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Microsoft DP-203 Sample Questions
Question # 1
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 statedgoals. Some question sets might have more than one correct solution, while othersmight not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As aresult, 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 adata warehouse in Azure Synapse Analytics.Solution: You schedule an Azure Databricks job that executes an R notebook, and theninserts the data into the data warehouse.Does this meet the goal?
A. Yes
B. No
Explanation:Must use an Azure Data Factory, not an Azure Databricks job.Reference:https://docs.microsoft.com/en-US/azure/data-factory/transform-data
Question # 2
You plan to use an Apache Spark pool in Azure Synapse Analytics to load data to an AzureData Lake Storage Gen2 account.You need to recommend which file format to use to store the data in the Data Lake Storageaccount. The solution must meet the following requirements:• Column names and data types must be defined within the files loaded to the Data LakeStorage account.• Data must be accessible by using queries from an Azure Synapse Analytics serverlessSQL pool.• Partition elimination must be supported without having to specify a specific partition.What should you recommend?
A. Delta Lake
B. JSON
C. CSV
D. ORC
Question # 3
You are designing 2 solution that will use tables in Delta Lake on Azure Databricks.You need to minimize how long it takes to perform the following:*Queries against non-partitioned tables* Joins on non-partitioned columnsWhich two options should you include in the solution? Each correct answer presents part ofthe solution.(Choose Correct Answer and Give Explanation and References to Support the answersbased from Data Engineering on Microsoft Azure)
A. Z-Ordering
B. Apache Spark caching
C. dynamic file pruning (DFP)
D. the clone command
Explanation: According to the information I found on the web, two options that you should
include in the solution to minimize how long it takes to perform queries and joins on nonpartitionedtables are:Z-Ordering: This is a technique to colocate related information in the same set offiles. This co-locality is automatically used by Delta Lake in data-skippingalgorithms. This behavior dramatically reduces the amount of data that Delta Lakeon Azure Databricks needs to read123.Apache Spark caching: This is a feature that allows you to cache data in memoryor on disk for faster access. Caching can improve the performance of repeatedqueries and joins on the same data. You can cache Delta tables using the CACHETABLE or CACHE LAZY commands. To minimize the time it takes to perform queries against non-partitioned tables and joins onnon-partitioned columns in Delta Lake on Azure Databricks, the following options should beincluded in the solution:A. Z-Ordering: Z-Ordering improves query performance by co-locating data that share thesame column values in the same physical partitions. This reduces the need for shufflingdata across nodes during query execution. By using Z-Ordering, you can avoid full tablescans and reduce the amount of data processed.B. Apache Spark caching: Caching data in memory can improve query performance byreducing the amount of data read from disk. This helps to speed up subsequent queriesthat need to access the same data. When you cache a table, the data is read from the datasource and stored in memory. Subsequent queries can then read the data from memory,which is much faster than reading it from disk.References:Delta Lake on Databricks: https://docs.databricks.com/delta/index.htmlBest Practices for Delta Lake onDatabricks: https://databricks.com/blog/2020/05/14/best-practices-for-delta-lakeon-databricks.html
Question # 4
You have an Azure subscription that contains an Azure Blob Storage account namedstorage1 and an Azure Synapse Analytics dedicated SQL pool named Pool1.You need to store data in storage1. The data will be read by Pool1. The solution must meetthe following requirements:Enable Pool1 to skip columns and rows that are unnecessary in a query.Automatically create column statistics.Minimize the size of files.Which type of file should you use?
A. JSON
B. Parquet
C. Avro
D. CSV
Explanation:Automatic creation of statistics is turned on for Parquet files. For CSV files, you need tocreate statistics manually until automatic creation of CSV files statistics is supported.Reference:https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-tables-statisti
Question # 5
You have an Azure Databricks workspace that contains a Delta Lake dimension tablenamed Tablet. Table1 is a Type 2 slowly changing dimension (SCD) table. You need toapply updates from a source table to Table1. Which Apache Spark SQL operation shouldyou use?
A. CREATE
B. UPDATE
C. MERGE
D. ALTER
Explanation:
The Delta provides the ability to infer the schema for data input which further reduces theeffort required in managing the schema changes. The Slowly Changing Data(SCD) Type 2records all the changes made to each key in the dimensional table. These operationsrequire updating the existing rows to mark the previous values of the keys as old and theninserting new rows as the latest values. Also, Given a source table with the updates andthe target table with dimensional data, SCD Type 2 can be expressed with the merge.Example:// Implementing SCD Type 2 operation using merge functioncustomersTableas(“customers”)merge(stagedUpdates.as(“staged_updates”),”customers.customerId = mergeKey”)whenMatched(“customers.current = true AND customers.address <>staged_updates.address”)updateExpr(Map(“current” -> “false”,”endDate” -> “staged_updates.effectiveDate”))whenNotMatched() insertExpr(Map(“customerid” -> “staged_updates.customerId”,”address” -> “staged_updates.address”,”current” -> “true”,”effectiveDate” -> “staged_updates.effectiveDate”,”endDate” -> “null”))execute()}Reference:https://www.projectpro.io/recipes/what-is-slowly-changing-data-scd-type-2-operation-deltatable-databricks
Question # 6
You have an Azure Synapse Analytics dedicated SQL pool named Pool1. Pool1 contains atable named table1.You load 5 TB of data intotable1.You need to ensure that columnstore compression is maximized for table1.Which statement should you execute?
A. ALTER INDEX ALL on table1 REORGANIZE
B. ALTER INDEX ALL on table1 REBUILD
C. DBCC DBREINOEX (table1)
D. DBCC INDEXDEFRAG (pool1,tablel)
Explanation:Columnstore and columnstore archive compressionColumnstore tables and indexes are always stored with columnstore compression. You canfurther reduce the size of columnstore data by configuring an additional compression calledarchival compression. To perform archival compression, SQL Server runs the MicrosoftXPRESS compression algorithm on the data. Add or remove archival compression byusing the following data compression types:Use COLUMNSTORE_ARCHIVE data compression to compress columnstore data witharchival compression.Use COLUMNSTORE data compression to decompress archival compression. Theresulting data continue to be compressed with columnstore compression.To add archival compression, use ALTER TABLE (Transact-SQL) or ALTER INDEX(Transact-SQL) with the REBUILD option and DATA COMPRESSION =COLUMNSTORE_ARCHIVE. Reference: https://learn.microsoft.com/en-us/sql/relational-databases/datacompression/data-compression
Question # 7
You have two Azure Blob Storage accounts named account1 and account2?You plan to create an Azure Data Factory pipeline that will use scheduled intervals toreplicate newly created or modified blobs from account1 to account?You need to recommend a solution to implement the pipeline. The solution must meet thefollowing requirements:• Ensure that the pipeline only copies blobs that were created of modified since the mostrecent replication event.• Minimize the effort to create the pipeline. What should you recommend?
A. Create a pipeline that contains a flowlet.
B. Create a pipeline that contains a Data Flow activity.
C. Run the Copy Data tool and select Metadata-driven copy task.
D. Run the Copy Data tool and select Built-in copy task.
Question # 8
You have an Azure Data Factory pipeline named pipeline1 that is invoked by a tumblingwindow trigger named Trigger1. Trigger1 has a recurrence of 60 minutes.You need to ensure that pipeline1 will execute only if the previous execution completessuccessfully.How should you configure the self-dependency for Trigger1?
A. offset: “-00:01:00” size: “00:01:00”
B. offset: “01:00:00” size: “-01:00:00”
C. offset: “01:00:00” size: “01:00:00”
D. offset: “-01:00:00” size: “01:00:00”
Explanation:
Tumbling window self-dependency propertiesIn scenarios where the trigger shouldn’t proceed to the next window until the precedingwindow is successfully completed, build a self-dependency. A self-dependency triggerthat’s dependent on the success of earlier runs of itself within the preceding hour will havethe properties indicated in the following code.Example code:”name”: “DemoSelfDependency”,”properties”: {“runtimeState”: “Started”,”pipeline”: {“pipelineReference”: {“referenceName”: “Demo”,”type”: “PipelineReference”}},
“type”: “TumblingWindowTrigger”,”typeProperties”: {“frequency”: “Hour”,”interval”: 1,”startTime”: “2018-10-04T00:00:00Z”,”delay”: “00:01:00″,”maxConcurrency”: 50,”retryPolicy”: {“intervalInSeconds”: 30},”dependsOn”: [{“type”: “SelfDependencyTumblingWindowTriggerReference”,”size”: “01:00:00″,”offset”: “-01:00:00”}]}}}Reference: https://docs.microsoft.com/en-us/azure/data-factory/tumbling-window-triggerdependency
Question # 9
You are building a data flow in Azure Data Factory that upserts data into a table in anAzure Synapse Analytics dedicated SQL pool.You need to add a transformation to the data flow. The transformation must specify logicindicating when a row from the input data must be upserted into the sink.Which type of transformation should you add to the data flow?
A. join
B. select
C. surrogate key
D. alter row
Explanation:The alter row transformation allows you to specify insert, update, delete, and upsertpolicies on rows based on expressions. You can use the alter row transformation toperform upserts on a sink table by matching on a key column and setting the appropriaterow policy
Question # 10
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 adata warehouse in Azure Synapse Analytics.Solution: You use an Azure Data Factory schedule trigger to execute a pipeline thatexecutes an Azure Databricks notebook, and then inserts the data into the datawarehouse.Dow this meet the goal?
A. Yes
B. No
Explanation:If you need to transform data in a way that is not supported by Data Factory, you cancreate a custom activity, not an Azure Databricks notebook, with your own data processinglogic and use the activity in the pipeline. You can create a custom activity to run R scriptson your HDInsight cluster with R installed.Reference:https://docs.microsoft.com/en-US/azure/data-factory/transform-data
Question # 11
You are designing an Azure Data Lake Storage solution that will transform raw JSON filesfor use in an analytical workload.You need to recommend a format for the transformed files. The solution must meet thefollowing requirements:Contain information about the data types of each column in the files.Support querying a subset of columns in the files.Support read-heavy analytical workloads.Minimize the file size.What should you recommend?
A. JSON
B. CSV
C. Apache Avro
D. Apache Parquet
Explanation:Parquet, an open-source file format for Hadoop, stores nested data structures in a flatcolumnar format.Compared to a traditional approach where data is stored in a row-oriented approach, Parquet file format is more efficient in terms of storage and performance.It is especially good for queries that read particular columns from a “wide” (with manycolumns) table since only needed columns are read, and IO is minimized.Reference: https://www.clairvoyant.ai/blog/big-data-file-formats
Question # 12
You have an Azure subscription that contains an Azure Synapse Analytics workspacenamed ws1 and an Azure Cosmos D6 database account named Cosmos1 Costmos1contains a container named container 1 and ws1 contains a serverless1 SQL pool. you need to ensure that you can Query the data in container by using the serverless1 SQLpool.Which three actions should you perform? Each correct answer presents part of the solutionNOTE: Each correct selection is worth one point.
A. Enable Azure Synapse Link for Cosmos1
B. Disable the analytical store for container1.
C. In ws1. create a linked service that references Cosmos1
D. Enable the analytical store for container1
E. Disable indexing for container1
Question # 13
You are designing a folder structure for the files m an Azure Data Lake Storage Gen2account. The account has one container that contains three years of data.You need to recommend a folder structure that meets the following requirements:• Supports partition elimination for queries by Azure Synapse Analytics serverless SQLpooh • Supports fast data retrieval for data from the current month• Simplifies data security management by departmentWhich folder structure should you recommend?
A. \YYY\MM\DD\Department\DataSource\DataFile_YYYMMMDD.parquet
B. \Depdftment\DataSource\YYY\MM\DataFile_YYYYMMDD.parquet
C. \DD\MM\YYYY\Department\DataSource\DataFile_DDMMYY.parquet
D. \DataSource\Department\YYYYMM\DataFile_YYYYMMDD.parquet
Explanation:Department top level in the hierarchy to simplify security management.Month (MM) at the leaf/bottom level to support fast data retrieval for data from the currentmonth.
Question # 14
You have an Azure Synapse Analytics dedicated SQL pod. You need to create a pipeline that will execute a stored procedure in the dedicated SQLpool and use the returned result set as the input (or a downstream activity. The solutionmust minimize development effort.Which Type of activity should you use in the pipeline?
A. Notebook
B. U-SQL
C. Script
D. Stored Procedure
Question # 15
You have an Azure Synapse Analytics dedicated SQL pool that contains a table namedTable1. Table1 contains the following:One billion rowsA clustered columnstore index A hash-distributed column named Product KeyA column named Sales Date that is of the date data type and cannot be nullThirty million rows will be added to Table1 each month.You need to partition Table1 based on the Sales Date column. The solution must optimizequery performance and data loading.How often should you create a partition?
A. once per month
B. once per year
C. once per day
D. once per week
Explanation: Need a minimum 1 million rows per distribution. Each table is 60 distributions. 30 millionsrows is added each month. Need 2 months to get a minimum of 1 million rows perdistribution in a new partition.Note: When creating partitions on clustered columnstore tables, it is important to considerhow many rows belong to each partition. For optimal compression and performance ofclustered columnstore tables, a minimum of 1 million rows per distribution and partition isneeded. Before partitions are created, dedicated SQL pool already divides each table into60 distributions.Any partitioning added to a table is in addition to the distributions created behind thescenes. Using this example, if the sales fact table contained 36 monthly partitions, andgiven that a dedicated SQL pool has 60 distributions, then the sales fact table shouldcontain 60 million rows per month, or 2.1 billion rows when all months are populated. If atable contains fewer than the recommended minimum number of rows per partition,consider using fewer partitions in order to increase the number of rows per partition.Reference:https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-datawarehouse-tables-partition
Question # 16
You have an Azure Databricks workspace named workspace! in the Standard pricing tier.Workspace1 contains an all-purpose cluster named cluster). You need to reduce the time ittakes for cluster 1 to start and scale up. The solution must minimize costs. What shouldyou 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.
Explanation:You can use Databricks Pools to Speed up your Data Pipelines and Scale ClustersQuickly.Databricks Pools, a managed cache of virtual machine instances that enables clusters tostart and scale 4 times faster.Reference:https://databricks.com/blog/2019/11/11/databricks-pools-speed-up-data-pipelines.html
Question # 17
You have an Azure subscription that contains an Azure Data Lake Storage account named myaccount1. The myaccount1 account contains two containers named container1 and contained. The subscription is linked to an Azure Active Directory (Azure AD) tenant that contains a security group named Group1. You need to grant Group1 read access to contamer1. The solution must use the principle of least privilege. Which role should you assign to Group1?
A. Storage Blob Data Reader for container1
B. Storage Table Data Reader for container1
C. Storage Blob Data Reader for myaccount1
D. Storage Table Data Reader for myaccount1
Question # 18
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
Explanation: 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/alwaysencrypted-datab…
Question # 19
You have an Azure Synapse Analytics dedicated SQL pool. You need to Create a fact table named Table1 that will store sales data from the last three years. The solution must be optimized for the following query operations: Show order counts by week. • Calculate sales totals by region. • Calculate sales totals by product. • Find all the orders from a given month. Which data should you use to partition Table1?
A. region
B. product
C. week
D. month
Question # 20
You plan to create a dimension table in Azure Synapse Analytics that will be less than 1 GB. You need to create the table to meet the following requirements: • Provide the fastest Query time. • Minimize data movement during queries. Which type of table should you use?
A. hash distributed
B. heap
C. replicated
D. round-robin
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