Microsoft Microsoft Certifications DP-203 Questions & Answers

• Question 181:

  You are designing database for an Azure Synapse Analytics dedicated SQL pool to support workloads for detecting ecommerce transaction fraud.

  Data will be combined from multiple ecommerce sites and can include sensitive financial information such as credit card numbers.

  You need to recommend a solution that meets the following requirements:

  Users must be able to identify potentially fraudulent transactions. Users must be able to use credit cards as a potential feature in models. Users must NOT be able to access the actual credit card numbers.

  What should you include in the recommendation?

  A. Transparent Data Encryption (TDE)

  B. row-level security (RLS)

  C. column-level encryption

  D. Azure Active Directory (Azure AD) pass-through authentication

  Correct Answer: C

  Use Always Encrypted to secure the required columns. You can configure Always Encrypted for individual database columns containing your sensitive data. Always Encrypted is a feature designed to protect sensitive data, such as credit card numbers or national identification numbers (for example, U.S. social security numbers), stored in Azure SQL Database or SQL Server databases.

  Reference: https://docs.microsoft.com/en-us/sql/relational-databases/security/encryption/always-encrypted-database-engine

• Question 182:

  You have two fact tables named Flight and Weather. Queries targeting the tables will be based on the join between the following columns.

  

  You need to recommend a solution that maximum query performance. What should you include in the recommendation?

  A. In each table, create a column as a composite of the other two columns in the table.

  B. In each table, create an IDENTITY column.

  C. In the tables, use a hash distribution of ArriveDateTime and ReportDateTime.

  D. In the tables, use a hash distribution of ArriveAirPortID and AirportID.

  Correct Answer: D

• Question 183:

  You are implementing a batch dataset in the Parquet format.

  Data tiles will be produced by using Azure Data Factory and stored in Azure Data Lake Storage Gen2. The files will be consumed by an Azure Synapse Analytics serverless SQL pool.

  You need to minimize storage costs for the solution.

  What should you do?

  A. Store all the data as strings in the Parquet tiles.

  B. Use OPENROWEST to query the Parquet files.

  C. Create an external table mat contains a subset of columns from the Parquet files.

  D. Use Snappy compression for the files.

  Correct Answer: C

  An external table points to data located in Hadoop, Azure Storage blob, or Azure Data Lake Storage. External tables are used to read data from files or write data to files in Azure Storage. With Synapse SQL, you can use external tables to read external data using dedicated SQL pool or serverless SQL pool.

  Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-tables-external- tables

• Question 184:

  You are designing an anomaly detection solution for streaming data from an Azure IoT hub. The solution must meet the following requirements:

  1.

  Send the output to Azure Synapse.

  2.

  Identify spikes and dips in time series data.

  3.

  Minimize development and configuration effort. Which should you include in the solution?

  A. Azure Databricks

  B. Azure Stream Analytics

  C. Azure SQL Database

  Correct Answer: B

  You can identify anomalies by routing data via IoT Hub to a built-in ML model in Azure Stream Analytics.

  Reference: https://docs.microsoft.com/en-us/learn/modules/data-anomaly-detection-using-azure-iot-hub/

• Question 185:

  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 have an Azure Data Lake Storage account that contains a staging zone.

  You need to design a daily process to ingest incremental data from the staging zone, transform the data by executing an R script, and then insert the transformed data into a data warehouse in Azure Synapse Analytics.

  Solution: You schedule an Azure Databricks job that executes an R notebook, and then inserts the data into the data warehouse.

  Does this meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  Must use an Azure Data Factory, not an Azure Databricks job.

  Reference: https://docs.microsoft.com/en-US/azure/data-factory/transform-data

• Question 186:

  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:

  A workload for data engineers who will use Python and SQL. A workload for jobs that will run notebooks that use Python, Scala, and SOL. 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:

  The data engineers must share a cluster.

  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.

  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 Standard cluster for the data engineers, and a High Concurrency 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 187:

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

  You have files that are ingested and loaded into an Azure Data Lake Storage Gen2 container named container1.

  You plan to insert data from the files in container1 into Table1 and transform the data. Each row of data in the files will produce one row in the serving layer of Table1.

  You need to ensure that when the source data files are loaded to container1, the DateTime is stored as an additional column in Table1.

  Solution: You use an Azure Synapse Analytics serverless SQL pool to create an external table that has an additional DateTime column.

  Does this meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  Instead use the derived column transformation to generate new columns in your data flow or to modify existing fields.

  Reference: https://docs.microsoft.com/en-us/azure/data-factory/data-flow-derived-column

• Question 188:

  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 have an Azure Data Lake Storage account that contains a staging zone.

  You need to design a daily process to ingest incremental data from the staging zone, transform the data by executing an R script, and then insert the transformed data into a data warehouse in Azure Synapse Analytics.

  Solution: You use an Azure Data Factory schedule trigger to execute a pipeline that executes mapping data flow, and then inserts the data into the data warehouse.

  Does this meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  If you need to transform data in a way that is not supported by Data Factory, you can create a custom activity, not a mapping flow,5 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.

  Reference: https://docs.microsoft.com/en-US/azure/data-factory/transform-data

• Question 189:

  You use Azure Stream Analytics to receive Twitter data from Azure Event Hubs and to output the data to an Azure Blob storage account.

  You need to output the count of tweets from the last five minutes every minute.

  Which windowing function should you use?

  A. Sliding

  B. Session

  C. Tumbling

  D. Hopping

  Correct Answer: D

  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.

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

• Question 190:

  You have an Azure Synapse Analytics dedicated SQL pool mat contains a table named dbo.Users.

  You need to prevent a group of users from reading user email addresses from dbo.Users. What should you use?

  A. row-level security

  B. column-level security

  C. Dynamic data masking

  D. Transparent Data Encryption (TDD

  Correct Answer: B

  Column-Level security allows customers to control access to table columns based on the user's execution context or group membership.

  Note: Column-level security simplifies the design and coding of security in your application, allowing you to restrict column access to protect sensitive data. For example, ensuring that specific users can access only certain columns of a table pertinent to their department. The access restriction logic is located in the database tier rather than away from the data in another application tier. The database applies the access restrictions every time data access is attempted from any tier. This restriction makes your security more reliable and robust by reducing the surface area of your overall security system. In addition, column-level security also eliminates the need for introducing views to filter out columns for imposing access restrictions on the users.

  Reference: https://learn.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/column-level-security