DP-203 Timed Practice Test 5 — 10 Questions

Question 1

Match each storage redundancy option to its description in Azure Storage.

Question 2

Match each performance optimization technique to its description.

Question 3

A company uses Azure Synapse Analytics to process large datasets. They need to transform JSON data stored in Azure Data Lake Storage Gen2 into a star schema. Which data processing approach minimizes data movement and leverages the compute closest to the data?

Accepted Answer

Create external tables on the JSON files using PolyBase, then use CREATE EXTERNAL TABLE AS SELECT (CETAS) to write transformed Parquet files.. Option D is correct because it uses PolyBase external tables and CETAS to transform JSON data directly in Azure Data Lake Storage Gen2, minimizing data movement by leveraging the compute power of the dedicated SQL pool or serverless SQL pool closest to the data. This approach reads JSON in place, transforms it into Parquet format, and writes the star schema tables back to the data lake without copying data to an intermediate store.

Answer

Use Azure Data Factory to copy the JSON data into Azure SQL Database, then use T-SQL to transform.

Answer

Use Azure Data Factory with SSIS to transform and load into dedicated SQL pool.

Answer

Load data into a Spark DataFrame in Synapse notebooks, transform, and write back.

Question 4

A company uses Azure Databricks to process streaming data from Event Hubs. The data is written to a Delta table. The job occasionally fails due to checkpoint corruption. Which THREE measures should you implement to improve reliability?

Accepted Answer

Configure checkpointing to a durable storage like Azure Data Lake Storage.. Option A is correct because checkpointing to durable storage like Azure Data Lake Storage (ADLS) ensures that streaming progress metadata is persisted across cluster restarts and failures. ADLS provides high durability and availability, preventing checkpoint corruption that can occur with local or ephemeral storage, thereby enabling exactly-once processing guarantees in Structured Streaming.

Answer

Increase the batch interval to reduce load.

Answer

Increase the cluster size to handle spikes.

Question 5

Refer to the exhibit. An Azure Data Factory instance uses a self-hosted integration runtime. The exhibit shows the properties of the integration runtime. The data engineer notices that copy activities are failing with errors indicating that the integration runtime is not available. What is the most likely cause?

Accepted Answer

The integration runtime version is outdated and needs to be manually updated. Option C is correct because the exhibit shows the integration runtime version as '5.24.8345.1' and the status as 'Online', but copy activities are failing. The most likely cause is that the self-hosted IR version is outdated and no longer compatible with the Azure Data Factory service endpoints, leading to connectivity failures. Auto-update being disabled (Option B) would prevent automatic updates, but the core issue is the outdated version itself, which requires manual intervention to update.

Answer

The integration runtime status is "Offline"

Answer

Auto-update is disabled, preventing the IR from updating

Answer

Self-contained interactive authoring is disabled, causing connectivity issues

Question 6

You need to audit all data access to an Azure Storage account. Which Azure service should you enable?

Accepted Answer

Azure Storage analytics logs and send to Log Analytics workspace. Azure Storage analytics logs capture detailed information about successful and failed requests to a storage account, including authentication details, IP addresses, and operation types. By sending these logs to a Log Analytics workspace, you can query and analyze them using KQL, enabling comprehensive auditing of all data access. This is the correct service for auditing because it provides the granular, queryable logs required for security and compliance audits.

Answer

Azure Policy to audit storage account access

Answer

Azure Monitor metrics

Answer

Azure Security Center

Question 7

Match each Azure service tier to its description.

Question 8

Match each Azure security feature to its description.

Question 9

A company uses Azure Data Factory to orchestrate an ETL pipeline that copies data from an on-premises SQL Server to Azure Synapse Analytics. The pipeline runs hourly and uses a self-hosted integration runtime. Recently, the pipeline started failing with timeout errors. The on-premises SQL Server is healthy and the network is stable. What is the most likely cause and solution?

Accepted Answer

The self-hosted integration runtime is under-provisioned; scale up the VM or add more nodes. Option B is correct because timeouts often occur when the self-hosted IR is overloaded or has insufficient resources, and scaling up or adding nodes resolves the issue. Option A is wrong because the integration runtime version is automatically updated. Option C is wrong because the source query timeout is set to 120 seconds by default and increasing it may mask the problem. Option D is wrong because staging is used for large data transfers, but the issue is likely IR performance.

Answer

The self-hosted integration runtime version is outdated; update it to the latest version

Answer

The copy activity is not using staging; enable staging through Azure Blob Storage

Answer

The source query timeout in the copy activity is too low; increase it to 3600 seconds

Question 10

A company uses Azure Synapse Analytics with dedicated SQL pools. They need to allow a data scientist to read all tables in the 'sales' schema but prevent access to columns containing personally identifiable information (PII). Which feature should be used?

Accepted Answer

Column-level security. Column-level security (C) is the correct choice because it allows you to restrict access to specific columns in a table, such as PII columns, while granting read access to all other columns in the 'sales' schema. This is achieved by defining a GRANT SELECT statement on the table with a column list, or by using a security policy with a filter predicate that blocks access to sensitive columns. Unlike Dynamic Data Masking, which obfuscates data at query time but does not prevent the user from seeing the masked values in certain scenarios, Column-level security actually denies access to the specified columns entirely.

Answer

Dynamic data masking

Answer

Row-level security

Answer

Azure Active Directory authentication