DP-203 Practice Test 44 — 15 Questions

Question 1

A company is designing a data lake on Azure Data Lake Storage Gen2. Data comes from multiple sources with varying schemas. The team must minimize storage costs while keeping all data available for future processing. Which storage tier should they use for the raw ingested data?

Accepted Answer

Cool tier. The Cool tier is the optimal choice for raw ingested data in a data lake because it offers low storage costs while maintaining low-latency access for future processing. Unlike the Archive tier, Cool tier data is immediately available without the multi-hour rehydration delay, and it is significantly cheaper than the Hot tier for data that is infrequently accessed but must remain online for ETL or batch processing.

Answer

Premium tier

Answer

Archive tier

Answer

Hot tier

Question 2

A data engineer needs to store CSV files containing customer data in Azure Blob Storage. The files must be encrypted at rest using a customer-managed key stored in Azure Key Vault. What should they configure?

Accepted Answer

Azure Storage Service Encryption (SSE) with customer-managed keys. Azure Storage Service Encryption (SSE) for Blob Storage encrypts data at rest automatically. By choosing customer-managed keys (CMK) stored in Azure Key Vault, the customer retains control over the encryption keys, meeting the requirement for customer-managed key encryption at rest. SSE with CMK is the correct service for encrypting blobs with a key the customer manages.

Answer

Azure Disk Encryption

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Azure Storage Firewall

Answer

Azure Information Protection

Question 3

A company stores IoT sensor data in Azure Blob Storage. The data is appended every minute and must be queried in near real-time using a SQL interface. Which Azure service should be used to enable this?

Accepted Answer

Azure Data Lake Storage Gen2. Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct choice because it combines Blob Storage's scalable, append-friendly architecture with a hierarchical namespace and full POSIX-like ACLs, enabling SQL-based querying via Azure Synapse Serverless SQL or PolyBase. The append-blob pattern (every-minute writes) is natively supported, and the SQL interface (e.g., OPENROWSET) can query the data in near real-time without moving it.

Answer

Azure Cosmos DB

Answer

Azure SQL Database

Answer

Azure Synapse SQL Pool

Question 4

You are designing a solution to store telemetry data from millions of devices. Each device sends a JSON payload every 5 seconds. The data must be partitioned by device ID and time for efficient querying and must support real-time streaming ingestion. Which Azure storage solution should you recommend?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is the correct choice because it offers a multi-model, globally distributed database service with native support for real-time streaming ingestion via the Change Feed and automatic indexing. Its partition key design (device ID + time) enables efficient querying across millions of devices, and it guarantees single-digit millisecond read/write latencies essential for telemetry data arriving every 5 seconds.

Answer

Azure SQL Database

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Azure Blob Storage with Azure Event Hubs

Answer

Azure Table Storage

Question 5

A company is designing a data storage solution for streaming IoT telemetry data. The data is JSON-formatted, arrives at up to 10,000 events per second, and must be stored for at least 30 days for real-time dashboards and ad-hoc querying. The solution must minimize operational overhead and query latency. Which Azure service should they use?

Accepted Answer

Azure Data Explorer (ADX). Azure Data Explorer (ADX) is purpose-built for high-velocity telemetry and time-series data, ingesting up to 10,000 events per second with low latency. Its columnar storage and indexing enable sub-second queries on JSON data for real-time dashboards, while the 30-day retention is natively configurable via caching and soft-delete policies. This minimizes operational overhead by eliminating the need for manual partitioning or index tuning.

Answer

Azure Blob Storage with Azure Data Lake Storage Gen2

Answer

Azure Cosmos DB with analytical store

Answer

Azure SQL Database with elastic query

Question 6

A data engineer runs the Azure CLI command shown in the exhibit. The blob is stored in Azure Blob Storage. The team previously set a lifecycle management rule to move blobs to the Archive tier after 30 days. The blob was created 45 days ago. What is the most likely reason the blob is still in the Cool tier?

Accepted Answer

The lifecycle management rule was configured after the blob was created, and it can take up to 24 hours for the rule to be evaluated.. The lifecycle management rule is evaluated by Azure Storage once per day. If the rule was configured after the blob was created, it may not have been evaluated yet, and it can take up to 24 hours for the rule to apply. Since the blob is 45 days old and still in Cool tier, the most likely reason is that the rule has not yet been evaluated after its configuration.

Answer

The lifecycle management rule only applies to blobs in the Hot tier.

Answer

The blob is in a container that is excluded from the lifecycle rule.

Answer

The blob must be in the Hot tier for the rule to move it to Archive.

Question 7

Refer to the exhibit. You are deploying the ARM template to create an Azure Synapse Analytics workspace. The deployment fails because the managed resource group cannot be created. What is the most likely reason?

Accepted Answer

The managedResourceGroupName property is not allowed; Azure Synapse creates the resource group automatically. Option A is correct because the `managedResourceGroupName` property is not allowed in an ARM template for Azure Synapse Analytics. Azure Synapse automatically creates and manages its own managed resource group (e.g., `managed-rg-<workspace-name>`) to hold provisioned resources like Spark pools and SQL pools. Specifying this property in the template causes a validation error, as the service requires full control over the naming and lifecycle of this group.

Answer

The apiVersion is invalid for this resource type

Answer

The location is missing the 'properties' section

Answer

The storage account URL must use the blob endpoint, not DFS

Question 8

You need to incrementally load new and updated records from a source SQL Server database to Azure Synapse Dedicated SQL Pool. The source table has a LastModifiedDate column. Which Azure Data Factory feature should you use to implement incremental loading efficiently?

Accepted Answer

Incremental copy (watermark) pattern using a Lookup activity and a Copy activity. The incremental copy (watermark) pattern using a Lookup activity and a Copy activity is the correct approach because it allows you to query the source table for the maximum LastModifiedDate value (the watermark), store it in a control table or variable, and then use a Copy activity with a WHERE clause to load only rows where LastModifiedDate is greater than the last run's watermark. This pattern is purpose-built for efficiently handling new and updated records in Azure Data Factory without reprocessing the entire dataset.

Answer

Alter Row transformation

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Schedule trigger

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Lookup activity alone

Question 9

Which TWO of the following are recommended practices for designing a data storage solution using Azure Data Lake Storage Gen2?

Accepted Answer

Enable soft delete to protect against accidental deletion. Option A is correct because enabling soft delete on Azure Data Lake Storage Gen2 protects against accidental deletion by retaining deleted data for a specified retention period. This allows recovery of blobs or snapshots that were deleted, overwritten, or modified, which is a critical data protection practice for enterprise storage solutions.

Answer

Use Kerberos authentication for the storage account

Answer

Store all files in a single directory for simplicity

Answer

Enable anonymous public access for ease of use

Question 10

A healthcare company stores sensitive patient data in Azure Data Lake Storage Gen2. They need to ensure that only authorized users can access data and that all access is audited. They also need to prevent data from being accessed by unauthorized Azure services. Which combination of security features should be used?

Accepted Answer

Use Azure RBAC and ACLs for authorization, enable firewall and virtual network service endpoints, and enable diagnostic settings for auditing.. Option B is correct because it combines Azure RBAC and ACLs for fine-grained authorization, a firewall with virtual network service endpoints to restrict access to authorized networks, and diagnostic settings to capture audit logs. This layered approach ensures that only authorized users can access the data, all access is audited, and unauthorized Azure services are blocked by the firewall and service endpoints.

Answer

Use a private endpoint and Azure AD authentication, disable public access.

Answer

Use managed identity for service access and disable public access.

Answer

Use Azure AD authentication and SAS tokens for access, enabling diagnostic logs for auditing.

Question 11

Your team needs to provide near-real-time analytics on IoT sensor data streaming into Azure Event Hubs. The data must be stored in Azure Data Lake Storage Gen2 in Parquet format, partitioned by date and device ID. Which architecture should you implement?

Accepted Answer

Use Azure Stream Analytics with output to Data Lake Storage Gen2, using partitioning by date and device ID.. Azure Stream Analytics provides native, low-latency processing of streaming data from Event Hubs with direct output to Azure Data Lake Storage Gen2. It supports automatic partitioning by specifying a partition key (e.g., date and device ID) in the output configuration, enabling efficient, near-real-time writes in Parquet format without additional orchestration.

Answer

Use Azure Data Factory with a tumbling window trigger to copy data from Event Hubs to Data Lake Storage.

Answer

Use Azure Functions to read from Event Hubs and write to Data Lake Storage.

Answer

Use Azure Databricks with Structured Streaming to read from Event Hubs and write to Data Lake Storage.

Question 12

You need to perform incremental data loading from Azure SQL Database to Azure Data Lake Storage Gen2 using Azure Data Factory. Which approach is the most efficient?

Accepted Answer

Use a lookup activity to retrieve the last watermark value, then copy only new records with a filter.. Option A is correct because it uses a lookup activity to retrieve the last watermark value (e.g., a timestamp or incrementing key), then copies only new or changed records via a filter in the Copy activity. This minimizes data movement and processing time, making it the most efficient incremental loading approach in Azure Data Factory.

Answer

Use a tumbling window trigger with a data flow that processes all data each time.

Answer

Use a mapping data flow with a full load and then use Azure Databricks to deduplicate.

Answer

Copy the entire table every time and use Azure Synapse serverless SQL to filter duplicates.

Question 13

A data engineer is designing a batch processing pipeline that reads data from Azure Blob Storage, transforms it using Azure Databricks, and writes the output to Azure Synapse Analytics. The source files are in CSV format and arrive daily at 02:00 UTC. The transformation must be idempotent and the pipeline should handle late-arriving data (up to 2 hours). What is the best approach to trigger the pipeline?

Accepted Answer

Tumbling window trigger with window size of 1 day and a late arrival window of 2 hours. Option C is correct because a tumbling window trigger in Azure Data Factory allows you to define a fixed-size window (1 day) and a late arrival window (2 hours), which ensures idempotent processing by automatically rerunning the window for late-arriving data within the specified delay. This matches the requirement for daily batch processing at 02:00 UTC while handling data arriving up to 2 hours late.

Answer

Storage event trigger using Azure Event Grid

Answer

Schedule trigger set to 02:00 UTC daily

Answer

Event trigger on blob creation in the container

Question 14

Refer to the exhibit. You are reviewing an Azure Cosmos DB for NoSQL container configuration. The container stores customer orders. The application frequently queries orders by orderId. However, these queries are consuming high RUs and are slow. What is the most likely cause?

Accepted Answer

The unique key on orderId does not index the field; queries by orderId are cross-partition.. Option A is correct because the unique key constraint on `orderId` does not automatically create an index for that field; it only enforces uniqueness. Without an explicit indexing policy that includes `orderId`, queries filtering by `orderId` must perform a cross-partition scan, which consumes high RUs and is slow. In Azure Cosmos DB for NoSQL, queries that cannot be served from a single physical partition due to missing index or partition key mismatch result in fan-out across all partitions, dramatically increasing RU consumption.

Answer

The _etag field is excluded from indexing, causing high RU for queries that include it.

Answer

The indexing mode is set to consistent, which causes high RU usage.

Answer

The container is provisioned with only 400 RU/s, which is too low for the workload.

Question 15

You are designing a data processing pipeline in Azure Synapse Analytics. The pipeline must ingest streaming data from Azure Event Hubs, perform real-time aggregations, and store the results in a dedicated SQL pool. Which component should you use to perform the real-time transformations?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is the correct choice because it is a fully managed, real-time analytics service designed specifically for processing streaming data from sources like Azure Event Hubs. It supports SQL-based query language for performing aggregations, windowing functions, and temporal joins, and can directly output results to a dedicated SQL pool in Azure Synapse Analytics. This makes it the optimal component for ingesting streaming data, performing real-time transformations, and storing aggregated results in a Synapse dedicated SQL pool.

Answer

Azure Databricks with Structured Streaming

Answer

Azure Data Factory

Answer

Azure Synapse Pipelines