DP-203 Practice Test 2 — 25 Questions

Question 1

You are analyzing the exhibit from an Azure Monitor metric query for a storage account. What is the primary purpose of this query?

Accepted Answer

To retrieve the average count of block blobs per hour.. The query uses the 'avg' aggregation on the 'BlobCount' metric, which calculates the average number of blobs over the specified time granularity (e.g., per hour). The result shows the average count of block blobs per hour, not the total count or the count in a specific tier. This aligns with option D, as the query is designed to retrieve the average count of block blobs per hour.

Answer

To calculate the average number of block blobs in the hot tier.

Answer

To identify the time period with the highest blob count.

Answer

To measure the total size of all block blobs in the account.

Question 2

You are designing a stream processing solution using Azure Stream Analytics. The job must reference a static lookup table (product catalog) stored in Azure Blob Storage. The catalog is updated once daily. The job should automatically pick up the latest version without restarting. Which two configurations are required? (Choose two.)

Accepted Answer

Set the reference input's 'Path pattern' to include date and time placeholders. Option B is correct because Azure Stream Analytics reference data inputs support path pattern placeholders like {date} and {time} to dynamically resolve the latest blob file. This allows the job to automatically load a new version of the static lookup table when the blob is updated, without requiring a job restart. The path pattern must be structured to match the naming convention of the uploaded file, such as 'catalog/{date}/{time}/products.csv'.

Answer

Configure the reference input with a static blob path

Answer

Use Azure Event Grid to trigger job restart on blob update

Answer

Store the reference data in Azure SQL Database instead of Blob Storage

Question 3

When designing a data processing solution using Azure Databricks, what is the recommended approach to handle schema evolution when reading data from Delta Lake tables?

Accepted Answer

Set the option 'mergeSchema' to 'true' on write. Option A is correct because in Delta Lake, schema evolution is automatically handled by setting the 'mergeSchema' option to 'true' on write operations. This allows new columns to be added or existing column types to be safely widened without manual intervention, preserving existing data and metadata integrity.

Answer

Set the option 'overwriteSchema' to 'true' on write

Answer

Manually alter the table schema using ALTER TABLE

Answer

Ignore schema changes and use 'failOnDataLoss' flag

Question 4

You are implementing a data pipeline using Azure Data Factory. The source is an on-premises SQL Server database. Which Azure Data Factory component is required to connect to the on-premises data source?

Accepted Answer

Self-hosted Integration Runtime. A self-hosted integration runtime (IR) is required to connect Azure Data Factory to on-premises SQL Server because it provides the compute environment for data movement between on-premises networks and Azure. It must be installed on a machine inside the corporate firewall, enabling secure communication via outbound HTTPS (port 443) to Azure. This is the only IR type that can access private, on-premises data sources directly.

Answer

Azure Integration Runtime

Answer

Managed Virtual Network Integration Runtime

Answer

Azure Data Factory Gateway

Question 5

Your company uses Azure Data Lake Storage Gen2. You need to ensure that data at rest is encrypted using a customer-managed key stored in Azure Key Vault. What should you configure?

Accepted Answer

Enable 'Azure Storage encryption' with customer-managed keys in the storage account's encryption blade.. The correct answer is B because Azure Storage encryption with customer-managed keys is configured in the encryption blade of the storage account. This ensures data at rest is encrypted using a key stored in Azure Key Vault. Option A is incorrect because Azure Policy can audit or enforce encryption but does not configure customer-managed keys. Option C is incorrect because client-side encryption encrypts data before it reaches Azure Storage, not at rest. Option D is incorrect because infrastructure encryption provides a second encryption layer but does not use customer-managed keys for the primary encryption.

Answer

Use Azure Policy to audit storage accounts without encryption.

Answer

Implement client-side encryption in the application code.

Answer

Enable 'Infrastructure encryption' for double encryption.

Question 6

A company uses Azure Synapse Analytics dedicated SQL pool. They notice that queries against a large fact table are running slower over time. The table is hash-distributed on a date key and has a clustered columnstore index. Which action should you take to improve query performance?

Accepted Answer

Rebuild the clustered columnstore index.. Over time, columnstore indexes can become fragmented due to insert, update, and delete operations, leading to compressed row groups that are not optimally sized or have deleted records. Rebuilding the clustered columnstore index reorganizes the data into fully compressed row groups, removes deleted rows, and restores the high compression and segment elimination that columnstore indexes rely on for fast query performance.

Answer

Add a non-clustered index on frequently filtered columns.

Answer

Change the distribution column to a column with higher cardinality.

Answer

Change the distribution to round-robin.

Question 7

You are tuning an Azure Stream Analytics job that reads from an Event Hub and writes to an Azure Synapse Analytics table. The job's SU% utilization is consistently at 90%. Which action would most likely reduce the SU% utilization?

Accepted Answer

Increase the number of streaming units (SU) allocated to the job.. Increasing the number of streaming units (SU) allocated to the job directly adds more compute resources, which reduces the SU% utilization by distributing the workload across more SUs. Since the job is consistently at 90% utilization, adding SUs lowers the per-SU load, preventing throttling and improving throughput. This is the standard scaling approach for Azure Stream Analytics when SU% is high.

Answer

Decrease the Event Hub throughput units.

Answer

Partition the output table in Azure Synapse Analytics.

Answer

Use a reference data join to filter events.

Question 8

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 9

Which THREE factors should you consider when designing a monitoring strategy for Azure Synapse Analytics dedicated SQL pool performance?

Accepted Answer

Use dynamic management views (DMVs) to identify long-running queries.. Option A is correct because dynamic management views (DMVs) in Azure Synapse Analytics dedicated SQL pool, such as sys.dm_pdw_exec_requests and sys.dm_pdw_request_steps, provide real-time insight into query execution, allowing you to identify long-running queries, monitor resource consumption, and detect performance bottlenecks. This is a foundational monitoring practice for tuning workload performance.

Answer

Ensure data is evenly distributed across distributions.

Answer

Configure automatic index rebuild for columnstore indexes.

Question 10

You are reviewing an Azure Policy assignment that uses the above JSON to define a role-based access control (RBAC) action. What is the primary purpose of this policy?

Accepted Answer

To authorize generation of a shared access signature (SAS) token for the storage account.. The policy JSON defines a role-based access control (RBAC) action that grants the 'Microsoft.Storage/storageAccounts/listAccountSas/action' permission. This specific action authorizes the generation of a shared access signature (SAS) token at the storage account level, not at the container or blob level. Therefore, the primary purpose is to allow the generation of an account SAS token, which provides delegated access to storage services.

Answer

To assign RBAC roles to users for the storage account.

Answer

To enable delegation of access to a specific blob.

Answer

To allow users to set permissions on storage account containers.

Question 11

Refer to the exhibit. You have an Azure Data Factory pipeline that copies trade data from Azure Blob Storage to Azure SQL Database. The pipeline runs every hour and truncates the destination table before each copy. However, users report that data is missing during the copy window. What is the most likely cause?

Accepted Answer

The preCopyScript truncates the table before the copy completes, causing a period with no data. Option C is correct. The preCopyScript truncates the destination table before the copy operation begins. During the copy window, if the copy takes time or fails, the table remains empty, causing users to see missing data. Option A is incorrect because the output dataset configuration does not cause this issue. Option B is incorrect because writeBatchSize affects performance but not data loss. Option D is incorrect because recursive setting affects source file selection, not data loss.

Answer

The output dataset is not correctly configured

Answer

The writeBatchSize is too low, causing timeouts

Answer

The source dataset is set to recursive, which includes unwanted files

Question 12

You are building a data processing solution using Azure Databricks. The solution must process streaming data from Azure Event Hubs, join it with a static reference table stored in Azure Data Lake Storage Gen2 (Parquet format), and write the output to Azure Synapse Analytics. The reference table is updated daily. Which approach minimizes latency and ensures data consistency?

Accepted Answer

Use Spark Structured Streaming with a streaming join and cache the reference table as a static DataFrame.. Option A is correct because using Spark Structured Streaming with a streaming join and caching the reference table as a static DataFrame minimizes latency and ensures consistency by reading the reference table once and caching it for the duration of the stream. This avoids reloading the reference table in each micro-batch (as in option C) and avoids the latency of batch processing (as in option D). Option B, foreachBatch, is useful for writing to sinks like Synapse but does not address the join performance or consistency of the reference data.

Answer

Use Spark Structured Streaming with foreachBatch to write to Synapse.

Answer

Use Spark Structured Streaming with a streaming join and load the reference table in each micro-batch.

Answer

Use a batch job that runs every hour to process the data.

Question 13

Your organization uses Azure Purview for data governance. You need to automatically scan an Azure Data Lake Storage Gen2 account and classify sensitive data such as credit card numbers and social security numbers. What should you configure?

Accepted Answer

A new scan rule set in Purview with classification rules for sensitive data types. The correct answer is C: A new scan rule set in Purview with classification rules for sensitive data types. Purview allows you to create custom scan rule sets that include classification rules for sensitive data types like credit card numbers and social security numbers. When you run a scan on Azure Data Lake Storage Gen2, it uses these rules to automatically detect and classify the sensitive data. Option A is incorrect because Azure Information Protection (AIP) scanner is for classifying and protecting files in on-premises file shares and SharePoint, not for scanning Azure Data Lake Storage. Option B is incorrect because Microsoft Defender for Cloud is for security posture management and threat detection, not data classification. Option D is incorrect because Azure Policy with guest configuration is for auditing and enforcing compliance settings on virtual machines, not for data classification in storage.

Answer

Azure Information Protection (AIP) scanner

Answer

Microsoft Defender for Cloud

Answer

Azure Policy with built-in guest configuration

Question 14

Your organization uses Azure Synapse Analytics serverless SQL pools to query data in Azure Data Lake Storage Gen2. You need to ensure that only authorized users can access the data via the serverless SQL endpoint, while minimizing administrative overhead. What should you use?

Accepted Answer

Enable Microsoft Entra ID authentication and grant users permissions via Azure RBAC on the storage account.. Option A is correct because Microsoft Entra ID authentication allows users to authenticate with their existing identities, and Azure RBAC on the storage account provides granular, identity-based access control without managing separate SQL logins or credentials, minimizing administrative overhead. Option B is incorrect because managed identities are designed for service-to-service authentication, not for individual users. Option C is incorrect because storage account access keys provide broad, shared access that does not support per-user identity and auditing. Option D is incorrect because SAS tokens require token generation and management per user, adding overhead.

Answer

Use managed identities for the serverless SQL pool.

Answer

Use storage account access keys for authentication.

Answer

Use shared access signatures (SAS) tokens generated for each user.

Question 15

A data engineer monitors an Azure Stream Analytics job that processes real-time data. The job is falling behind, and the SU utilization is at 100%. Which action should be taken to improve performance?

Accepted Answer

Increase the number of Streaming Units (SU).. When SU utilization reaches 100%, the job is fully saturated and cannot process incoming data fast enough. Increasing the number of Streaming Units (SU) allocates more compute resources (CPU and memory) to the job, allowing it to handle higher throughput and reduce backlog. This is the direct and recommended action for resolving performance bottlenecks caused by insufficient SU capacity.

Answer

Reduce the number of Streaming Units.

Answer

Change the query compatibility level to 1.0.

Answer

Deploy a second Stream Analytics job and split the input.

Question 16

Which TWO actions should you take to reduce costs associated with an Azure Synapse Dedicated SQL Pool that is used for reporting during business hours only?

Accepted Answer

Pause the pool during non-business hours.. Option A is correct because pausing a Dedicated SQL Pool stops billing for compute resources (DWU) while retaining storage costs. Since the pool is only needed for reporting during business hours, pausing it during non-business hours directly eliminates compute charges for that period, which is the most significant cost driver.

Answer

Enable advanced data compression on all tables.

Answer

Scale down the pool during business hours.

Answer

Change the distribution of large tables to ROUND_ROBIN.

Question 17

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 18

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 19

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 20

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

DP-203 Practice Test 2 — 25 Questions

Exhibit

DP-203 Practice Test 2 — 25 Questions

Exhibit