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Google Professional-Data-Engineer Dumps

Google Professional Data Engineer Exam Questions and Answers

Question 1

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:

A.

BigQuery

B.

Cloud SQL

C.

Cloud Bigtable

D.

Cloud Datastore

Question 2

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:

A.

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

B.

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

C.

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

D.

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

Question 3

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:

A.

Rewrite the job in Pig.

B.

Rewrite the job in Apache Spark.

C.

Increase the size of the Hadoop cluster.

D.

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

Question 4

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:

A.

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

B.

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

C.

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

D.

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.

E.

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.

Question 5

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:

A.

Change the processing job to use Google Cloud Dataproc instead.

B.

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

C.

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

D.

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

Question 6

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:

A.

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

B.

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

C.

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

D.

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 7

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?

as

as

Options:

A.

Option A

B.

Option B.

C.

Option C

D.

Option D

Question 8

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:

A.

Redis

B.

HBase

C.

MySQL

D.

MongoDB

E.

Cassandra

F.

HDFS with Hive

Question 9

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:

A.

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

B.

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.

C.

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.

D.

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 10

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

Options:

A.

LocalRunner

B.

DirectPipelineRunner

C.

MachineRunner

D.

LocalPipelineRunner

Question 11

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

Options:

A.

before

B.

after

C.

only if

D.

once

Question 12

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:

A.

Cancel

B.

Drain

C.

Stop

D.

Finish

Question 13

Which of the following is not possible using primitive roles?

Options:

A.

Give a user viewer access to BigQuery and owner access to Google Compute Engine instances.

B.

Give UserA owner access and UserB editor access for all datasets in a project.

C.

Give a user access to view all datasets in a project, but not run queries on them.

D.

Give GroupA owner access and GroupB editor access for all datasets in a project.

Question 14

Which is not a valid reason for poor Cloud Bigtable performance?

Options:

A.

The workload isn't appropriate for Cloud Bigtable.

B.

The table's schema is not designed correctly.

C.

The Cloud Bigtable cluster has too many nodes.

D.

There are issues with the network connection.

Question 15

To run a TensorFlow training job on your own computer using Cloud Machine Learning Engine, what would your command start with?

Options:

A.

gcloud ml-engine local train

B.

gcloud ml-engine jobs submit training

C.

gcloud ml-engine jobs submit training local

D.

You can't run a TensorFlow program on your own computer using Cloud ML Engine .

Question 16

Which of the following are examples of hyperparameters? (Select 2 answers.)

Options:

A.

Number of hidden layers

B.

Number of nodes in each hidden layer

C.

Biases

D.

Weights

Question 17

Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)

Options:

A.

The wide model is used for memorization, while the deep model is used for generalization.

B.

A good use for the wide and deep model is a recommender system.

C.

The wide model is used for generalization, while the deep model is used for memorization.

D.

A good use for the wide and deep model is a small-scale linear regression problem.

Question 18

If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?

Options:

A.

Unsupervised learning

B.

Regressor

C.

Classifier

D.

Clustering estimator

Question 19

Which of these statements about BigQuery caching is true?

Options:

A.

By default, a query's results are not cached.

B.

BigQuery caches query results for 48 hours.

C.

Query results are cached even if you specify a destination table.

D.

There is no charge for a query that retrieves its results from cache.

Question 20

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:

A.

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

B.

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

C.

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

D.

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

Question 21

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:

A.

Store the common data in BigQuery as partitioned tables.

B.

Store the common data in BigQuery and expose authorized views.

C.

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

D.

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

Question 22

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:

A.

Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

B.

Cloud Pub/Sub, Cloud Dataflow, and Local SSD

C.

Cloud Pub/Sub, Cloud SQL, and Cloud Storage

D.

Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

Question 23

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:

A.

Export the data into a Google Sheet for virtualization.

B.

Create an additional table with only the necessary columns.

C.

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

D.

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

Question 24

You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:

    Decoupling producer from consumer

    Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely

    Near real-time SQL query

    Maintain at least 2 years of historical data, which will be queried with SQ

Which pipeline should you use to meet these requirements?

Options:

A.

Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.

B.

Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.

C.

Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.

D.

Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.

Question 25

You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?

Options:

A.

Use Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable data.

B.

Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.

C.

Use Cloud Storage for storage. Link as permanent tables in BigQuery for query.

D.

Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.

Question 26

A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

Options:

A.

Implement clustering in BigQuery on the ingest date column.

B.

Implement clustering in BigQuery on the package-tracking ID column.

C.

Tier older data onto Cloud Storage files, and leverage extended tables.

D.

Re-create the table using data partitioning on the package delivery date.

Question 27

Your company is migrating its on-premises data warehousing solution to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply daily updates from transactional database sources Your company wants to use BigQuery to improve its handling of CDC and to optimize the performance of the data warehouse Source system changes must be available for query m near-real time using tog-based CDC streams You need to ensure that changes in the BigQuery reporting table are available with minimal latency and reduced overhead. What should you do? Choose 2 answers

Options:

A.

Perform a DML INSERT UPDATE, or DELETE to replicate each CDC record in the reporting table m real time.

B.

Periodically DELETE outdated records from the reporting table

Periodically use a DML MERGE to simultaneously perform DML INSERT. UPDATE, and DELETE operations in the reporting table

C.

Insert each new CDC record and corresponding operation type into a staging table in real time

D.

Insert each new CDC record and corresponding operation type into the reporting table in real time and use a materialized view to expose only the current version of each unique record.

Question 28

You need to look at BigQuery data from a specific table multiple times a day. The underlying table you are querying is several petabytes in size, but you want to filter your data and provide simple aggregations to downstream users. You want to run queries faster and get up-to-date insights quicker. What should you do?

Options:

A.

Run a scheduled query to pull the necessary data at specific intervals daily.

B.

Create a materialized view based off of the query being run.

C.

Use a cached query to accelerate time to results.

D.

Limit the query columns being pulled in the final result.

Question 29

You are using Workflows to call an API that returns a 1 KB JSON response, apply some complex business logic on this response, wait for the logic to complete, and then perform a load from a Cloud Storage file to BigQuery. The Workflows standard library does not have sufficient capabilities to perform your complex logic, and you want to use Python's standard library instead. You want to optimize your workflow for simplicity and speed of execution. What should you do?

Options:

A.

Invoke a Cloud Function instance that uses Python to apply the logic on your JSON file.

B.

Invoke a subworkflow in Workflows to apply the logic on your JSON file.

C.

Create a Cloud Composer environment and run the logic in Cloud Composer.

D.

Create a Dataproc cluster, and use PySpark to apply the logic on your JSON file.

Question 30

You are migrating a large number of files from a public HTTPS endpoint to Cloud Storage. The files are protected from unauthorized access using signed URLs. You created a TSV file that contains the list of object URLs and started a transfer job by using Storage Transfer Service. You notice that the job has run for a long time and eventually failed Checking the logs of the transfer job reveals that the job was running fine until one point, and then it failed due to HTTP 403 errors on the remaining files You verified that there were no changes to the source system You need to fix the problem to resume the migration process. What should you do?

Options:

A.

Set up Cloud Storage FUSE, and mount the Cloud Storage bucket on a Compute Engine Instance Remove the completed files from the TSV file Use a shell script to iterate through the TSV file and download the remaining URLs to the FUSE mount point.

B.

Update the file checksums in the TSV file from using MD5 to SHA256. Remove the completed files from the TSV file and rerun the Storage Transfer Service job.

C.

Renew the TLS certificate of the HTTPS endpoint Remove the completed files from the TSV file and rerun the Storage Transfer Service job.

D.

Create a new TSV file for the remaining files by generating signed URLs with a longer validity period. Split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel.

Question 31

You are architecting a data transformation solution for BigQuery. Your developers are proficient with SOL and want to use the ELT development technique. In addition, your developers need an intuitive coding environment and the ability to manage SQL as code. You need to identify a solution for your developers to build these pipelines. What should you do?

Options:

A.

Use Cloud Composer to load data and run SQL pipelines by using the BigQuery job operators.

B.

Use Dataflow jobs to read data from Pub/Sub, transform the data, and load the data to BigQuery.

C.

Use Dataform to build, manage, and schedule SQL pipelines.

D.

Use Data Fusion to build and execute ETL pipelines

Question 32

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:

A.

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

B.

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.

C.

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.

D.

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.

Question 33

You have important legal hold documents in a Cloud Storage bucket. You need to ensure that these documents are not deleted or modified. What should you do?

Options:

A.

Set a retention policy. Lock the retention policy.

B.

Set a retention policy. Set the default storage class to Archive for long-term digital preservation.

C.

Enable the Object Versioning feature. Add a lifecycle rule.

D.

Enable the Object Versioning feature. Create a copy in a bucket in a different region.

Question 34

You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?

Options:

A.

Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name

B.

Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to a new unique job name

C.

Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code

D.

Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code

Question 35

An organization maintains a Google BigQuery dataset that contains tables with user-level datA. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

Options:

A.

Create and share an authorized view that provides the aggregate results.

B.

Create and share a new dataset and view that provides the aggregate results.

C.

Create and share a new dataset and table that contains the aggregate results.

D.

Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.

Question 36

You’re training a model to predict housing prices based on an available dataset with real estate properties. Your plan is to train a fully connected neural net, and you’ve discovered that the dataset contains latitude and longtitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you’d like to engineer a feature that incorporates this physical dependency.

What should you do?

Options:

A.

Provide latitude and longtitude as input vectors to your neural net.

B.

Create a numeric column from a feature cross of latitude and longtitude.

C.

Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.

D.

Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.

Question 37

You need to create a SQL pipeline. The pipeline runs an aggregate SOL transformation on a BigQuery table every two hours and appends the result to another existing BigQuery table. You need to configure the pipeline to retry if errors occur. You want the pipeline to send an email notification after three consecutive failures. What should you do?

Options:

A.

Create a BigQuery scheduled query to run the SOL transformation with schedule options that repeats every two hours, and enable email

notifications.

B.

Use the BigQueryUpsertTableOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter to

true.

C.

Use the BigQuerylnsertJobOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter to

true.

D.

Create a BigQuery scheduled query to run the SQL transformation with schedule options that repeats every two hours, and enable

notification to Pub/Sub topic. Use Pub/Sub and Cloud Functions to send an email after three tailed executions.

Question 38

You are deploying MariaDB SQL databases on GCE VM Instances and need to configure monitoring and alerting. You want to collect metrics including network connections, disk IO and replication status from MariaDB with minimal development effort and use StackDriver for dashboards and alerts.

What should you do?

Options:

A.

Install the OpenCensus Agent and create a custom metric collection application with a StackDriver exporter.

B.

Place the MariaDB instances in an Instance Group with a Health Check.

C.

Install the StackDriver Logging Agent and configure fluentd in_tail plugin to read MariaDB logs.

D.

Install the StackDriver Agent and configure the MySQL plugin.

Question 39

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:

A.

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

B.

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

C.

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

D.

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 40

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:

A.

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

B.

Enable BigQuery monitoring in Google Stackdriver and create an alert.

C.

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

D.

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

Question 41

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:

A.

Disable writes to certain tables.

B.

Restrict access to tables by role.

C.

Ensure that the data is encrypted at all times.

D.

Restrict BigQuery API access to approved users.

E.

Segregate data across multiple tables or databases.

F.

Use Google Stackdriver Audit Logging to determine policy violations.

Question 42

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:

A.

Run a local version of Jupiter on the laptop.

B.

Grant the user access to Google Cloud Shell.

C.

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

D.

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

Question 43

You have Google Cloud Dataflow streaming pipeline running with a Google Cloud Pub/Sub subscription as the source. You need to make an update to the code that will make the new Cloud Dataflow pipeline incompatible with the current version. You do not want to lose any data when making this update. What should you do?

Options:

A.

Update the current pipeline and use the drain flag.

B.

Update the current pipeline and provide the transform mapping JSON object.

C.

Create a new pipeline that has the same Cloud Pub/Sub subscription and cancel the old pipeline.

D.

Create a new pipeline that has a new Cloud Pub/Sub subscription and cancel the old pipeline.

Question 44

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:

A.

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

B.

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

C.

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.

D.

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 45

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:

A.

Issue a command to restart the database servers.

B.

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

C.

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

D.

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

Question 46

You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:

    No interaction by the user on the site for 1 hour

    Has added more than $30 worth of products to the basket

    Has not completed a transaction

You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?

Options:

A.

Use a fixed-time window with a duration of 60 minutes.

B.

Use a sliding time window with a duration of 60 minutes.

C.

Use a session window with a gap time duration of 60 minutes.

D.

Use a global window with a time based trigger with a delay of 60 minutes.

Question 47

Your company’s customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations. The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations. What should you do?

Options:

A.

Add a node to the MySQL cluster and build an OLAP cube there.

B.

Use an ETL tool to load the data from MySQL into Google BigQuery.

C.

Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.

D.

Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.

Question 48

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:

A.

Linear regression

B.

Logistic classification

C.

Recurrent neural network

D.

Feedforward neural network

Question 49

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:

A.

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

B.

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

C.

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

D.

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

Question 50

Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

Options:

A.

Create a Google Cloud Dataflow job to process the data.

B.

Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.

C.

Create a Hadoop cluster on Google Compute Engine that uses persistent disks.

D.

Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.

E.

Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.

Question 51

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:

A.

‘bigquery-public-data.noaa_gsod.gsod‘

B.

bigquery-public-data.noaa_gsod.gsod*

C.

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

D.

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

Question 52

You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.

Which Google database service should you use?

Options:

A.

Cloud SQL

B.

BigQuery

C.

Cloud Bigtable

D.

Cloud Datastore

Question 53

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:

A.

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

B.

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

C.

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

D.

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 54

You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

Options:

A.

Re-write the application to load accumulated data every 2 minutes.

B.

Convert the streaming insert code to batch load for individual messages.

C.

Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.

D.

Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

Question 55

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:

A.

Load data into different partitions.

B.

Load data into a different dataset for each client.

C.

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

D.

Restrict a client’s dataset to approved users.

E.

Only allow a service account to access the datasets.

F.

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

Question 56

Your company built a TensorFlow neural-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

Options:

A.

Threading

B.

Serialization

C.

Dropout Methods

D.

Dimensionality Reduction

Question 57

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:

A.

The message body for the sensor event is too large.

B.

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

C.

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

D.

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

Question 58

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:

A.

The zone

B.

The number of workers

C.

The disk size per worker

D.

The maximum number of workers

Question 59

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:

A.

Create a table called tracking_table and include a DATE column.

B.

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

C.

Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.

D.

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

Question 60

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:

A.

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

combination of criteria.

B.

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

C.

Export the data to a spreadsheet, compose a series of charts and tables, one for each possible

combination of criteria, and spread them across multiple tabs.

D.

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 61

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:

A.

Cloud Datastore and Cloud Bigtable

B.

Cloud Bigtable and Cloud SQL

C.

BigQuery and Cloud Bigtable

D.

BigQuery and Cloud Storage

Question 62

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:

A.

Ensure all the tables are included in global dataset.

B.

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

C.

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

D.

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

E.

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

Question 63

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

Which approach meets the requirements?

Options:

A.

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

B.

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.

C.

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.

D.

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 64

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:

A.

Rowkey: date#device_idColumn data: data_point

B.

Rowkey: dateColumn data: device_id, data_point

C.

Rowkey: device_idColumn data: date, data_point

D.

Rowkey: data_pointColumn data: device_id, date

E.

Rowkey: date#data_pointColumn data: device_id

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