DP-203 Practice Exam 3 — 20 Questions

Question 1

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?

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

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 2

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 3

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 4

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 5

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

Question 6

You are a data engineer at a retail company. You have designed a near real-time data processing solution using Azure Stream Analytics. The input is from Azure Event Hubs, which receives clickstream events from the company's e-commerce website. The output is written to an Azure SQL Database table for reporting. Each event includes fields: UserId, ProductId, EventType (e.g., 'click', 'purchase'), and Timestamp. The requirement is to calculate the number of purchases per product in a 5-minute tumbling window and update a SQL table. The Stream Analytics job has been running for a week, but the reporting team notices that the purchase counts in SQL are consistently lower than expected compared to a direct count from Event Hubs. You suspect that late-arriving events are being dropped. The job's configuration includes a 5-minute tumbling window with no late arrival policy. What should you do to fix the issue without losing data?

Accepted Answer

Modify the query to use a larger tumbling window (e.g., 10 minutes) and add a late arrival policy with a 5-minute grace period to allow late events to be included.. The current 5-minute tumbling window has no late arrival policy, so any event that arrives after the window ends is dropped. By increasing the window size to 10 minutes and adding a 5-minute late arrival grace period, you allow events that arrive up to 5 minutes late to still be included in the correct window, matching the actual purchase count from Event Hubs.

Answer

Modify the query to use TIMESTAMP BY on the EventHubs enqueued time instead of the event's Timestamp field.

Answer

Change the tumbling window to a hopping window with a 1-minute hop size to increase the frequency of output updates.

Answer

Add a second Stream Analytics job to process late-arriving events separately and union the results.

Question 7

Which THREE statements are true about partitioning in Azure Synapse Analytics dedicated SQL pool?

Accepted Answer

Partition switching can be used to quickly load data into a table.. Partition switching in Azure Synapse Analytics dedicated SQL pool allows you to quickly load data into a table by switching a partition from a staging table into the target table. This operation is metadata-only and does not require data movement, making it highly efficient for incremental data loads.

Answer

Partitions are automatically aligned with distributions.

Answer

Partitioning is only supported on tables with clustered rowstore indexes.

Question 8

You are using Azure Stream Analytics to process real-time temperature data from IoT devices. The output must be written to Azure SQL Database. The job has been running successfully for weeks, but recently you notice that the output data has duplicate rows. The input events are unique. The job uses a windowed aggregation (TumblingWindow). What is the most likely cause of duplicates?

Accepted Answer

The job is being restarted and reprocessing data.. When an Azure Stream Analytics job is restarted, it may reprocess data from the last checkpoint or from the beginning of the input stream, depending on the configured start time and output policy. This reprocessing can cause duplicate rows in the output, especially when using windowed aggregations like TumblingWindow, because the same events are aggregated again and written to Azure SQL Database without deduplication logic.

Answer

The job is not handling late-arriving events.

Answer

The input event hub is receiving duplicate events.

Answer

The tumbling window size is too small.

Question 9

You need to assign permissions to a service principal so that it can write data to a specific container in Azure Data Lake Storage Gen2, but not delete blobs. The above JSON shows the built-in role 'Storage Blob Data Contributor'. The role includes delete permission in DataActions. What should you do?

Accepted Answer

Create a custom role that includes read and write DataActions but excludes the delete DataAction, then assign that custom role.. Azure RBAC roles are all-or-nothing at the permission level; you cannot selectively remove a single DataAction from a built-in role at assignment time. The only way to grant write access without delete is to create a custom role that explicitly includes the required read and write DataActions (e.g., Microsoft.Storage/storageAccounts/blobServices/containers/blobs/write) and omits the delete DataAction (Microsoft.Storage/storageAccounts/blobServices/containers/blobs/delete). This custom role is then assigned to the service principal at the container scope, ensuring it can write data but never delete blobs.

Answer

Assign the Storage Blob Data Contributor role and create a deny assignment that denies delete.

Answer

Assign the Storage Blob Data Contributor role and use ACLs to deny delete on the container.

Answer

Assign the Storage Blob Data Contributor role and remove the delete permission at the role assignment scope.

Question 10

Which TWO actions can you take to optimize the performance of an Azure Synapse Analytics dedicated SQL pool? (Choose two.)

Accepted Answer

Replicate small dimension tables.. Replicating small dimension tables across all distributions reduces data movement during joins and improves query performance. Option E is correct because hash distribution on a column used in joins and aggregations ensures that rows with the same key are colocated on the same distribution, minimizing data shuffling. Option A is incorrect: scaling up increases resources but is not a targeted performance optimization and may not address underlying distribution or indexing issues. Option C is incorrect: heap indexes are generally not optimal for fact tables because they lack compression and indexing benefits; clustered columnstore indexes are recommended. Option D is incorrect: round-robin distribution distributes data evenly but does not reduce data movement for joins and aggregations, often leading to poor query performance.

Answer

Scale up the SQL pool to a higher DWU.

Answer

Use heap indexes for fact tables.

Answer

Use round-robin distribution for all large fact tables.

Question 11

Your organization is implementing a data lake using Azure Data Lake Storage Gen2. You have a folder structure like '/data/landing/' for raw data and '/data/curated/' for cleaned data. The data is ingested daily from various sources. You need to ensure that data in the curated zone is immutable and cannot be modified or deleted by anyone, including administrators, for compliance reasons. However, data in the landing zone should be modifiable. What should you do?

Accepted Answer

Enable immutable storage with a time-based retention policy on the curated zone container. Azure Data Lake Storage Gen2 supports immutable storage at the container level, which enforces a time-based retention policy that prevents any data from being modified or deleted—even by administrators—until the retention period expires. This directly meets the compliance requirement for the curated zone, while leaving the landing zone container unaffected and modifiable.

Answer

Remove the 'Delete' permission from the storage account key

Answer

Set ACLs on the curated zone folder to deny write and delete for all users

Answer

Use Azure RBAC to deny delete and write permissions for all users on the curated zone folder

Question 12

A company is designing a data storage solution for IoT device telemetry. Each device sends a JSON payload every second. The data must be stored in a way that supports real-time dashboards and long-term analytics with low latency. Which Azure data store should be used for the ingestion layer?

Accepted Answer

Azure Event Hubs. Azure Event Hubs is the correct choice for the ingestion layer because it is a fully managed, real-time data streaming platform designed to ingest millions of events per second with low latency. It supports the capture of JSON telemetry from IoT devices and integrates directly with downstream analytics services like Azure Stream Analytics for real-time dashboards and long-term storage in Azure Data Lake or Blob Storage. Its partitioned throughput model ensures scalable, durable ingestion without blocking producers.

Answer

Azure SQL Database

Answer

Azure Blob Storage

Answer

Azure Data Lake Storage

Question 13

Match each Azure data integration tool to its typical use case.

Accepted Answer

Azure Data Factory: Orchestrate and automate data movement and transformation at scale.. In this matching exercise, the correct pairs are: Azure Data Factory for orchestration, Azure Synapse Analytics for data warehousing, Azure Databricks for data engineering/ML, and Azure Stream Analytics for real-time streaming. Common confusions involve swapping orchestration and streaming roles.

Answer

Azure Data Factory: Process real-time streaming data from IoT devices.

Answer

Azure Synapse Analytics: Orchestrate data movement at scale.

Question 14

Which TWO techniques should you use to secure sensitive data in Azure Synapse Analytics dedicated SQL pools when implementing column-level security?

Accepted Answer

Define dynamic data masking rules to obfuscate sensitive columns for unauthorized users.. Dynamic data masking obfuscates sensitive columns for unauthorized users at query time, providing column-level security without altering the underlying data. Option D is correct because column-level security in Azure Synapse dedicated SQL pools uses GRANT SELECT on specific columns to authorized users, restricting access to sensitive columns. Option B is incorrect because Transparent Data Encryption (TDE) encrypts data at rest, not at the column level. Option C is incorrect because security policies with filter predicates are used for row-level security, not column-level security. Option E is incorrect because Always Encrypted encrypts columns at the application level, which is separate from column-level security in Synapse.

Answer

Enable Transparent Data Encryption (TDE) on the database.

Answer

Create a security policy with filter predicates to restrict access to specific columns.

Answer

Implement Always Encrypted to encrypt columns at the application level.

Question 15

Which THREE metrics from Azure Monitor should be used to diagnose performance bottlenecks in an Azure Data Factory pipeline?

Accepted Answer

Pipeline Succeeded Rerun Count. Pipeline Succeeded Rerun Count (A) is correct because a high number of reruns indicates that the pipeline is repeatedly failing and retrying, which directly points to a performance bottleneck such as resource contention or throttling. This metric helps identify pipelines that are not completing successfully on the first attempt, signaling underlying issues that degrade throughput.

Answer

Blob Capacity

Answer

SQL Pool DWU Used

Question 16

You are designing a data pipeline that uses Azure Data Factory to load data from an FTP server to Azure Data Lake Storage. The FTP server requires authentication with username and password. Which type of linked service should you create?

Accepted Answer

FTP. Azure Data Factory provides a native FTP connector that supports username/password authentication for connecting to FTP servers. This linked service type is specifically designed to handle the FTP protocol (RFC 959) and allows you to copy data directly from an FTP server to Azure Data Lake Storage without requiring any additional gateways or custom activities.

Answer

Azure Blob Storage

Answer

HTTP

Answer

Rest service

Question 17

Refer to the exhibit. A data engineer runs a pipeline copy activity and gets this error. What is the most likely cause of the failure?

Accepted Answer

The self-hosted integration runtime is not running or unreachable.. The error indicates the self-hosted integration runtime is not ready. This typically happens when the IR is not running or has network issues.

Answer

The destination storage account is inaccessible.

Answer

The pipeline parameters are missing.

Answer

The source database is offline.

Question 18

You are designing a streaming data solution for IoT devices that generate 10,000 events per second. The data must be processed with sub-second latency and then stored in Azure Data Lake Storage Gen2 for archival. Which Azure service should you use for the stream processing?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is the correct choice because it is purpose-built for real-time stream processing with sub-second latency, and it natively integrates with Azure Data Lake Storage Gen2 for output. It can handle 10,000 events per second using its streaming unit scaling, and its SQL-like query language allows for low-latency transformations without the overhead of cluster management.

Answer

Azure HDInsight including Spark Structured Streaming

Answer

Azure Data Factory

Answer

Azure Event Hubs

Question 19

A data engineer needs to process a large dataset stored in Azure Blob Storage using Azure Databricks. The dataset consists of millions of small CSV files. The processing job is slow due to the overhead of reading many small files. Which technique should be used to improve performance?

Accepted Answer

Coalesce the small files into larger files using a Databricks notebook. Coalescing the millions of small CSV files into larger files reduces the metadata overhead and I/O operations when reading from Azure Blob Storage. Databricks can then process fewer, larger files more efficiently, as each task handles a substantial data chunk rather than incurring the cost of opening and closing many small files.

Answer

Increase the number of worker nodes in the cluster

Answer

Convert the CSV files to Parquet format

Answer

Use Delta Lake caching to store the data in memory

Question 20

You need to transform data in Azure Synapse Analytics using a language that supports procedural logic and error handling. Which option should you use?

Accepted Answer

T-SQL stored procedures. T-SQL stored procedures are the correct choice because they support procedural logic (e.g., IF/ELSE, loops, TRY/CATCH) and error handling within Azure Synapse Analytics dedicated SQL pools. This allows you to encapsulate complex data transformation logic, handle runtime errors gracefully, and manage transactions, which is not possible with declarative objects like views or external tables.

Answer

CREATE VIEW

Answer

PolyBase

Answer

CREATE EXTERNAL TABLE