DP-203 Practice Test 25 — 15 Questions

Question 1

Your organization uses Azure Synapse Analytics serverless SQL pools to query data in Azure Data Lake Storage Gen2. You need to ensure that only users with specific Microsoft Entra ID roles can query the data. What should you configure?

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

Accepted Answer

Assign the Storage Blob Data Contributor role to the users on the Azure Data Lake Storage Gen2 account.. Azure Synapse Analytics serverless SQL pools rely on Microsoft Entra ID tokens and RBAC roles to authorize access to data in Azure Data Lake Storage Gen2. The Storage Blob Data Contributor role grants read and write access to data. Option B is wrong because SAS tokens are shared secrets, not tied to user identity. Option C is wrong because firewall rules control network access, not user-level authorization. Option D is wrong because managed identity is not suitable for per-user authorization.

Answer

Generate a shared access signature (SAS) token for the storage account and include it in the external table definition.

Answer

Configure an IP firewall rule on the storage account to allow only the SQL pool's outbound IP addresses.

Answer

Create a managed identity for the SQL pool and grant it access to the storage account.

Question 2

Which THREE metrics should you monitor to evaluate the performance of an Azure Stream Analytics job?

Accepted Answer

Input Events Backlogged. Input Events Backlogged (A) is a correct metric because it measures the number of input events that are waiting to be processed, directly revealing whether the job is falling behind its input rate. Output Events (B) is correct because it counts the events emitted to the output sink, letting you verify the job is actually producing results and detect drops in throughput. Watermark Delay (seconds) (E) is correct because it quantifies how far behind real time the job is running, which is the key indicator of streaming latency and timeliness. Conversion Errors (C) is not one of the three performance metrics for evaluating job throughput/latency; it reflects data-format deserialization problems rather than performance. SU (Memory) Utilization (D) is a Streaming Unit resource metric that indicates capacity consumption, not the job's runtime performance as measured by backlog, output, and watermark delay.

Answer

Conversion Errors

Answer

SU (Memory) Utilization

Question 3

You are designing a security strategy for Azure Synapse Analytics. The solution must prevent users from accessing sensitive columns in a dedicated SQL pool, such as Social Security numbers, unless they have explicit permission. Which feature should you use?

Accepted Answer

Column-level security.. Column-level security (CLS) restricts access to specific columns by granting or denying SELECT permissions on individual columns. Option C (Dynamic data masking) obfuscates data at query time but does not prevent access, as users can still see the data if they bypass masking. Option B (Azure Purview) is a data governance service for cataloging and classifying data, not for access control. Option D (Row-level security) filters rows based on user context, not columns.

Answer

Azure Purview data classification.

Answer

Dynamic data masking.

Answer

Row-level security (RLS).

Question 4

You are reviewing a script to create an external data source in Azure Synapse Analytics serverless SQL pool. Based on the exhibit, what is the purpose of the SAS token?

Accepted Answer

To provide read access to the container for querying data.. In Azure Synapse Analytics serverless SQL pool, an external data source pointing to Azure Storage requires a SAS token to provide read access to the container so that queries can read the data. The SAS token grants limited access rights without exposing the account key, and it is used in the CREATE EXTERNAL DATA SOURCE statement to authenticate and authorize access to the storage.

Answer

To provide write access to the container for storing query results.

Answer

To encrypt the connection between the serverless pool and storage.

Answer

To authenticate the user to the serverless SQL pool.

Question 5

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 6

Which Azure storage solution is best suited for storing large volumes of unstructured data, such as log files and media files, and supports both hierarchical namespace and POSIX-like access control lists?

Accepted Answer

Azure Data Lake Storage Gen2. Azure Data Lake Storage Gen2 (ADLS Gen2) combines a hierarchical namespace with POSIX-like access control lists (ACLs) on top of Azure Blob Storage. This makes it ideal for storing large volumes of unstructured data (e.g., log files, media files) while supporting fine-grained, POSIX-compliant permissions and directory-level operations that are essential for big data analytics workloads.

Answer

Azure Blob Storage

Answer

Azure Files

Answer

Azure SQL Database

Question 7

You are designing a data processing solution in Azure Synapse Analytics. The solution must ensure that data at rest in a dedicated SQL pool is encrypted using customer-managed keys (CMK) stored in Azure Key Vault. The encryption should be enabled at the database level. What should you configure?

Accepted Answer

Transparent Data Encryption (TDE) with a customer-managed key in Azure Key Vault.. Transparent Data Encryption (TDE) with a customer-managed key in Azure Key Vault provides database-level encryption for dedicated SQL pools in Azure Synapse Analytics. TDE encrypts data at rest, and using a customer-managed key (CMK) allows the organization to control the key lifecycle. Option B is incorrect because Azure Purview is for data governance and classification, not encryption. Option C is incorrect because Azure Disk Encryption encrypts VM disks, not the SQL pool database. Option D is incorrect because Always Encrypted is a column-level encryption feature, not for full database encryption.

Answer

Azure Purview data classification and encryption policies.

Answer

Azure Disk Encryption on the nodes hosting the dedicated SQL pool.

Answer

Always Encrypted with keys stored in Azure Key Vault.

Question 8

You are a data engineer at a manufacturing company. You need to process sensor data from IoT devices that arrive in real time. The data is sent to Azure Event Hubs. You need to aggregate the data over 5-minute windows and store the results in Azure Data Lake Storage Gen2 in Parquet format. The solution should minimize cost and use serverless components. Which solution should you use?

Accepted Answer

Use Azure Stream Analytics to create a query with a tumbling window of 5 minutes, and output the results to Azure Data Lake Storage Gen2 in Parquet format.. Azure Stream Analytics is a fully managed, serverless real-time analytics service that natively supports tumbling windows and can output directly to Azure Data Lake Storage Gen2 in Parquet format. It minimizes operational cost and management overhead because there are no clusters to provision, and it integrates directly with Event Hubs as an input.

Answer

Use Azure Databricks with Structured Streaming to read from Event Hubs, aggregate with a sliding window, and write to ADLS Gen2 in Parquet.

Answer

Use Azure Data Factory with a tumbling window trigger to run a pipeline every 5 minutes that copies data from Event Hubs to ADLS Gen2.

Answer

Use Azure Functions with an Event Hubs trigger to aggregate data in memory and write to ADLS Gen2.

Question 9

Which of the following are valid methods to secure data at rest in Azure Data Lake Storage Gen2? (Choose two.)

Accepted Answer

Azure Storage Service Encryption (SSE) with Microsoft-managed keys. Azure Storage Service Encryption (SSE) with Microsoft-managed keys encrypts data at rest automatically for Azure Data Lake Storage Gen2 using 256-bit AES encryption. This is enabled by default for all storage accounts, ensuring data written to disk is encrypted before being persisted, with no additional configuration required.

Answer

Azure Active Directory (Azure AD) authentication for storage accounts

Answer

Configure firewall rules to restrict IP access

Question 10

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 automatically detects sensitive data types such as credit card numbers through classification rules that are part of a scan rule set. When you configure a scan rule set and include the built-in system classification rules (e.g., Credit Card Number, which matches patterns like 16-digit numbers with Luhn validation), Purview applies those classifications during scans and can then apply sensitivity labels. This is the native mechanism for automated sensitive data detection and labeling 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 11

You are monitoring an Azure Cosmos DB account using Azure Monitor. The 'Normalized RU Consumption' metric for a container is consistently above 90%. You need to ensure that the container can handle the load without throttling. What should you do?

Accepted Answer

Increase the provisioned throughput (RU/s) for the container.. The 'Normalized RU Consumption' metric indicates the percentage of provisioned throughput (RU/s) being used. Consistently above 90% means the container is operating near its capacity limit, risking throttling (HTTP 429 errors) during traffic spikes. Increasing the provisioned throughput (RU/s) directly raises the capacity, allowing the container to handle the load without throttling.

Answer

Change the partition key to a different property.

Answer

Switch the account to serverless mode.

Answer

Modify the indexing policy to exclude unused paths.

Question 12

Your Azure Synapse Analytics dedicated SQL pool is experiencing performance degradation. Queries that previously completed in seconds now take minutes. You notice high queue wait times in sys.dm_pdw_exec_requests. What is the most likely cause?

Accepted Answer

Concurrency throttling due to insufficient resources. High queue wait times in sys.dm_pdw_exec_requests typically indicate that queries are waiting for resources due to concurrency limits. In Azure Synapse dedicated SQL pools, each DWU level supports a fixed number of concurrent queries and slots; when the number of concurrent queries exceeds this limit, additional queries are queued. Therefore, the most likely cause is concurrency throttling due to insufficient resources, making option C correct.

Answer

Outdated statistics

Answer

A single long-running query blocking others

Answer

Data skew in distribution

Question 13

A company uses Azure Synapse Analytics dedicated SQL pool for a data warehouse. They notice that some queries are using more memory than expected, causing resource contention. Which TWO actions should they take to diagnose and optimize memory usage?

Accepted Answer

Increase the resource class for the users running the heavy queries.. Option B is correct because in a dedicated SQL pool, the resource class assigned to a user directly controls the memory grant (and concurrency slots) available to their queries; raising the resource class for users running memory-heavy queries gives those queries a larger memory allocation, reducing memory pressure and contention. Option D is correct because sys.dm_pdw_exec_requests is the DMV that exposes per-request execution details, including resource allocation and memory grant information, so querying it lets administrators identify exactly which queries are consuming excessive memory before tuning them. Option A is not appropriate because result-set caching is a feature of serverless SQL pools and does not address memory grants in a dedicated SQL pool. Option C is not the right first step because scaling DWU changes overall compute capacity and cost rather than diagnosing or right-sizing per-query memory grants. Option E is not relevant because rebuilding clustered columnstore indexes addresses data compression and segment quality, not query memory grant consumption.

Answer

Enable result-set caching.

Answer

Scale up the DWU setting.

Answer

Rebuild clustered columnstore indexes.

Question 14

You are partitioning a large fact table in Azure Synapse Dedicated SQL Pool by date. The table is used for queries that filter on CustomerID and Date. You want to minimize data movement. Which distribution strategy should you use?

Accepted Answer

Hash distribution on CustomerID. Hash distribution on CustomerID is correct because queries filtering on CustomerID and Date will benefit from collocated joins and aggregations when CustomerID is the distribution key. Since the table is large and partitioned by Date, hash distribution on CustomerID minimizes data movement by ensuring that rows with the same CustomerID reside on the same distribution node, allowing filters on Date to be applied locally within each partition.

Answer

Round-robin distribution

Answer

Replicate distribution

Answer

Hash distribution on Date

Question 15

Which TWO Azure services can be used to perform real-time data processing on streaming data?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics (B) is purpose-built for real-time stream processing: it ingests continuous data from sources such as Azure Event Hubs, IoT Hub, or Blob Storage and runs SQL-like queries with temporal windows to produce low-latency outputs. Azure Databricks (E) also supports real-time processing because its Structured Streaming engine (built on Apache Spark) can continuously process streaming data from Event Hubs, Kafka, or IoT Hub with exactly-once semantics. Azure Synapse Analytics dedicated SQL pool (A) is a batch-oriented MPP data warehouse, not a real-time streaming engine, so it does not fit. Azure Data Factory (C) is an orchestration/ETL service for scheduled batch data movement and transformation, not continuous stream processing. Azure Logic Apps (D) is a workflow automation and integration service triggered by events, but it does not perform real-time analytical stream processing.

Answer

Azure Synapse Analytics (dedicated SQL pool)

Answer

Azure Data Factory

Answer

Azure Logic Apps