Microsoft Microsoft Certifications DP-203 Questions & Answers

• Question 191:

  You have an Azure Stream Analytics job.

  You need to ensure that the job has enough streaming units provisioned

  You configure monitoring of the SU % Utilization metric.

  Which two additional metrics should you monitor? Each correct answer presents part of the solution.

  NOTE Each correct selection is worth one point

  A. Backlogged Input Events

  B. Watermark Delay

  C. Watermark Delay

  D. Out of order Events

  E. Late Input Events

  Correct Answer: AB

  To react to increased workloads and increase streaming units, consider setting an alert of 80% on the SU Utilization metric. Also, you can use watermark delay and backlogged events metrics to see if there is an impact.

  Note: Backlogged Input Events: Number of input events that are backlogged. A non-zero value for this metric implies that your job isn't able to keep up with the number of incoming events. If this value is slowly increasing or consistently nonzero, you should scale out your job, by increasing the SUs.

  Reference: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-monitoring

• Question 192:

  You have an Azure Databricks workspace named workspace! in the Standard pricing tier. Workspace contains an all-purpose cluster named cluster.

  You need to reduce the time it takes for cluster 1 to start and scale up. The solution must minimize costs.

  What should you do first?

  A. Upgrade workspace! to the Premium pricing tier.

  B. Create a cluster policy in workspace1.

  C. Create a pool in workspace1.

  D. Configure a global init script for workspace1.

  Correct Answer: C

  You can use Databricks Pools to Speed up your Data Pipelines and Scale Clusters Quickly.

  Databricks Pools, a managed cache of virtual machine instances that enables clusters to start and scale 4 times faster.

  Reference:

  https://databricks.com/blog/2019/11/11/databricks-pools-speed-up-data-pipelines.html

• Question 193:

  You are designing an Azure Synapse Analytics workspace.

  You need to recommend a solution to provide double encryption of all the data at rest.

  Which two components should you include in the recommendation? Each coned answer presents part of the solution

  NOTE: Each correct selection is worth one point.

  A. an X509 certificate

  B. an RSA key

  C. an Azure key vault that has purge protection enabled

  D. an Azure virtual network that has a network security group (NSG)

  E. an Azure Policy initiative

  Correct Answer: BE

  Synapse workspaces encryption uses existing keys or new keys generated in Azure Key Vault. A single key is used to encrypt all the data in a workspace. Synapse workspaces support RSA 2048 and 3072 byte-sized keys, and RSA-HSM keys.

  The Key Vault itself needs to have purge protection enabled.

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

• Question 194:

  Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while

  others might not have a correct solution.

  After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

  You plan to create an Azure Databricks workspace that has a tiered structure. The workspace will contain the following three workloads:

  1.

  A workload for data engineers who will use Python and SQL.

  2.

  A workload for jobs that will run notebooks that use Python, Scala, and SOL.

  3.

  A workload that data scientists will use to perform ad hoc analysis in Scala and R.

  The enterprise architecture team at your company identifies the following standards for Databricks environments:

  1.

  The data engineers must share a cluster.

  2.

  The job cluster will be managed by using a request process whereby data scientists and data engineers provide packaged notebooks for deployment to the cluster.

  3.

  All the data scientists must be assigned their own cluster that terminates automatically after 120 minutes of inactivity. Currently, there are three data scientists.

  You need to create the Databricks clusters for the workloads.

  Solution: You create a Standard cluster for each data scientist, a High Concurrency cluster for the data engineers, and a High Concurrency cluster for the jobs.

  Does this meet the goal?

  A. Yes

  B. No

  Correct Answer: A

  We need a High Concurrency cluster for the data engineers and the jobs.

  Note:

  Standard clusters are recommended for a single user. Standard can run workloads developed in any language:

  Python, R, Scala, and SQL.

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

  Reference:

  https://docs.azuredatabricks.net/clusters/configure.html

• Question 195:

  You have an Azure Data Factory pipeline that is triggered hourly.

  The pipeline has had 100% success for the past seven days.

  The pipeline execution fails, and two retries that occur 15 minutes apart also fail. The third failure returns the following error.

  ErrorCode=UserErrorFileNotFound,'Type=Microsoft.DataTransfer.Common.Shared.HybridDeliveryException,Message=ADLS Gen2 operation failed for: Operation returned an invalid statuscode 'NotFound'. Account: 'contosoproduksouth'. Filesystem: wwi. Path: 'BIKES/CARBON/year=2021/month=01/day=10/hour=06'. ErrorCode: 'PathNotFound'. Message: 'The specified pathdoes not exist.'. RequestId: '6d269b78-901f-001b-4924-e7a7bc000000'. TimeStamp: 'Sun, 10 Jan 2021 07:45:05

  What is a possible cause of the error?

  A. From 06.00 to 07:00 on January 10.2021 there was no data in w1/bikes/CARBON.

  B. The parameter used to generate year.2021/month=0/day=10/hour=06 was incorrect

  C. From 06:00 to 07:00 on January 10,2021 the file format of data wi/BiKES/CARBON was incorrect

  D. The pipeline was triggered too early.

  Correct Answer: B

• Question 196:

  You have an Azure Synapse Analytics Apache Spark pool named Pool1.

  You plan to load JSON files from an Azure Data Lake Storage Gen2 container into the tables in Pool1. The structure and data types vary by file.

  You need to load the files into the tables. The solution must maintain the source data types.

  What should you do?

  A. Use a Get Metadata activity in Azure Data Factory.

  B. Use a Conditional Split transformation in an Azure Synapse data flow.

  C. Load the data by using the OPEHROwset Transact-SQL command in an Azure Synapse Anarytics serverless SQL pool.

  D. Load the data by using PySpark.

  Correct Answer: A

  Serverless SQL pool can automatically synchronize metadata from Apache Spark. A serverless SQL pool database will be created for each database existing in serverless Apache Spark pools.

  Serverless SQL pool enables you to query data in your data lake. It offers a T-SQL query surface area that accommodates semi-structured and unstructured data queries. To support a smooth experience for in place querying of data that's

  located in Azure Storage files, serverless SQL pool uses the OPENROWSET function with additional capabilities.

  The easiest way to see to the content of your JSON file is to provide the file URL to the OPENROWSET function, specify csv FORMAT.

  Reference:

  https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/query-json-files

  https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/query-data-storage

• Question 197:

  You have an Azure data factory.

  You need to examine the pipeline failures from the last 180 flays.

  What should you use?

  A. the Activity tog blade for the Data Factory resource

  B. Azure Data Factory activity runs in Azure Monitor

  C. Pipeline runs in the Azure Data Factory user experience

  D. the Resource health blade for the Data Factory resource

  Correct Answer: B

  Data Factory stores pipeline-run data for only 45 days. Use Azure Monitor if you want to keep that data for a longer time.

  Reference: https://docs.microsoft.com/en-us/azure/data-factory/monitor-using-azure-monitor

• Question 198:

  You need to design a solution that will process streaming data from an Azure Event Hub and output the data to Azure Data Lake Storage. The solution must ensure that analysts can interactively query the streaming data. What should you use?

  A. event triggers in Azure Data Factory

  B. Azure Stream Analytics and Azure Synapse notebooks

  C. Structured Streaming in Azure Databricks

  D. Azure Queue storage and read-access geo-redundant storage (RA-GRS)

  Correct Answer: C

  Apache Spark Structured Streaming is a fast, scalable, and fault-tolerant stream processing API. You can use it to perform analytics on your streaming data in near real-time. With Structured Streaming, you can use SQL queries to process streaming data in the same way that you would process static data.

  Azure Event Hubs is a scalable real-time data ingestion service that processes millions of data in a matter of seconds. It can receive large amounts of data from multiple sources and stream the prepared data to Azure Data Lake or Azure Blob storage.

  Azure Event Hubs can be integrated with Spark Structured Streaming to perform the processing of messages in near real-time. You can query and analyze the processed data as it comes by using a Structured Streaming query and Spark SQL.

  Reference: https://k21academy.com/microsoft-azure/data-engineer/structured-streaming-with-azure-event-hubs/

• Question 199:

  You have an Azure Synapse Analytics dedicated SQL pool named SA1 that contains a table named Table1.

  You need to identify tables that have a high percentage of deleted rows.

  What should you run?

  A. sys.pdw_nodes_column_store_segments

  B. sys.dm_db_column_store_row_group_operational_stats

  C. sys.pdw_nodes_column_store_row_groups

  D. sys.dm_db_column_store_row_group_physical_stats

  Correct Answer: C

  Use sys.pdw_nodes_column_store_row_groups to determine which row groups have a high percentage of deleted rows and should be rebuilt.

  Note: sys.pdw_nodes_column_store_row_groups provides clustered columnstore index information on a per-segment basis to help the administrator make system management decisions in Azure Synapse Analytics.

  sys.pdw_nodes_column_store_row_groups has a column for the total number of rows physically stored (including those marked as deleted) and a column for the number of rows marked as deleted.

  Incorrect:

  Not A: You can join sys.pdw_nodes_column_store_segments with other system tables to determine the number of columnstore segments per logical table.

  Not B: Use sys.dm_db_column_store_row_group_operational_stats to track the length of time a user query must wait to read or write to a compressed rowgroup or partition of a columnstore index, and identify rowgroups that are encountering

  significant I/O activity or hot spots.

• Question 200:

  You have an enterprise data warehouse in Azure Synapse Analytics.

  You need to monitor the data warehouse to identify whether you must scale up to a higher service level to accommodate the current workloads

  Which is the best metric to monitor?

  More than one answer choice may achieve the goal. Select the BEST answer.

  A. DWU used

  B. CPU percentage

  C. DWU percentage

  D. Data IO percentage

  Correct Answer: C