use the combined throughput when working with Cloud Storage

Question 54

You currently have transactional data stored on-premises in a PostgreSQL database. To modernize your data environment, you want to run transactional workloads and support analytics needs with a single database. You need to move to Google Cloud without changing database management systems, and minimize cost and complexity. What should you do?

Options:

Migrate your workloads to AlloyDB for PostgreSQL.

Migrate to BigQuery to optimize analytics.

Migrate and modernize your database with Cloud Spanner.

Migrate your PostgreSQL database to Cloud SQL for PostgreSQL.

Answer:

Explanation:

The key requirements are:

On-premises PostgreSQL database.

Run transactional workloads AND support analytics needs with a single database.

Move to Google Cloud without changing database management systems (i.e., remain PostgreSQL-compatible).

Minimize cost and complexity.

AlloyDB for PostgreSQL (Option A) is the best fit for these requirements.

PostgreSQL-Compatible: AlloyDB is fully PostgreSQL-compatible, meaning minimal to no application changes are required ("without changing database management systems").

Transactional and Analytical Workloads: AlloyDB is designed to handle demanding transactional workloads while also providing significantly faster analytical query performance compared to standard PostgreSQL. It achieves this through its intelligent, database-optimized storage layer and columnar engine integration. This addresses the "single database" for both needs.

Cost and Complexity: As a managed service, it reduces operational complexity. Its performance benefits for both OLTP and OLAP can lead to better cost-efficiency by handling mixed workloads effectively on a single system.

Let's analyze why other options are less suitable:

B (Migrate to BigQuery): BigQuery is an analytical data warehouse, not designed for transactional workloads. This violates the "single database" for both types of workloads and "without changing database management systems" (as BigQuery is not PostgreSQL).

C (Migrate to Cloud Spanner): Cloud Spanner is a globally distributed, horizontally scalable relational database. While excellent for high-availability transactional workloads, it has its own SQL dialect (ANSI 2011 with extensions, not fully PostgreSQL wire-compatible without tools like PGAdapter, which adds complexity) and a different architecture. This would involve more significant changes than moving to a PostgreSQL-compatible system. The requirement was "without changing database management systems."

D (Migrate to Cloud SQL for PostgreSQL): Cloud SQL for PostgreSQL is a fully managed PostgreSQL service. It's excellent for transactional workloads and simpler analytical queries. However, for more demanding analytical needs on the same database instance, AlloyDB is specifically optimized to provide superior performance due to its architectural enhancements (like the columnar engine). If the analytical needs are significant, AlloyDB offers a better converged experience. While Cloud SQL is PostgreSQL-compatible, AlloyDB is positioned for superior performance on mixed workloads.

[Reference:, , Google Cloud Documentation: AlloyDB for PostgreSQL > Overview. "AlloyDB for PostgreSQL is a fully managed, PostgreSQL-compatible database service for your most demandingtransactional and analytical workloads... AlloyDB offers full PostgreSQL compatibility, so you can migrate your existing PostgreSQL applications with no code changes.", Google Cloud Documentation: AlloyDB for PostgreSQL > Key benefits. Highlights include "Industry-leading performance: ...up to 100x faster analytical queries than standard PostgreSQL." and "Support for transactional and analytical workloads: AlloyDB is designed to efficiently handle both transactional and analytical queries, allowing you to use a single database for a wide range of applications.", , , , ]

Question 55

You are developing a model to identify the factors that lead to sales conversions for your customers. You have completed processing your data. You want to continue through the model development lifecycle. What should you do next?

Options:

Use your model to run predictions on fresh customer input data.

Test and evaluate your model on your curated data to determine how well the model performs.

Monitor your model performance, and make any adjustments needed.

Delineate what data will be used for testing and what will be used for training the model.

Question 56

You are running a streaming pipeline with Dataflow and are using hopping windows to group the data as the data arrives. You noticed that some data is arriving late but is not being marked as late data, which is resulting in inaccurate aggregations downstream. You need to find a solution that allows you to capture the late data in the appropriate window. What should you do?

Options:

Change your windowing function to session windows to define your windows based on certain activity.

Change your windowing function to tumbling windows to avoid overlapping window periods.

Expand your hopping window so that the late data has more time to arrive within the grouping.

Use watermarks to define the expected data arrival window Allow late data as it arrives.

Question 57

Your infrastructure team has set up an interconnect link between Google Cloud and the on-premises network. You are designing a high-throughput streaming pipeline to ingest data in streaming from an Apache Kafka cluster hosted on-premises. You want to store the data in BigQuery, with as minima! latency as possible. What should you do?

Options:

Use a proxy host in the VPC in Google Cloud connecting to Kafka. Write a Dataflow pipeline, read data from the proxy host, and write the data to BigQuery.

Setup a Kafka Connect bridge between Kafka and Pub/Sub. Use a Google-provided Dataflow template to read the data from Pub/Sub, and write the data to BigQuery.

Setup a Kafka Connect bridge between Kafka and Pub/Sub. Write a Dataflow pipeline, read the data from Pub/Sub, and write the data toBigQuery.

Use Dataflow, write a pipeline that reads the data from Kafka, and writes the data to BigQuery.

Answer:

Explanation:

Here's a detailed breakdown of why this solution is optimal and why others fall short:

Why Option C is the Best Solution:

Kafka Connect Bridge:This bridge acts as a reliable and scalable conduit between your on-premises Kafka cluster and Google Cloud's Pub/Sub messaging service. It handles the complexities of securely transferring data over the interconnect link.

Pub/Sub as a Buffer:Pub/Sub serves as a highly scalable buffer, decoupling the Kafka producer from the Dataflow consumer. This is crucial for handling fluctuations in message volume and ensuring smooth data flow even during spikes.

Custom Dataflow Pipeline:Writing a custom Dataflow pipeline gives you the flexibility to implement any necessary transformations or enrichments to the data before it's written to BigQuery. This is often required in real-world streaming scenarios.

Minimal Latency:By using Pub/Sub as a buffer and Dataflow for efficient processing, you minimize the latency between the data being produced in Kafka and being available for querying in BigQuery.

Why Other Options Are Not Ideal:

Option A:Using a proxy host introduces an additional point of failure and can create a bottleneck, especially with high-throughput streaming.

Option B:While Google-provided Dataflow templates can be helpful, they might lack the customization needed for specific transformations or handling complex data structures.

Option D:Dataflow doesn't natively connect to on-premises Kafka clusters. Directly reading from Kafka would require complex networking configurations and could lead to performance issues.

Additional Considerations:

Schema Management:Ensure that the schema of the data being produced in Kafka is compatible with the schema expected in BigQuery. Consider using tools like Schema Registry for schema evolution management.

Monitoring:Set up robust monitoring and alerting to detect any issues in the pipeline, such as message backlogs or processing errors.

By following Option C, you leverage the strengths of Kafka Connect, Pub/Sub, and Dataflow to create a high-throughput, low-latency streaming pipeline that seamlessly integrates your on-premises Kafka data with BigQuery.

Question 58

You are loading CSV files from Cloud Storage to BigQuery. The files have known data quality issues, including mismatched data types, such as STRINGS and INT64s in the same column, andinconsistent formatting of values such as phone numbers or addresses. You need to create the data pipeline to maintain data quality and perform the required cleansing and transformation. What should you do?

Options:

Use Data Fusion to transform the data before loading it into BigQuery.

Load the CSV files into a staging table with the desired schema, perform the transformations with SQL. and then write the results to the final destination table.

Create a table with the desired schema, toad the CSV files into the table, and perform the transformations in place using SQL.

Use Data Fusion to convert the CSV files lo a self-describing data formal, such as AVRO. before loading the data to BigOuery.

Question 59

You are running your BigQuery project in the on-demand billing model and are executing a change data capture (CDC) process that ingests data. The CDC process loads 1 GB of data every 10 minutes into a temporary table, and then performs a merge into a 10 TB target table. This process is very scan intensive and you want to explore options to enable a predictable cost model. You need to create a BigQuery reservation based on utilization information gathered from BigQuery Monitoring and apply the reservation to the CDC process. What should you do?

Options:

Create a BigQuery reservation for the job.

Create a BigQuery reservation for the service account running the job.

Create a BigQuery reservation for the dataset.

Create a BigQuery reservation for the project.

Answer:

Explanation:

Here's why creating a BigQuery reservation for the project is the most suitable solution:

Project-Level Reservation:BigQuery reservations are applied at the project level. This means that the reserved slots (processing capacity) are shared across all jobs and queries running within that project. Since your CDC process is a significant contributor to your BigQuery usage, reserving slots for the entire project ensures that your CDC process always has access to the necessary resources, regardless of other activities in the project.

Predictable Cost Model:Reservations provide a fixed, predictable cost model. Instead of paying the on-demand price for each query, you pay a fixed monthly fee for the reserved slots. This eliminates the variability of costs associated with on-demand billing, making it easier to budget and forecast your BigQuery expenses.

BigQuery Monitoring:You can use BigQuery Monitoring to analyze the historical usage patterns of your CDC process and other queries within your project. This informationhelps you determine the appropriate amount of slots to reserve, ensuring that you have enough capacity to handle your workload while optimizing costs.

Why other options are not suitable:

A. Create a BigQuery reservation for the job:BigQuery does not support reservations at the individual job level. Reservations are applied at the project or assignment level.

B. Create a BigQuery reservation for the service account running the job:While you can create reservations for assignments (groups of users or service accounts), it's less efficient than a project-level reservation in this scenario. A project-level reservation covers all jobs within the project, regardless of the service account used.

C. Create a BigQuery reservation for the dataset:BigQuery does not support reservations at the dataset level.

By creating a BigQuery reservation for your project based on your utilization analysis, you can achieve a predictable cost model while ensuring that your CDC process and other queries have the necessary resources to run smoothly.

Question 60

You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30–90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

Options:

Re-create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.

Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.

Modify your pipeline to maintain the last 30–90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.

Write an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

Question 61

You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

Options:

Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.

Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.

Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery.

Question 62

You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.

You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)

Options:

Redis

HBase

MySQL

MongoDB

Cassandra

HDFS with Hive

Question 63

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

Options:

Load the data every 30 minutes into a new partitioned table in BigQuery.

Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

Question 64

You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:

The user profile: What the user likes and doesn’t like to eat

The user account information: Name, address, preferred meal times

The order information: When orders are made, from where, to whom

The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?

Options:

BigQuery

Cloud SQL

Cloud Bigtable

Cloud Datastore

Question 65

Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?

Options:

Rewrite the job in Pig.

Rewrite the job in Apache Spark.

Increase the size of the Hadoop cluster.

Decrease the size of the Hadoop cluster but also rewrite the job in Hive.

Question 66

Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

Options:

The CSV data loaded in BigQuery is not flagged as CSV.

The CSV data has invalid rows that were skipped on import.

The CSV data loaded in BigQuery is not using BigQuery’s default encoding.

The CSV data has not gone through an ETL phase before loading into BigQuery.

Question 67

You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity ‘Movie’ the property ‘actors’ and the property ‘tags’ have multiple values but the property ‘date released’ does not. A typical query would ask for all movies with actor= ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

Options:

Option A

Option B.

Option C

Option D

Question 68

You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?

Options:

Change the processing job to use Google Cloud Dataproc instead.

Manually start the Cloud Dataflow job each morning when you get into the office.

Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.

Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.

Question 69

Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.

You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

Options:

Introduce data compression for each file to increase the rate file of file transfer.

Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.

Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.

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