DP-203 Practice Test 38 — 15 Questions

Question 1

You have an Azure Stream Analytics job that reads from an Event Hub and writes to Azure SQL Database. The job processes high-velocity IoT sensor data. You notice that the output to SQL Database is slower than expected and the job's watermark delay is increasing. What should you do to improve throughput?

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

Accepted Answer

Partition the output by a column like DeviceId.. Partitioning the output by a column like DeviceId allows Azure Stream Analytics to write to multiple SQL Database tables or use partitioned tables, enabling parallel writes. This reduces contention and improves throughput because the job can distribute the load across multiple write operations, directly addressing the bottleneck caused by high-velocity IoT sensor data overwhelming a single output stream.

Answer

Disable late arrival and out-of-order event handling.

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Increase the Streaming Units (SU) of the job.

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Decrease the window size in the query.

Question 2

A company uses Azure Data Lake Storage Gen2 with Azure Databricks. They notice that the job to write data into Delta Lake tables takes too long. The data is coming from a streaming source with a high velocity of small writes. Which approach should be taken to optimize write performance?

Accepted Answer

Configure the streaming to write in micro-batches with a higher trigger interval.. Increasing the trigger interval for micro-batches reduces the frequency of writes, allowing more data to accumulate per batch. This minimizes the overhead of small file commits and metadata operations in Delta Lake, which is the primary bottleneck for high-velocity streaming writes. By batching more records together, the job writes fewer, larger files, improving overall throughput.

Answer

Increase the cluster size to 16 nodes.

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Enable 'auto optimize' and 'optimized writes' on the Delta table.

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Change the output format from Delta to Parquet.

Question 3

A company runs a streaming pipeline using Azure Stream Analytics to ingest IoT data and output to Azure SQL Database. They notice that the output latency increases over time and eventually the job fails with a timeout error. What is the most likely cause?

Accepted Answer

The Azure SQL Database target table lacks proper indexes.. The most likely cause is that the Azure SQL Database target table lacks proper indexes. Without indexes, each batch of output from Stream Analytics triggers full table scans for inserts or updates, causing cumulative latency. Over time, the backlog exceeds the job's timeout threshold (default 5 minutes for output), leading to failure.

Answer

The Stream Analytics job has a high late arrival tolerance.

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The event hub is not partitioned correctly.

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The event hub consumer group is misconfigured.

Question 4

You are developing a data processing pipeline for a gaming company that uses Azure Databricks. The pipeline processes game event data from Azure Event Hubs. You need to detect cheating patterns by analyzing events in real time. The solution must be able to handle high throughput and low latency. The output should be written to Azure Cosmos DB for real-time dashboards. Which approach should you use?

Accepted Answer

Use Azure Databricks Structured Streaming to read from Event Hubs, use Spark SQL and machine learning to detect cheating patterns, and write to Cosmos DB using the Azure Cosmos DB Spark connector.. Azure Databricks Structured Streaming can handle high throughput and low latency, and it can write to Cosmos DB using the Azure Cosmos DB Spark connector. Option B is wrong because Azure Functions with Event Hubs trigger would process each event individually, which may struggle with very high throughput and lacks the flexibility for complex pattern detection using machine learning. Option C is wrong because Azure Data Factory is not designed for real-time processing; it is an orchestration tool for batch data movement. Option D is wrong because Azure Stream Analytics is simpler but may not provide the same flexibility for custom machine learning models as Databricks.

Answer

Use Azure Functions with Event Hubs trigger to process each event and write to Cosmos DB.

Answer

Use Azure Data Factory with continuous copy to load data into Cosmos DB and then use Azure Synapse Analytics to detect patterns.

Answer

Use Azure Stream Analytics to query the stream for cheating patterns and output to Cosmos DB.

Question 5

You need to store historical sales data for 10 years with infrequent queries. The storage cost must be minimized while retaining the ability to query using Azure Synapse serverless SQL pool. Which storage tier should you use?

Accepted Answer

Azure Storage Cool tier.. The Cool tier is the correct choice because it provides low-cost storage for data that is infrequently accessed (e.g., historical sales data spanning 10 years) while still supporting immediate read access via Azure Synapse serverless SQL pool. Unlike the Archive tier, Cool tier data is online and can be queried without the need for time-consuming rehydration, making it suitable for infrequent but on-demand analytical queries.

Answer

Azure Storage Archive tier.

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Azure Storage Premium tier.

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Azure Storage Hot tier.

Question 6

You need to transform data in Azure Databricks using Apache Spark. The data is stored in Delta Lake format in Azure Data Lake Storage Gen2. Which method should you use to read the data into a Spark DataFrame?

Accepted Answer

spark.read.format('delta').load('abfss://container@storage.dfs.core.windows.net/path'). The data is stored in Delta Lake format, which requires using the 'delta' format reader in Spark to properly read the transaction log and schema. The `spark.read.format('delta').load()` method is the standard way to read Delta tables, leveraging the Delta Lake protocol for ACID transactions and time travel capabilities.

Answer

spark.read.parquet('abfss://container@storage.dfs.core.windows.net/path')

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spark.read.csv('abfss://container@storage.dfs.core.windows.net/path')

Answer

spark.read.json('abfss://container@storage.dfs.core.windows.net/path')

Question 7

You run the PowerShell command shown in the exhibit for an Azure Synapse Analytics dedicated SQL pool. Which configuration will be applied?

Accepted Answer

The SQL pool is set to auto-pause after 15 minutes of inactivity.. The PowerShell command Set-AzSynapseSqlPool with the -AutoPauseDelayInMinutes parameter configures the dedicated SQL pool to automatically pause after 15 minutes of inactivity. This feature is supported for dedicated SQL pools in Azure Synapse Analytics. Therefore, option C is correct. Option A is incorrect because transactional replication is not configured by this command. Option B is incorrect because dedicated SQL pools do support auto-pause. Option D is incorrect because partitioning is not involved.

Answer

The SQL pool is configured with transactional replication and auto-pause.

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The command fails because dedicated SQL pools do not support auto-pause.

Answer

The SQL pool is partitioned into 10 partitions with automatic cleanup.

Question 8

You are reviewing an ARM template snippet for an Azure Blob Storage container. What is the effect of this configuration?

Accepted Answer

It enforces a 7-year immutable retention policy allowing appends.. The ARM template snippet configures a time-based retention policy on the container with a retention period of 2555 days (7 years) and sets `allowProtectedAppendWrites` to true. This enables an immutable storage policy that prevents deletion or modification of existing blobs but allows new append blocks to be added to append blobs, making option C correct.

Answer

It enables legal hold on the container for 2555 days.

Answer

It blocks append writes to blobs in the container.

Answer

It sets a 3-year retention policy blocking any modifications.

Question 9

A company uses Azure Data Lake Storage Gen2 with a hierarchical namespace. They need to secure access to specific directories using RBAC roles. Which RBAC role should be assigned to a user to grant read and write access to a specific folder without giving access to other folders in the same container?

Accepted Answer

Storage Blob Data Contributor with ACLs at folder level. Azure Data Lake Storage Gen2 supports POSIX-like access control lists (ACLs) at the directory and file level. Assigning the Storage Blob Data Contributor role at the storage account level grants broad data-plane access, but when combined with ACL entries on a specific folder, you can restrict read and write permissions to only that folder. This allows granular security without affecting other folders in the same container.

Answer

Storage Account Contributor

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Storage Blob Data Reader

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Storage Blob Data Owner

Question 10

Which THREE options are valid ways to transform data in Azure Synapse Analytics?

Accepted Answer

Use T-SQL scripts in a dedicated SQL pool.. T-SQL scripts are a native and primary method for transforming data within a dedicated SQL pool in Azure Synapse Analytics. You can use CREATE TABLE AS SELECT (CTAS), INSERT...SELECT, and other T-SQL statements to perform complex transformations like aggregations, joins, and data cleansing directly on the distributed data, leveraging the MPP (Massively Parallel Processing) engine for high performance.

Answer

Use Power Query online in Synapse pipelines.

Answer

Use Azure Machine Learning pipelines for data wrangling.

Question 11

Which TWO Azure services can be used to ingest streaming data into Azure Synapse Analytics?

Accepted Answer

Azure Stream Analytics.. Azure Stream Analytics is correct because it is a fully managed stream processing engine that can ingest streaming data from sources like Azure Event Hubs and output directly to Azure Synapse Analytics (via SQL pool or dedicated SQL pool). It enables real-time analytics on streaming data before landing it in Synapse for further analysis.

Answer

Azure Databricks Auto Loader.

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Azure Data Factory.

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Azure Logic Apps.

Question 12

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. The correct answers are B and E. Option B: Using access control lists (ACLs) in Azure Data Lake Storage Gen2 allows granular permissions at the directory and file level, enabling you to restrict access to sensitive data. Option E: Enabling encryption at rest with customer-managed keys in Azure Key Vault provides an additional layer of security by allowing you to manage your own encryption keys. Option A is incorrect because public network access from all networks exposes data to potential threats; it should be restricted. Option C is incorrect as anonymous access should always be disabled for sensitive data. Option D is incorrect because soft delete should be enabled to protect against accidental data deletion or corruption.

Answer

Enable public network access from all networks for ease of use

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Allow anonymous access to enable sharing

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Disable soft delete to prevent accidental retention of deleted data

Question 13

You are building a data processing solution that requires exactly-once semantics when writing to Azure Event Hubs from Azure Stream Analytics. Which output format should you configure?

Accepted Answer

Avro. Azure Stream Analytics supports exactly-once semantics when writing to Event Hubs only when using Avro serialization. This is because Avro provides a compact binary format with embedded schema, enabling Stream Analytics to track and deduplicate events precisely during output, which is required for exactly-once delivery. JSON, CSV, and Parquet do not support the necessary metadata and checkpointing mechanisms for exactly-once guarantees in this specific integration.

Answer

JSON

Answer

CSV

Answer

Parquet

Question 14

Your organization has an Azure Data Factory pipeline that executes a series of activities to transform data. One of the activities is an Azure Databricks notebook that should run only if the previous activity succeeds. You need to configure the pipeline to handle failures gracefully and send an email alert if the Databricks activity fails. What should you do?

Accepted Answer

Add a failure output path from the Databricks activity to a Web activity that calls an email API.. You can configure a failure output path from the Databricks activity and attach a Web activity that calls an email API to send an alert when the Databricks activity fails. Option B is incorrect because retry and timeout settings do not send email alerts. Option C is incorrect because a Schedule trigger does not handle activity-level failures directly. Option D is incorrect because setting a dependency to 'Succeeded' only runs subsequent activities on success, not on failure.

Answer

Configure a retry policy and a timeout for the Databricks activity.

Answer

Use a Schedule trigger to run the pipeline and check for failures using Azure Monitor.

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Set a dependency condition on the Databricks activity to 'Succeeded' and add a Send Email activity on the success path.

Question 15

Your company uses Azure Synapse Analytics to run a data warehouse. You have a dedicated SQL pool with a hash-distributed fact table named Sales. The distribution column is ProductID. You notice that queries against the Sales table are slow due to data skew. After analysis, you find that a few products (e.g., ProductID 100, 200) account for 80% of the rows. You need to optimize query performance without redesigning the entire table. You also need to minimize data movement during queries. Which action should you take?

Accepted Answer

Change the distribution to round-robin.. Round-robin distribution distributes data evenly across distributions, eliminating skew. However, it may increase data movement for joins. Given the severe skew, round-robin is a reasonable trade-off. Option B is wrong because increasing the number of distributions does not fix skew. Option C is wrong because changing to replicate distribution is not suitable for large fact tables. Option D is wrong because creating non-clustered indexes does not address distribution skew.

Answer

Increase the number of distributions to 120.

Answer

Change the distribution to replicate for the Sales table.

Answer

Create non-clustered indexes on the ProductID column.