DP-203 Practice Test 2 — 25 Questions

Question 1

You are building a real-time processing solution using Azure Stream Analytics. The solution must handle out-of-order events and late arrivals. Which THREE mechanisms should you configure in the Stream Analytics job?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Set an 'Out-of-order tolerance' window in the event ordering settings.. Azure Stream Analytics allows you to configure an 'Out-of-order tolerance' window in the event ordering settings. This window defines the maximum time difference that out-of-order events can be reordered before being considered late. By setting this tolerance, you ensure that events arriving slightly out of sequence are still processed correctly, which is critical for real-time analytics where event order matters.

Answer

Adjust the 'Streaming units' to handle higher throughput.

Answer

Enable 'Event hub capture' to store raw events for reprocessing.

Question 2

Which TWO are benefits of using Azure Databricks Auto Loader for incremental data ingestion?

Accepted Answer

It can process new files as they arrive in cloud storage.. Azure Databricks Auto Loader is designed to incrementally ingest new data files as they arrive in cloud storage (e.g., Azure Data Lake Storage Gen2 or Blob Storage) by using a notification-based or directory-listing approach. It automatically tracks which files have already been processed using a structured streaming checkpoint mechanism, eliminating the need for manual checkpoint management even at high data volumes. This makes options A and B correct because Auto Loader's core value is its ability to discover and process new files without manual intervention and to handle large-scale ingestion reliably.

Answer

It automatically evolves the schema without any configuration.

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It provides sub-second latency for real-time streaming.

Answer

It provides built-in deduplication of records.

Question 3

You are using Azure Synapse Analytics dedicated SQL pool to run a query that joins a large fact table (10 billion rows) and a small dimension table (1 million rows). The query is slow. Which distribution strategy should you use for the dimension table to improve performance?

Accepted Answer

Replicate the dimension table to all compute nodes.. Replicating the small dimension table (1 million rows) to all compute nodes eliminates data movement during the join with the large fact table (10 billion rows). In Azure Synapse dedicated SQL pool, replicated tables store a full copy on each distribution, so the join can be performed locally on every node without shuffling data across the network, drastically reducing query latency.

Answer

Round-robin distribute the dimension table.

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Hash-distribute the dimension table on its primary key.

Answer

Hash-distribute the dimension table on the foreign key column.

Question 4

You are designing a data processing solution for a financial services company. The solution must process sensitive customer data in Azure Databricks with encryption at rest and in transit, and must also support row-level security (RLS). Which combination of features should you use?

Accepted Answer

Use Azure Databricks with Delta Lake and enable row-level security on the Delta table. Delta Lake in Azure Databricks now supports row-level security (RLS) natively through Delta Sharing and Spark SQL predicates, allowing you to enforce data access policies directly on the Delta table without external systems. Combined with Azure Databricks' built-in encryption at rest (using Azure Storage Service Encryption or customer-managed keys) and in transit (TLS 1.2+), this meets all requirements without introducing additional services.

Answer

Use Azure SQL Database with row-level security and connect Databricks via JDBC

Answer

Use Azure Databricks with Azure Key Vault for encryption and implement RLS in application code

Answer

Use Azure Synapse Analytics dedicated SQL pool with dynamic data masking and always encrypted

Question 5

You are configuring a data pipeline in Azure Data Factory that uses a Mapping Data Flow. The source is a SQL Server table with 50 million rows. The sink is a Delta table in ADLS Gen2. The pipeline runs slowly. You need to improve performance by reducing the number of partitions in the data flow. Which setting should you adjust?

Accepted Answer

In the sink transformation, set the 'Optimize' tab to 'Single partition'.. Setting the sink transformation's 'Optimize' tab to 'Single partition' forces all data to be written into a single partition, which reduces the number of output files and the associated metadata overhead. This can improve performance when the downstream processing or storage benefits from fewer, larger files, especially for small-to-medium datasets or when the sink is a Delta table that must be compacted later.

Answer

In the source transformation, set the 'Optimize' tab to 'Use current partitioning'.

Answer

Decrease the cluster size to 4 cores.

Answer

In the pipeline activity, set the 'Data flow' property 'partitionOption' to 'Single partition'.

Question 6

You are designing a solution to store telemetry data from millions of devices. Each device sends a JSON payload every 5 seconds. The data must be partitioned by device ID and time for efficient querying and must support real-time streaming ingestion. Which Azure storage solution should you recommend?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is the correct choice because it offers a multi-model, globally distributed database service with native support for real-time streaming ingestion via the Change Feed and automatic indexing. Its partition key design (device ID + time) enables efficient querying across millions of devices, and it guarantees single-digit millisecond read/write latencies essential for telemetry data arriving every 5 seconds.

Answer

Azure SQL Database

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Azure Blob Storage with Azure Event Hubs

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Azure Table Storage

Question 7

You are optimizing a data pipeline in Azure Synapse Analytics that loads data from a CSV file in ADLS Gen2 into a dedicated SQL pool using PolyBase. The load is slow and you need to improve performance. Which action would be MOST effective?

Accepted Answer

Combine the CSV files into fewer, larger files before loading.. Combining many small CSV files into fewer, larger files reduces the number of file open/close operations and minimizes the overhead of PolyBase's external file enumeration and parallel split logic. PolyBase performs best when each file is at least 256 MB, as it can then assign a full file to each distribution, avoiding the overhead of splitting tiny files across multiple threads.

Answer

Increase the service level (DWU) of the dedicated SQL pool.

Answer

Change the file format from CSV to Avro.

Answer

Use Azure Data Factory to stage the data in Azure Blob Storage before loading.

Question 8

Your organization is using Azure Synapse Analytics dedicated SQL pool. You notice that queries are running slower than expected. Upon reviewing the execution plans, you see that some queries are performing table scans instead of seeks on large fact tables. What is the most likely cause?

Accepted Answer

The statistics on the tables are outdated or missing.. Outdated or missing statistics prevent the Azure Synapse Analytics dedicated SQL pool query optimizer from accurately estimating row counts and data distribution. Without reliable statistics, the optimizer may incorrectly choose a table scan over a more efficient index seek or partition elimination, leading to slower query performance on large fact tables.

Answer

The tables are distributed using round-robin distribution.

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Result-set caching is disabled.

Answer

The resource class for the user is set to smallrc.

Question 9

You are building a real-time dashboard to monitor user activity on a website. The data is ingested via Azure Event Hubs and must be aggregated every minute with a 30-second late-arrival tolerance. The aggregated results should be stored in Azure Cosmos DB for low-latency reads. Which Azure service should you use to perform the windowed aggregation?

Accepted Answer

Azure Stream Analytics with a tumbling window of 1 minute and a late-arrival policy of 30 seconds.. Azure Stream Analytics is the correct choice because it natively supports windowed aggregations (tumbling, hopping, sliding, session) and allows you to define a late-arrival policy to handle out-of-order events. A tumbling window of 1 minute with a late-arrival tolerance of 30 seconds meets the requirement exactly, and the output can be directly written to Azure Cosmos DB for low-latency reads.

Answer

Azure Functions triggered by Event Hubs to aggregate data and write to Cosmos DB.

Answer

Azure Databricks with structured streaming and a sliding window.

Answer

Azure Analysis Services to process streaming data directly from Event Hubs.

Question 10

You are designing a data processing solution that uses Azure Databricks to transform large datasets. You need to ensure that the processing is cost-effective and can scale to handle variable workloads. Which cluster configuration should you recommend?

Accepted Answer

Use an auto-scaling cluster with spot instances.. Auto-scaling clusters in Azure Databricks dynamically adjust the number of workers based on workload demands, ensuring cost-effectiveness by scaling down during low activity. Spot instances (Azure Spot VMs) further reduce costs by using unused Azure capacity at a significant discount, making this combination ideal for variable workloads where fault tolerance is acceptable.

Answer

Use a fixed-size cluster with premium tier.

Answer

Use a Photon-accelerated cluster with premium tier.

Answer

Use an interactive cluster with a large number of workers.

Question 11

You are optimizing a data pipeline in Azure Data Factory that uses a Copy activity to transfer data from an Azure SQL Database to a dedicated SQL pool in Azure Synapse Analytics. The source table has 500 million rows and the copy operation is taking too long. You need to reduce the copy duration. Which configuration change will have the most impact?

Accepted Answer

Enable staging and use PolyBase as the copy method for the sink.. Enabling staging with PolyBase as the copy method for the sink is the most impactful change because PolyBase leverages the massively parallel processing (MPP) architecture of Azure Synapse Analytics to load data in parallel directly into the dedicated SQL pool. This bypasses the single-threaded bottleneck of the standard INSERT-based copy method, dramatically reducing the time required to ingest 500 million rows.

Answer

Change the copy behavior to 'sequential' to reduce load on the source.

Answer

Increase the degree of copy parallelism (DOP) to the maximum value supported.

Answer

Split the source data into multiple smaller files and use multiple copy activities running in parallel.

Question 12

Refer to the exhibit. You are monitoring the CopyDataPipeline in Azure Data Factory. The copy activity is failing with timeout errors. What is the most likely cause?

Accepted Answer

The writeBatchTimeout is set too low (30 seconds), causing timeouts. The copy activity in Azure Data Factory is failing with timeout errors because the `writeBatchTimeout` is set to 30 seconds, which is too low for the volume of data being written to the sink. This property defines the maximum time allowed for a single batch write operation to complete; when it expires, the activity times out. Increasing this value (e.g., to 120 seconds or more) accommodates larger or slower writes, resolving the timeout.

Answer

The enableStaging is false, causing network congestion

Answer

The writeBatchSize is too large (10000), exceeding SQL limits

Answer

The recursive property is set to true, causing infinite loops

Question 13

You are building a data pipeline in Azure Data Factory to copy data from an on-premises SQL Server database to Azure Blob Storage. The pipeline must run daily and handle incremental updates. The on-premises SQL Server table has a LastModifiedDate column that is updated when a row changes. What is the most efficient way to implement incremental loads?

Accepted Answer

Use a Lookup activity to get the maximum LastModifiedDate from the destination, then use a Copy activity with a query that filters rows where LastModifiedDate > that value.. It uses a Lookup activity to retrieve the maximum LastModifiedDate from the destination (Azure Blob Storage), then passes that value as a parameter to a Copy activity that queries only rows where LastModifiedDate exceeds it. This minimizes data transfer by reading only new or changed rows, and it avoids the overhead of enabling Change Data Capture or performing full loads, making it the most efficient approach for incremental loads in Azure Data Factory.

Answer

Enable Change Data Capture (CDC) on the SQL Server database and use an ADF mapping data flow to read changes.

Answer

Use a tumbling window trigger with a window size of 1 day and copy all data from the source each time.

Answer

Perform a full load every day and use a Delete activity to remove duplicates.

Question 14

Which TWO Azure services can be used to perform data transformation in a data pipeline? (Select two.)

Accepted Answer

Azure Data Factory. Azure Data Factory is a cloud-based ETL service that provides a code-free visual interface for orchestrating data movement and transformation at scale. It supports data flows, which allow you to perform transformations like aggregations, joins, and filtering without writing code, making it a correct choice for data transformation in a pipeline.

Answer

Azure Monitor

Answer

Azure Storage

Answer

Azure Event Hubs

Question 15

You are designing a data processing pipeline in Azure Data Factory that ingests data from an on-premises SQL Server database to Azure Data Lake Storage Gen2. The data volume is large (500 GB). The network connection between on-premises and Azure is limited to 100 Mbps. You need to minimize the time to transfer the initial full load while ensuring data integrity. Which approach should you recommend?

Accepted Answer

Use Azure Data Box to copy the data offline. Azure Data Box physically ships the data, bypassing network bandwidth limitations for large initial loads. Option A is wrong because it would take over 11 hours even at full bandwidth, and network may not be stable. Option B is wrong because it compresses but still uses network. Option C is wrong because VPN adds overhead.

Answer

Use Azure Data Factory copy activity with parallel connections

Answer

Use Azure ExpressRoute to increase bandwidth

Answer

Compress the data using GZip and use copy activity

Question 16

Refer to the exhibit. You are configuring an Azure Purview data policy for Azure Storage. The policy above is intended to audit all access events. However, the security team complains that not all read events are being audited. What is the most likely reason?

Accepted Answer

The storage account is not enabled for Purview policy enforcement.. The security team reports that not all read events are being audited. The policy uses 'ALL_ACTIONS' which should include read events, so the issue is not with the action group or predicate. The most likely reason is that the storage account has not been enabled for Purview policy enforcement. Azure Purview requires the 'AllowPurviewPolicyEnforcement' property to be enabled on the storage account for policies to take effect. Without this, no auditing events are captured, even if the policy is correctly defined.

Answer

The filter predicate is set to 'true', which only captures a subset of events.

Answer

The action group 'ALL_ACTIONS' does not include read events.

Answer

The policy excludes the 'Read' action by default.

Question 17

Your organization uses Microsoft Purview to catalog data assets. You need to ensure that sensitive data such as credit card numbers are automatically detected and labeled. Which Purview feature should you configure?

Accepted Answer

Configure a scan rule set with built-in classification rules for sensitive data types.. Microsoft Purview can automatically detect sensitive data like credit card numbers by configuring a scan rule set that includes built-in classification rules. Option A is incorrect because Azure Policy enforces compliance rules but does not perform data scanning or classification. Option C is incorrect because Data Catalog self-service search is for discovering and searching data assets, not for automatic sensitive data detection. Option D is incorrect because Microsoft Information Protection is primarily for labeling and protection in Microsoft 365, not for scanning data in Purview.

Answer

Create an Azure Policy to enforce tagging.

Answer

Enable the Data Catalog self-service search.

Answer

Enable Microsoft Information Protection for the data sources.

Question 18

You are designing a change data capture (CDC) solution to incrementally load data from an on-premises SQL Server database to Azure Synapse Analytics. The source tables have no timestamp columns and you cannot modify the schema. Which Azure service should you use?

Accepted Answer

Azure Data Factory with change tracking. Azure Data Factory's change tracking capability is the correct choice because it can capture row-level inserts, updates, and deletes from SQL Server without requiring timestamp columns or schema modifications. It uses SQL Server's built-in change tracking feature, which tracks changes at the table level and provides a reliable incremental load mechanism to Azure Synapse Analytics.

Answer

Azure Synapse Pipelines with mapping data flows

Answer

Azure Databricks with Auto Loader

Answer

Azure Stream Analytics

Question 19

You are designing a data processing solution in Azure Synapse Analytics. The solution must use a serverless SQL pool to query data in Azure Data Lake Storage Gen2. The data is stored in Delta Lake format. Which of the following statements is true regarding querying Delta Lake tables with serverless SQL pool?

Accepted Answer

You can use the OPENROWSET function with the BULK option and FORMAT='DELTA' to query Delta Lake tables.. Serverless SQL pool in Azure Synapse Analytics supports querying Delta Lake tables directly using the OPENROWSET function with the BULK option and FORMAT='DELTA'. This allows you to read Delta Lake data stored in Azure Data Lake Storage Gen2 without needing to convert it to Parquet or create external tables first. The Delta format is natively supported, enabling time travel and schema evolution features.

Answer

Serverless SQL pool supports Delta Lake only if the files are in Parquet format.

Answer

Serverless SQL pool cannot query Delta Lake tables; you must convert them to Parquet first.

Answer

You must create an external table in serverless SQL pool using the CREATE EXTERNAL TABLE statement with the Delta format.

Question 20

Refer to the exhibit. A Bicep file is used to deploy an Azure Synapse Analytics workspace. What is the purpose of the 'purviewConfiguration' property?

Accepted Answer

It links the workspace to a Microsoft Purview account for data lineage and cataloging. The 'purviewConfiguration' property in a Bicep file for Azure Synapse Analytics links the workspace to a Microsoft Purview account. This integration enables automated data lineage tracking, cataloging, and discovery across the Synapse environment, allowing users to search for and govern data assets directly from Purview. Without this property, the Synapse workspace operates independently of Purview's unified data governance capabilities.

Answer

It configures automated backups of the Synapse workspace

Answer

It enables monitoring of data movement by Azure Monitor

Answer

It connects the workspace to a data catalog for pipeline sources