Microsoft Microsoft Certifications DP-203 Questions & Answers

• Question 291:

  You create an Azure Databricks cluster and specify an additional library to install.

  When you attempt to load the library to a notebook, the library in not found.

  You need to identify the cause of the issue.

  What should you review?

  A. notebook logs

  B. cluster event logs

  C. global init scripts logs

  D. workspace logs

  Correct Answer: C

  Cluster-scoped Init Scripts: Init scripts are shell scripts that run during the startup of each cluster node before the Spark driver or worker JVM starts. Databricks customers use init scripts for various purposes such as installing custom libraries, launching background processes, or applying enterprise security policies.

  Logs for Cluster-scoped init scripts are now more consistent with Cluster Log Delivery and can be found in the same root folder as driver and executor logs for the cluster.

  Reference: https://databricks.com/blog/2018/08/30/introducing-cluster-scoped-init-scripts.html

• Question 292:

  You have an Azure Synapse Analytics dedicated SQL pool that contains a large fact table.

  The table contains 50 columns and 5 billion rows and is a heap.

  Most queries against the table aggregate values from approximately 100 million rows and return only two columns.

  You discover that the queries against the fact table are very slow.

  Which type of index should you add to provide the fastest query times?

  A. nonclustered columnstore

  B. clustered columnstore

  C. nonclustered

  D. clustered

  Correct Answer: B

  Clustered columnstore indexes are one of the most efficient ways you can store your data in dedicated SQL pool. Columnstore tables won't benefit a query unless the table has more than 60 million rows.

  Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/best-practices-dedicated-sql-pool

• Question 293:

  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 Standard cluster for the jobs.

  Does this meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  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 294:

  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 295:

  You have a partitioned table in an Azure Synapse Analytics dedicated SQL pool. You need to design queries to maximize the benefits of partition elimination. What should you include in the Transact-SQL queries?

  A. JOIN

  B. WHERE

  C. DISTINCT

  D. GROUP BY

  Correct Answer: B

• Question 296:

  You implement an enterprise data warehouse in Azure Synapse Analytics.

  You have a large fact table that is 10 terabytes (TB) in size.

  Incoming queries use the primary key SaleKey column to retrieve data as displayed in the following table:

  

  You need to distribute the large fact table across multiple nodes to optimize performance of the table. Which technology should you use?

  A. hash distributed table with clustered index

  B. hash distributed table with clustered Columnstore index

  C. round robin distributed table with clustered index

  D. round robin distributed table with clustered Columnstore index

  E. heap table with distribution replicate

  Correct Answer: B

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

  Columnstore indexes can achieve up to 100x better performance on analytics and data warehousing workloads and up to 10x better data compression than traditional rowstore indexes.

  Incorrect Answers:

  C, D: Round-robin tables are useful for improving loading speed.

  Reference:

  https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute https://docs.microsoft.com/en-us/sql/relational-databases/indexes/columnstore-indexes-query-performance

• Question 297:

  You are designing an Azure Stream Analytics solution that will analyze Twitter data.

  You need to count the tweets in each 10-second window. The solution must ensure that each tweet is counted only once.

  Solution: You use a hopping window that uses a hop size of 5 seconds and a window size 10 seconds.

  Does this meet the goal?

  A. Yes

  B. No

  Correct Answer: B

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

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

• Question 298:

  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 SQL.

  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 High Concurrency cluster for each data scientist, a High Concurrency cluster for the data engineers, and a Standard cluster for the jobs.

  Does this meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  Need a High Concurrency cluster for the jobs.

  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 299:

  You have an Azure Databricks workspace named workspace1 in the Standard pricing tier.

  You need to configure workspace1 to support autoscaling all-purpose clusters. The solution must meet the following requirements:

  Automatically scale down workers when the cluster is underutilized for three minutes.

  Minimize the time it takes to scale to the maximum number of workers.

  Minimize costs.

  What should you do first?

  A. Enable container services for workspace1.

  B. Upgrade workspace1 to the Premium pricing tier.

  C. Set Cluster Mode to High Concurrency.

  D. Create a cluster policy in workspace1.

  Correct Answer: B

  For clusters running Databricks Runtime 6.4 and above, optimized autoscaling is used by all-purpose clusters in the Premium plan Optimized autoscaling:

  Scales up from min to max in 2 steps.

  Can scale down even if the cluster is not idle by looking at shuffle file state.

  Scales down based on a percentage of current nodes.

  On job clusters, scales down if the cluster is underutilized over the last 40 seconds.

  On all-purpose clusters, scales down if the cluster is underutilized over the last 150 seconds.

  The spark.databricks.aggressiveWindowDownS Spark configuration property specifies in seconds how often a cluster makes down-scaling decisions. Increasing the value causes a cluster to scale down more slowly. The maximum value is

  600.

  Note: Standard autoscaling

  Starts with adding 8 nodes. Thereafter, scales up exponentially, but can take many steps to reach the max. You can customize the first step by setting the spark.databricks.autoscaling.standardFirstStepUp Spark configuration property.

  Scales down only when the cluster is completely idle and it has been underutilized for the last 10 minutes.

  Scales down exponentially, starting with 1 node.

  Reference:

  https://docs.databricks.com/clusters/configure.html

• Question 300:

  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 are designing an Azure Stream Analytics solution that will analyze Twitter data.

  You need to count the tweets in each 10-second window. The solution must ensure that each tweet is counted only once.

  Solution: You use a session window that uses a timeout size of 10 seconds.

  Does this meet the goal?

  A. Yes

  B. No

  Correct Answer: B

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

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