Microsoft DP-203 Dumps

Box 1: Round-robin

Round-robin tables are useful for improving loading speed.

Scenario: Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month.

Box 2: Hash

Hash-distributed tables improve query performance on large fact tables.

Scenario: Purge Twitter feed data records that are older than two years.

Data sets have unique lifecycles. Early in the lifecycle, people access some data often. But the need for access often drops drastically as the data ages. Some data remains idle in the cloud and is rarely accessed once stored. Some data sets expire days or months after creation, while other data sets are actively read and modified throughout their lifetimes. Azure Storage lifecycle management offers a rule-based policy that you can use to transition blob data to the appropriate access tiers or to expire data at the end of the data lifecycle.

Box 1: Sales date

Scenario: Contoso requirements for data integration include:

Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.

Box 2: An Azure Synapse Analytics Dedicated SQL pool

Scenario: Contoso requirements for data integration include:

Ensure that data storage costs and performance are predictable.

The size of a dedicated SQL pool (formerly SQL DW) is determined by Data Warehousing Units (DWU).

Dedicated SQL pool (formerly SQL DW) stores data in relational tables with columnar storage. This format significantly reduces the data storage costs, and improves query performance.

Synapse analytics dedicated sql pool

Scenario: Allow Contoso users to use PolyBase in an Azure Synapse Analytics dedicated SQL pool to query the content of the data records that host the Twitter feeds. Data must be protected by using row-level security (RLS). The users must be authenticated by using their own Azure AD credentials.

Box 1: CREATE EXTERNAL DATA SOURCE

External data sources are used to connect to storage accounts.

Box 2: CREATE EXTERNAL FILE FORMAT

CREATE EXTERNAL FILE FORMAT creates an external file format object that defines external data stored in Azure Blob Storage or Azure Data Lake Storage. Creating an external file format is a prerequisite for creating an external table.

Box 3: CREATE EXTERNAL TABLE AS SELECT

When used in conjunction with the CREATE TABLE AS SELECT statement, selecting from an external table imports data into a table within the SQL pool. In addition to the COPY statement, external tables are useful for loading data.

Box 1: Configure Evegent Hubs partitions

Scenario: Maximize the throughput of ingesting Twitter feeds from Event Hubs to Azure Storage without purchasing additional throughput or capacity units.

Event Hubs is designed to help with processing of large volumes of events. Event Hubs throughput is scaled by using partitions and throughput-unit allocations.

Event Hubs traffic is controlled by TUs (standard tier). Auto-inflate enables you to start small with the minimum required TUs you choose. The feature then scales automatically to the maximum limit of TUs you need, depending on the increase in your traffic.

Box 2: An Azure Data Lake Storage Gen2 account

Scenario: Ensure that the data store supports Azure AD-based access control down to the object level.

Azure Data Lake Storage Gen2 implements an access control model that supports both Azure role-based access control (Azure RBAC) and POSIX-like access control lists (ACLs).

Box 1: Hash

Scenario:

Ensure that queries joining and filtering sales transaction records based on product ID complete as quickly as possible.

A hash distributed table can deliver the highest query performance for joins and aggregations on large tables.

Box 2: Set the distribution column to the sales date.

Scenario: Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.

Scenario: Identify a process to ensure that changes to the ingestion and transformation activities can be version-controlled and developed independently by multiple data engineers.

Step 1: Create a repository and a main branch

You need a Git repository in Azure Pipelines, TFS, or GitHub with your app.

Step 2: Create a feature branch

Step 3: Create a pull request

Step 4: Merge changes

Merge feature branches into the main branch using pull requests.

Step 5: Publish changes

Box 1: Create table

Scenario: Load the sales transaction dataset to Azure Synapse Analytics

Box 2: RANGE RIGHT FOR VALUES

Scenario: Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.

RANGE RIGHT: Specifies the boundary value belongs to the partition on the right (higher values).

FOR VALUES ( boundary_value [,...n] ): Specifies the boundary values for the partition.

Scenario: Load the sales transaction dataset to Azure Synapse Analytics.

Contoso identifies the following requirements for the sales transaction dataset:

Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.

Ensure that queries joining and filtering sales transaction records based on product ID complete as quickly as possible.

Implement a surrogate key to account for changes to the retail store addresses.

Ensure that data storage costs and performance are predictable.

Minimize how long it takes to remove old records.

Scenario: Implement a surrogate key to account for changes to the retail store addresses.

A surrogate key on a table is a column with a unique identifier for each row. The key is not generated from the table data. Data modelers like to create surrogate keys on their tables when they design data warehouse models. You can use the IDENTITY property to achieve this goal simply and effectively without affecting load performance.

Scenario: All cloud data must be encrypted at rest and in transit.

Always Encrypted is a feature designed to protect sensitive data stored in specific database columns from access (for example, credit card numbers, national identification numbers, or data on a need to know basis). This includes database administrators or other privileged users who are authorized to access the database to perform management tasks, but have no business need to access the particular data in the encrypted columns. The data is always encrypted, which means the encrypted data is decrypted only for processing by client applications with access to the encryption key.

Box 1: Self-hosted integration runtime

A self-hosted IR is capable of running copy activity between a cloud data stores and a data store in private network.

Box 2: Schedule trigger

Schedule every 8 hours

Box 3: Copy activity

Scenario:

Customer data, including name, contact information, and loyalty number, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.

Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.

Guarantee Stream Analytics job reliability during service updates

Part of being a fully managed service is the capability to introduce new service functionality and improvements at a rapid pace. As a result, Stream Analytics can have a service update deploy on a weekly (or more frequent) basis. No matter how much testing is done there is still a risk that an existing, running job may break due to the introduction of a bug. If you are running mission critical jobs, these risks need to be avoided. You can reduce this risk by following Azure’s paired region model.

Scenario: The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty number, price, and discount amount, from the point of sale (POS) system and output the data to data storage in Azure

Virtual network rules are one firewall security feature that controls whether the database server for your single databases and elastic pool in Azure SQL Database or for your databases in SQL Data Warehouse accepts communications that are sent from particular subnets in virtual networks.

Server-level, not database-level: Each virtual network rule applies to your whole Azure SQL Database server, not just to one particular database on the server. In other words, virtual network rule applies at the serverlevel, not at the database-level.

Event-driven architecture (EDA) is a common data integration pattern that involves production, detection, consumption, and reaction to events. Data integration scenarios often require Data Factory customers to trigger

pipelines based on events happening in storage account, such as the arrival or deletion of a file in Azure Blob Storage account.

To create a bar chart that displays sales by product while minimizing development effort, follow these steps in the correct order:

Correct Order

Create a SQL script by using Synapse Studio.

Start by writing a SQL query in Synapse Studio that retrieves the sales by product data from the data stored in Azure Data Lake.

Add a SELECT statement that will return the sales by product data.

Write the query in the SQL script, such as:

sql

Copy

SELECT ProductName, SUM(SalesAmount) AS TotalSales

FROM OPENROWSET(

BULK ' ',

FORMAT='CSV',

DATA_SOURCE='MyDataSource'

) AS SalesData

GROUP BY ProductName

ORDER BY TotalSales DESC;

Execute the script.

Run the SQL script to retrieve the data and ensure it returns the correct results.

Switch to the Chart view.

In Synapse Studio, switch to the Chart view to visualize the query results.

Modify the Chart settings.

Customize the chart settings to display a bar chart with "ProductName" on the x-axis and "TotalSales" on the y-axis.

Explanation

The sequence ensures that you first retrieve the data correctly, verify it by executing the query, and then switch to the Chart view to visualize the data with minimal development effort.

If you need to transform data in a way that is not supported by Data Factory, you can create a custom activity, not an Azure Databricks notebook, with your own data processing logic and use the activity in the pipeline. You can create a custom activity to run R scripts on your HDInsight cluster with R installed.

Box 1: fail until the node comes back online

We see: High Availability Enabled: False

Note: Higher availability of the self-hosted integration runtime so that it's no longer the single point of failure in your big data solution or cloud data integration with Data Factory.

Box 2: lowered

We see:

Concurrent Jobs (Running/Limit): 2/14

CPU Utilization: 6%

Note: When the processor and available RAM aren't well utilized, but the execution of concurrent jobs reaches a node's limits, scale up by increasing the number of concurrent jobs that a node can run

Hopping window functions hop forward in time by a fixed period. It may be easy to think of them as Tumbling windows that can overlap and be emitted more often than the window size. Events can belong to more than one Hopping window result set. To make a Hopping window the same as a Tumbling window, specify the hop size to be the same as the window size.

Azure SQL Database currently supports encryption at rest for Microsoft-managed service side and client-side encryption scenarios.

Support for server encryption is currently provided through the SQL feature called Transparent Data Encryption.

Client-side encryption of Azure SQL Database data is supported through the Always Encrypted feature.

Customer Scenario:

SQL Server 2016 or SQL DW connected to Azure blob storage. The CREATE EXTERNAL TABLE DDL points to a directory (and not a specific file) and the directory contains files with different schemas.

SSMS Error:

Select query on the external table gives the following error:

Msg 7320, Level 16, State 110, Line 14

Cannot execute the query "Remote Query" against OLE DB provider "SQLNCLI11" for linked server "(null)". Query aborted-- the maximum reject threshold (0 rows) was reached while reading from an external source: 1 rows rejected out of total 1 rows processed.

Possible Reason:

The reason this error happens is because each file has different schema. The PolyBase external table DDL when pointed to a directory recursively reads all the files in that directory. When a column or data type mismatch happens, this error could be seen in SSMS.

Possible Solution:

If the data for each table consists of one file, then use the filename in the LOCATION section prepended by the directory of the external files. If there are multiple files per table, put each set of files into different directories in Azure Blob Storage and then you can point LOCATION to the directory instead of a particular file. The latter suggestion is the best practices recommended by SQLCAT even if you have one file per table.

Box 1: GRANT

You can implement column-level security with the GRANT T-SQL statement.

Box 2: CREATE SECURITY POLICY

Implement Row Level Security by using the CREATE SECURITY POLICY Transact-SQL statement

Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals. The following diagram illustrates a stream with a series of events and how they are mapped into 10-second tumbling windows.

A High Concurrency cluster is a managed cloud resource. The key benefits of High Concurrency clusters are that they provide fine-grained sharing for maximum resource utilization and minimum query latencies.

Databricks chooses the appropriate number of workers required to run your job. This is referred to as autoscaling. Autoscaling makes it easier to achieve high cluster utilization, because you don’t need to provision the cluster to match a workload.

There are a number of resource constraints that can cause the streaming pipeline to slow down. The watermark delay metric can rise due to:

Not enough processing resources in Stream Analytics to handle the volume of input events.

Not enough throughput within the input event brokers, so they are throttled.

Output sinks are not provisioned with enough capacity, so they are throttled. The possible solutions vary widely based on the flavor of output service being used.

A Type 3 SCD supports storing two versions of a dimension member as separate columns. The table includes a column for the current value of a member plus either the original or previous value of the member. So Type 3 uses additional columns to track one key instance of history, rather than storing additional rows to track each change like in a Type 2 SCD.

This type of tracking may be used for one or two columns in a dimension table. It is not common to use it for many members of the same table. It is often used in combination with Type 1 or Type 2 members.

Box 1: 0

The YearlyIncome column is of the money data type.

The Default masking function: Full masking according to the data types of the designated fields

Use a zero value for numeric data types (bigint, bit, decimal, int, money, numeric, smallint, smallmoney, tinyint, float, real).

Box 2: the values stored in the database

Users with administrator privileges are always excluded from masking, and see the original data without any mask.

Step 1: Assign a managed identity to Server1

You will need an existing Managed Instance as a prerequisite.

Step 2: Create an Azure key vault and grant the managed identity permissions to the vault

Create Resource and setup Azure Key Vault.

Step 3: Add key1 to the Azure key vault

The recommended way is to import an existing key from a .pfx file or get an existing key from the vault. Alternatively, generate a new key directly in Azure Key Vault.

Step 4: Configure key1 as the TDE protector for Server1

Provide TDE Protector key

Step 5: Enable TDE on Pool1

Type 2 - Creating a new additional record. In this methodology all history of dimension changes is kept in the database. You capture attribute change by adding a new row with a new surrogate key to the dimension table. Both the prior and new rows contain as attributes the natural key(or other durable identifier). Also 'effective date' and 'current indicator' columns are used in this method. There could be only one record with current indicator set to 'Y'. For 'effective date' columns, i.e. start_date and end_date, the end_date for current record usually is set to value 9999-12-31. Introducing changes to the dimensional model in type 2 could be very expensive database operation so it is not recommended to use it in dimensions where a new attribute could be added in the future.