DP-203 Practice Exam 3 — 20 Questions

Question 1

Your company runs a streaming job in Azure Stream Analytics that ingests data from Event Hubs and outputs to Azure Synapse Analytics. The job is failing with a 'Watermark delay' alert and the output to Synapse is delayed by over 30 minutes. The input rate is 5,000 events per second. The job uses a 1-minute tumbling window. What is the most likely cause of the delay?

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

Accepted Answer

The Stream Analytics job is under-provisioned in terms of Streaming Units (SUs).. A watermark delay alert in Azure Stream Analytics indicates that the job is falling behind in processing incoming data. With an input rate of 5,000 events per second and a 1-minute tumbling window, the job requires sufficient Streaming Units (SUs) to keep up. Under-provisioned SUs cause backpressure, leading to output delays exceeding 30 minutes.

Answer

The output schema in Synapse does not match the Stream Analytics output.

Answer

The Event Hubs has a large number of late-arriving events.

Answer

The tumbling window size is too large.

Question 2

Which TWO are valid ways to process data in Azure Synapse Analytics?

Accepted Answer

Use Synapse SQL pool to run T-SQL queries.. Option C is correct because a Synapse SQL pool (dedicated or serverless) is a core Synapse Analytics engine that executes T-SQL queries directly against data in the workspace, making it a valid data-processing method. Option E is correct because Synapse Spark notebooks run Apache Spark code, including Scala, Python, SQL, and R, and are a first-class way to transform and process data in Azure Synapse Analytics. Option A is not appropriate because Logic Apps is a workflow/automation service for orchestrating triggers and connectors, not a data transformation engine. Option B is not the intended Synapse processing method because Azure Functions is a separate serverless compute service outside Synapse's built-in SQL and Spark engines. Option D is not correct because Power BI is a visualization and reporting tool; its data transformation (Power Query) is for modeling/reporting, not a Synapse data-processing workload.

Answer

Use Logic Apps to run data transformations.

Answer

Use Azure Functions to process data in a serverless manner.

Answer

Use Power BI to transform data.

Question 3

You are designing a data processing solution in Azure Synapse Analytics. The solution must support both batch and streaming data ingestion. Which Azure service should you use to ingest streaming data into Synapse Analytics?

Accepted Answer

Azure Event Hubs. Azure Event Hubs is Microsoft's managed, real-time event-ingestion service designed for high-throughput streaming scenarios, and it integrates natively with Azure Synapse Analytics (via the Synapse Event Hubs connector or Spark Structured Streaming) to land streaming data into dedicated SQL pools, Spark pools, or the lake. It is the canonical answer for streaming ingestion into Synapse.

Answer

Azure Data Factory

Answer

Azure Blob Storage

Answer

Azure Analysis Services

Question 4

Your organization has an Azure Synapse Analytics dedicated SQL pool that stores sensitive customer data. You need to ensure that only authorized users can access the data, and auditing must be enabled to track all access attempts. What should you do first?

Accepted Answer

Configure Microsoft Entra ID authentication and use RBAC to grant only necessary permissions.. Before implementing column-level security, dynamic data masking, or auditing, you must first establish who the authorized users are by configuring Microsoft Entra ID authentication and applying RBAC to grant only the necessary permissions. This foundational identity and access control step ensures that subsequent security features (masking, column-level security, auditing) operate against a properly authenticated and authorized user base.

Answer

Implement column-level security to restrict sensitive columns.

Answer

Enable auditing on the SQL pool and configure a storage account for audit logs.

Answer

Apply dynamic data masking to the sensitive columns.

Question 5

You are monitoring Azure Stream Analytics job performance. The job is falling behind in processing real-time data. You notice that the SU (Streaming Unit) utilization is consistently at 90% or higher. What is the most appropriate action to improve throughput?

Accepted Answer

Increase the number of Streaming Units (SUs). Streaming Unit (SU) utilization consistently at 90% or higher indicates the job is resource-constrained. Increasing the number of SUs allocates more compute resources, which can improve throughput and reduce backlog. This is the most direct and appropriate action to scale the job.

Answer

Change the output to use a partition scheme

Answer

Reduce the window duration in the query

Answer

Decrease the event ordering tolerance

Question 6

You need to ensure that data stored in Azure Data Lake Storage Gen2 is encrypted at rest using customer-managed keys. Which Azure service should you use to manage the keys?

Accepted Answer

Azure Key Vault. Azure Key Vault is the service used to store and manage customer-managed keys (CMK) for Azure Storage encryption at rest. You create a key in Key Vault (or Managed HSM) and configure the storage account to use that key via the encryption settings, enabling CMK.

Answer

Microsoft Purview

Answer

Azure Confidential Computing

Answer

Microsoft Entra ID

Question 7

You are designing a data storage solution for a retail company that needs to store transaction data that is frequently updated and requires strong consistency. The solution must support complex queries and joins across multiple tables. Which Azure data service should you recommend?

Accepted Answer

Azure SQL Database. Azure SQL Database is a fully managed relational database service that provides strong consistency, supports complex queries and joins across multiple tables, and is optimized for frequently updated transaction data. It offers ACID compliance and built-in high availability, making it the ideal choice for this retail scenario.

Answer

Azure Cosmos DB

Answer

Azure Synapse Analytics

Answer

Azure Table Storage

Question 8

You are designing a data storage solution for a marketing analytics platform. The platform collects clickstream data from websites and needs to store it for both real-time dashboards and historical analysis. The data is semi-structured (JSON) and arrives at a rate of 10,000 events per second. You need to choose an Azure storage solution that can handle the ingestion rate, support schema-on-read, and integrate with Azure Databricks for advanced analytics. The solution must also be cost-effective for long-term storage. What should you use?

Accepted Answer

Azure Data Lake Storage Gen2. Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct choice because it combines a hierarchical namespace with Azure Blob Storage, providing high-throughput ingestion (up to 60 GB/s per account) to handle 10,000 events per second of semi-structured JSON data. It supports schema-on-read natively, allowing Azure Databricks to query the data directly using Spark without prior schema definition, and its tiered storage (hot, cool, archive) makes it cost-effective for long-term historical analysis.

Answer

Azure Table Storage

Answer

Azure Cosmos DB

Answer

Azure SQL Database

Question 9

Your organization uses Azure Data Factory to orchestrate data pipelines. You need to ensure that sensitive data is not exposed in pipeline logs. What should you configure?

Accepted Answer

Enable 'Secure output' on pipeline activities.. Enabling 'Secure output' on pipeline activities prevents sensitive data from being written to Azure Data Factory logs. Option A is incorrect because while storing connection strings in Azure Key Vault is a security best practice, it does not prevent sensitive data from appearing in pipeline logs. Option C is incorrect because setting a retention policy for logs controls how long logs are kept, but does not prevent sensitive data exposure. Option D is incorrect because data flow debug logs are for debugging and do not mask sensitive data in pipeline logs.

Answer

Store connection strings in Azure Key Vault.

Answer

Set a retention policy for pipeline logs.

Answer

Use data flow debug logs with session logs.

Question 10

You need to design a data storage solution for a batch processing pipeline that processes petabytes of data daily. The data is stored in Parquet format and must be accessible by both Azure Databricks and Azure Synapse Analytics. Which storage solution should you recommend?

Accepted Answer

Azure Data Lake Storage Gen2. Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct choice because it combines a hierarchical namespace with Azure Blob Storage's scalable object storage, providing native POSIX-like access control and high throughput for petabyte-scale batch processing. Both Azure Databricks and Azure Synapse Analytics have optimized connectors for ADLS Gen2 that leverage the hierarchical namespace for efficient partition pruning and file listing, which is critical for Parquet-based analytics at this scale.

Answer

Azure Files

Answer

Azure SQL Database

Answer

Azure Blob Storage

Question 11

Refer to the exhibit. A data engineer notices that the copy activity sometimes copies 0 rows despite reading 1 million rows. What is the most likely cause?

Accepted Answer

The source query includes a 'WHERE' clause that filters out all rows on alternate runs.. In Azure Data Factory, the copy activity can be configured for incremental loads by using a source query with a WHERE clause that filters rows based on a watermark (e.g., last modified timestamp). On pipeline runs where no new or updated rows exist in the source table, this WHERE clause will return zero rows. Although the source table contains 1 million rows, the incremental filter causes the copy activity to read and write 0 rows. This is the most likely cause of the described behavior.

Answer

The source table is being updated between pipeline runs, but the copy activity is not configured to upsert.

Answer

The pipeline is using a full load each time, overwriting the sink, so the second run sees the same data and skips because the sink already has it.

Answer

The sink is a file system that fails to write due to permission issues.

Question 12

You are designing a data processing pipeline that ingests data from a REST API endpoint every hour. The API returns JSON data with a varying schema. You need to store the raw data in Azure Data Lake Storage Gen2 and later process it using Azure Databricks. Which file format should you use for the raw data storage?

Accepted Answer

JSON. C is correct because the raw data arrives from a REST API with a varying JSON schema, and storing it in JSON format preserves the exact structure and schema variability without data loss or transformation. JSON is schema-on-read, meaning the raw data can be ingested as-is into Azure Data Lake Storage Gen2 and later processed by Azure Databricks, which natively supports JSON parsing. This avoids premature schema enforcement that would occur with columnar or binary formats.

Answer

Parquet

Answer

CSV

Answer

Avro

Question 13

Which TWO actions should you take to secure sensitive data in Azure Data Lake Storage Gen2? (Choose two.)

Accepted Answer

Use access control lists (ACLs) to restrict access to specific directories. Option B is correct because Azure Data Lake Storage Gen2 supports POSIX-style access control lists (ACLs) that let you grant or deny read/write/execute permissions at the directory and file level, which is essential for least-privilege access to sensitive data. Option E is correct because enabling encryption at rest with customer-managed keys stored in Azure Key Vault gives you control over the key lifecycle and satisfies compliance requirements for protecting sensitive data at rest. Option A is wrong because enabling public network access from all networks exposes the storage account to the internet and increases attack surface. Option C is wrong because allowing anonymous access permits unauthenticated reads and is a security risk, not a protection measure. Option D is wrong because disabling soft delete removes a recovery safeguard and does nothing to secure sensitive data.

Answer

Enable public network access from all networks for ease of use

Answer

Allow anonymous access to enable sharing

Answer

Disable soft delete to prevent accidental retention of deleted data

Question 14

A data engineer needs to store semi-structured JSON logs from IoT devices. The data will be queried using SQL and must support high-throughput writes. Which Azure data store is most appropriate?

Accepted Answer

Azure Cosmos DB with Core (SQL) API. Azure Cosmos DB with Core (SQL) API is the most appropriate choice because it natively stores semi-structured JSON documents, supports high-throughput writes with single-digit millisecond latency, and allows querying the JSON data directly using SQL syntax. Its schema-agnostic design and automatic indexing make it ideal for IoT workloads where device telemetry arrives at high velocity and must be immediately queryable.

Answer

Azure Blob Storage with JSON blobs

Answer

Azure Data Lake Storage Gen2 with JSON files and PolyBase

Answer

Azure SQL Database with JSON columns

Question 15

You need to ensure that sensitive data stored in Azure SQL Database is encrypted at rest. Which feature should you enable?

Accepted Answer

Transparent Data Encryption (TDE). Transparent Data Encryption (TDE) should be enabled to ensure sensitive data stored in Azure SQL Database is encrypted at rest. TDE encrypts the database, backups, and log files at rest without requiring changes to the application.

Answer

Always Encrypted

Answer

Azure Information Protection

Answer

Dynamic Data Masking

Question 16

Refer to the exhibit. You are deploying an Azure Synapse Analytics workspace using an ARM template. The exhibit shows the encryption configuration. What is the effect of setting infrastructureEncryption to Enabled?

Accepted Answer

It adds a second layer of encryption at the infrastructure level, ensuring data is encrypted at rest with two different keys.. Enabling infrastructureEncryption adds a second layer of encryption at the infrastructure level, ensuring data is encrypted at rest with two different keys (double encryption). Option A is incorrect because enabling infrastructure encryption does not disable customer-managed key encryption; it adds an additional layer. Option B is incorrect because infrastructure encryption applies to data at rest, not in transit between nodes. Option C is incorrect because infrastructure encryption applies to the entire Azure Synapse Analytics workspace, not just SQL pools, and it is not transparent data encryption (TDE); it is a separate infrastructure-level encryption.

Answer

It disables encryption using the customer-managed key.

Answer

It enables encryption of data in transit between nodes.

Answer

It enables transparent data encryption for SQL pools.

Question 17

You are responsible for managing an Azure Data Lake Storage Gen2 account that stores parquet files for analytics. You need to implement a data retention policy that automatically deletes files older than 90 days in the 'logs' container. Additionally, you need to ensure that no data is lost due to accidental deletion; you want to be able to recover deleted files within 30 days. You also need to monitor the storage account for unusual access patterns. The solution must minimize administrative effort. What should you do?

Accepted Answer

Enable soft delete with a retention period of 30 days and configure a lifecycle management rule to delete blobs older than 90 days. Enabling soft delete with a 30-day retention period allows recovery of accidentally deleted blobs within that window. A lifecycle management rule automatically deletes blobs older than 90 days, meeting the retention policy with minimal administrative effort.

Answer

Create an Azure Policy to enforce tag-based retention and use Azure Monitor to alert on access

Answer

Enable versioning and configure a retention policy in Azure Policy

Answer

Use Azure Backup for the storage account and manually delete old files

Question 18

You have an Azure Data Factory pipeline that loads data from an on-premises SQL Server to Azure Synapse Analytics. The pipeline fails intermittently with network connectivity errors. You need to ensure reliable data transfer with minimal latency. Which solution should you recommend?

Accepted Answer

Deploy a self-hosted IR with high availability on two nodes. A self-hosted Integration Runtime (IR) is required for Azure Data Factory to reach on-premises SQL Server, and deploying it with high availability across two nodes eliminates the single point of failure causing intermittent connectivity errors. The self-hosted IR runs inside the corporate network, so it uses the existing network path rather than requiring new VPN infrastructure, and HA mode ensures failover if one node goes down. This directly addresses both reliability and minimal latency.

Answer

Set up a site-to-site VPN gateway

Answer

Stage data in Azure Blob Storage using sharded files

Answer

Use an Azure Integration Runtime instead

Question 19

Which THREE of the following are best practices for designing tables in a dedicated SQL pool in Azure Synapse Analytics?

Accepted Answer

Avoid data skew by choosing a good distribution key.. Option B is correct because choosing a good distribution key prevents data skew, which otherwise causes some distributions to hold disproportionate rows and forces the query engine to move data across nodes, degrading performance in a dedicated SQL pool. Option D is correct because replicated tables copy the full table to every compute node, eliminating data movement for joins; this is recommended for small dimension tables under roughly 1 GB (or up to 2 GB compressed). Option E is correct because hash distribution on a high-cardinality column spreads rows evenly across the 60 distributions, which is ideal for large fact tables and minimizes skew. Option A is wrong because clustered columnstore indexes are the default and preferred storage for large tables in dedicated SQL pools, delivering high compression and fast analytical scans. Option C is wrong because round-robin distribution is a fallback for staging or tables with no clear join key, not a blanket best practice for all large fact tables, since it can cause costly data movement during joins.

Answer

Avoid using clustered columnstore indexes on large tables.

Answer

Use round-robin distribution for all large fact tables.

Question 20

You need to monitor resource utilization for an Azure Synapse Analytics dedicated SQL pool. Which Azure Monitor metric shows the percentage of allocated DWU being used?

Accepted Answer

DWU used. The 'DWU used' metric in Azure Monitor reports the percentage of allocated Data Warehouse Units (DWUs) being consumed by the dedicated SQL pool. It is the direct measure of how much of the provisioned compute capacity is in use, making it the correct answer for monitoring DWU utilization. This metric is available in the Azure portal under the Synapse workspace's Metrics blade.

Answer

Memory percentage

Answer

Data IO percentage

Answer

CPU percentage