Microsoft Microsoft Certifications DP-203 Questions & Answers

• Question 111:

  HOTSPOT

  You are processing streaming data from vehicles that pass through a toll booth.

  You need to use Azure Stream Analytics to return the license plate, vehicle make, and hour the last vehicle passed during each 10-minute window.

  How should you complete the query? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: MAX The first step on the query finds the maximum time stamp in 10-minute windows, that is the time stamp of the last event for that window. The second step joins the results of the first query with the original stream to find the event that match the last time stamps in each window.

  Query: WITH LastInWindow AS (

  SELECT MAX(Time) AS LastEventTime FROM Input TIMESTAMP BY Time GROUP BY TumblingWindow(minute, 10) )

  SELECT Input.License_plate, Input.Make, Input.Time

  FROM Input TIMESTAMP BY Time INNER JOIN LastInWindow ON DATEDIFF(minute, Input, LastInWindow) BETWEEN 0 AND 10 AND Input.Time = LastInWindow.LastEventTime

  Box 2: TumblingWindow

  Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals.

  Box 3: DATEDIFF

  DATEDIFF is a date-specific function that compares and returns the time difference between two DateTime fields, for more information, refer to date functions.

  Reference:

  https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics

• Question 112:

  HOTSPOT

  You plan to create a real-time monitoring app that alerts users when a device travels more than 200 meters away from a designated location.

  You need to design an Azure Stream Analytics job to process the data for the planned app. The solution must minimize the amount of code developed and the number of technologies used.

  What should you include in the Stream Analytics job? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Input type: Stream

  You can process real-time IoT data streams with Azure Stream Analytics.

  Function: Geospatial

  With built-in geospatial functions, you can use Azure Stream Analytics to build applications for scenarios such as fleet management, ride sharing, connected cars, and asset tracking.

  Note: In a real-world scenario, you could have hundreds of these sensors generating events as a stream. Ideally, a gateway device would run code to push these events to Azure Event Hubs or Azure IoT Hubs.

  Reference:

  https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-get-started-with-azure-stream-analytics-to-process-data-from-iot-devices

  https://docs.microsoft.com/en-us/azure/stream-analytics/geospatial-scenarios

• Question 113:

  HOTSPOT

  You are designing an Azure Synapse Analytics dedicated SQL pool.

  Groups will have access to sensitive data in the pool as shown in the following table.

  

  You have policies for the sensitive data. The policies vary be region as shown in the following table.

  

  You have a table of patients for each region. The tables contain the following potentially sensitive columns.

  

  You are designing dynamic data masking to maintain compliance.

  For each of the following statements, select Yes if the statement is true. Otherwise, select No.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Reference: https://docs.microsoft.com/en-us/azure/azure-sql/database/dynamic-data-masking-overview

• Question 114:

  HOTSPOT

  You develop a dataset named DBTBL1 by using Azure Databricks.

  DBTBL1 contains the following columns:

  1.

  SensorTypeID

  2.

  GeographyRegionID

  3.

  Year

  4.

  Month

  5.

  Day

  6.

  Hour

  7.

  Minute

  8.

  Temperature

  9.

  WindSpeed 10.Other

  You need to store the data to support daily incremental load pipelines that vary for each GeographyRegionID. The solution must minimize storage costs.

  How should you complete the code? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: .partitionBy

  Incorrect Answers:

  .format:

  Method: format():

  Arguments: "parquet", "csv", "txt", "json", "jdbc", "orc", "avro", etc.

  .bucketBy:

  Method: bucketBy()

  Arguments: (numBuckets, col, col..., coln)

  The number of buckets and names of columns to bucket by. Uses Hive's bucketing scheme on a filesystem.

  Box 2: ("Year", "Month", "Day","GeographyRegionID")

  Specify the columns on which to do the partition. Use the date columns followed by the GeographyRegionID column.

  Box 3: .saveAsTable("/DBTBL1")

  Method: saveAsTable()

  Argument: "table_name"

  The table to save to.

  Reference:

  https://www.oreilly.com/library/view/learning-spark-2nd/9781492050032/ch04.html

  https://docs.microsoft.com/en-us/azure/databricks/delta/delta-batch

• Question 115:

  HOTSPOT

  You need to collect application metrics, streaming query events, and application log messages for an Azure Databrick cluster.

  Which type of library and workspace should you implement? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  You can send application logs and metrics from Azure Databricks to a Log Analytics workspace. It uses the Azure Databricks Monitoring Library, which is available on GitHub.

  Reference: https://docs.microsoft.com/en-us/azure/architecture/databricks-monitoring/application-logs

• Question 116:

  HOTSPOT

  You have an Azure subscription that contains an Azure Data Lake Storage account. The storage account contains a data lake named DataLake1.

  You plan to use an Azure data factory to ingest data from a folder in DataLake1, transform the data, and land the data in another folder.

  You need to ensure that the data factory can read and write data from any folder in the DataLake1 file system. The solution must meet the following requirements:

  1.

  Minimize the risk of unauthorized user access.

  2.

  Use the principle of least privilege.

  3.

  Minimize maintenance effort.

  How should you configure access to the storage account for the data factory? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: Azure Active Directory (Azure AD)

  On Azure, managed identities eliminate the need for developers having to manage credentials by providing an identity for the Azure resource in Azure AD and using it to obtain Azure Active Directory (Azure AD) tokens.

  Box 2: a managed identity

  A data factory can be associated with a managed identity for Azure resources, which represents this specific data factory. You can directly use this managed identity for Data Lake Storage Gen2 authentication, similar to using your own

  service principal. It allows this designated factory to access and copy data to or from your Data Lake Storage Gen2.

  Note: The Azure Data Lake Storage Gen2 connector supports the following authentication types.

  1.

  Account key authentication

  2.

  Service principal authentication

  3.

  Managed identities for Azure resources authentication

  Reference: https://docs.microsoft.com/en-us/azure/active-directory/managed-identities-azure-resources/overview https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-data-lake-storage

• Question 117:

  HOTSPOT

  You have an Azure subscription that contains a logical Microsoft SQL server named Server1. Server1 hosts an Azure Synapse Analytics SQL dedicated pool named Pool1.

  You need to recommend a Transparent Data Encryption (TDE) solution for Server1. The solution must meet the following requirements:

  1.

  Track the usage of encryption keys.

  2.

  Maintain the access of client apps to Pool1 in the event of an Azure datacenter outage that affects the availability of the encryption keys.

  What should you include in the recommendation? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: TDE with customer-managed keys

  Customer-managed keys are stored in the Azure Key Vault. You can monitor how and when your key vaults are accessed, and by whom. You can do this by enabling logging for Azure Key Vault, which saves information in an Azure storage

  account that you provide.

  Box 2: Create and configure Azure key vaults in two Azure regions

  The contents of your key vault are replicated within the region and to a secondary region at least 150 miles away, but within the same geography to maintain high durability of your keys and secrets.

  Reference:

  https://docs.microsoft.com/en-us/azure/synapse-analytics/security/workspaces-encryption

  https://docs.microsoft.com/en-us/azure/key-vault/general/logging

• Question 118:

  HOTSPOT

  You have an on-premises data warehouse that includes the following fact tables. Both tables have the following columns: DateKey, ProductKey, RegionKey. There are 120 unique product keys and 65 unique region keys.

  

  Queries that use the data warehouse take a long time to complete.

  You plan to migrate the solution to use Azure Synapse Analytics. You need to ensure that the Azure-based solution optimizes query performance and minimizes processing skew.

  What should you recommend? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point

  Hot Area:

  

  Correct Answer:

  

  Box 1: Hash-distributed

  Box 2: ProductKey

  ProductKey is used extensively in joins.

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

  Box 3: Round-robin

  Box 4: RegionKey

  Round-robin tables are useful for improving loading speed.

  Consider using the round-robin distribution for your table in the following scenarios:

  When getting started as a simple starting point since it is the default If there is no obvious joining key

  If there is not good candidate column for hash distributing the table If the table does not share a common join key with other tables If the join is less significant than other joins in the query When the table is a temporary staging table

  Note: A distributed table appears as a single table, but the rows are actually stored across 60 distributions. The rows are distributed with a hash or round-robin algorithm.

• Question 119:

  HOTSPOT

  You have an Azure event hub named retailhub that has 16 partitions. Transactions are posted to retailhub. Each transaction includes the transaction ID, the individual line items, and the payment details. The transaction ID is used as the partition key. You are designing an Azure Stream Analytics job to identify potentially fraudulent transactions at a retail store. The job will use retailhub as the input. The job will output the transaction ID, the individual line items, the payment details, a fraud

  score, and a fraud indicator.

  You plan to send the output to an Azure event hub named fraudhub.

  You need to ensure that the fraud detection solution is highly scalable and processes transactions as quickly as possible.

  How should you structure the output of the Stream Analytics job? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: 16

  For Event Hubs you need to set the partition key explicitly.

  An embarrassingly parallel job is the most scalable scenario in Azure Stream Analytics. It connects one partition of the input to one instance of the query to one partition of the output.

  Box 2: Transaction ID

  Reference:

  https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-features#partitions

• Question 120:

  HOTSPOT

  You have a self-hosted integration runtime in Azure Data Factory.

  The current status of the integration runtime has the following configurations:

  1.

  Status: Running

  2.

  Type: Self-Hosted

  3.

  Version: 4.4.7292.1

  4.

  Running / Registered Node(s): 1/1

  5.

  High Availability Enabled: False

  6.

  Linked Count: 0

  7.

  Queue Length: 0

  8.

  Average Queue Duration. 0.00s

  The integration runtime has the following node details:

  Name: X-M Status: Running Version: 4.4.7292.1 Available Memory: 7697MB CPU Utilization: 6% Network (In/Out): 1.21KBps/0.83KBps Concurrent Jobs (Running/Limit): 2/14 Role: Dispatcher/Worker Credential Status: In Sync

  Use the drop-down menus to select the answer choice that completes each statement based on the information presented.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  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

  Reference:

  https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime