DP-203 Practice Test 21 — 15 Questions

Question 1

Which THREE measures should you implement to monitor and optimize the performance of Azure Data Lake Storage Gen2?

Accepted Answer

Enable Storage Insights to monitor capacity and transactions.. Option B is correct because Azure Storage Insights (part of Azure Monitor) provides built-in dashboards for monitoring ADLS Gen2 capacity, transactions, latency, and availability metrics, which is essential for tracking and optimizing storage performance. Option D is correct because Storage Analytics logging captures detailed per-request records (including latency, request rate, and operation type) for blob and Data Lake Gen2 endpoints, enabling granular performance analysis. Option E is correct because Azure Monitor diagnostic settings route resource logs (such as StorageRead, StorageWrite, and StorageDelete) and metrics to Log Analytics, Event Hubs, or Storage, allowing you to query read/write request patterns and diagnose performance bottlenecks. Option A is not correct because NSG flow logs capture IP-level network traffic metadata for security and connectivity troubleshooting, not storage performance or capacity metrics. Option C is not correct because lifecycle management policies optimize cost by tiering or deleting data based on age, not by monitoring or optimizing performance.

Answer

Enable Network Security Group flow logs for the storage account subnet.

Answer

Configure lifecycle management policies to move cold data to archive tier.

Question 2

You are designing a security strategy for an Azure Data Lake Storage Gen2 account that stores sensitive data. You need to ensure that data is encrypted at rest using customer-managed keys. What should you configure?

Accepted Answer

Configure a customer-managed key in Azure Key Vault and associate it with the storage account.. Customer-managed keys for Azure Storage encryption require you to create and manage a key in Azure Key Vault and then configure the storage account to use that key. This gives you control over encryption at rest. Microsoft-managed keys are the default but do not meet the requirement. Infrastructure encryption adds a second layer but does not change key management. Azure Disk Encryption protects VM disks, not the data lake. Therefore, the correct approach is to use a customer-managed key from Key Vault.

Answer

Enable Azure Storage Service Encryption with Microsoft-managed keys.

Answer

Enable infrastructure encryption (double encryption) on the storage account.

Answer

Use Azure Disk Encryption on the virtual machines that access the data lake.

Question 3

You are designing a data processing solution in Azure Synapse Analytics. The solution must process streaming data from Azure Event Hubs and store the results in a dedicated SQL pool. The solution must support exactly-once semantics and handle late-arriving data. Which Azure service should you use to implement this solution?

Accepted Answer

Azure Stream Analytics.. Azure Stream Analytics is the correct choice because it natively integrates with Azure Event Hubs and dedicated SQL pools, supports exactly-once semantics through checkpointing and output deduplication, and provides built-in handling for late-arriving data via configurable late arrival tolerance windows and out-of-order event policies.

Answer

Azure Data Factory with tumbling window trigger.

Answer

Azure Functions with Event Hubs trigger.

Answer

Azure HDInsight Spark Structured Streaming.

Question 4

A company uses Azure Synapse Analytics dedicated SQL pool to store sales data. The sales table is partitioned by month and has a clustered columnstore index. Over time, the performance of queries filtering on a specific month has degraded. The data engineer suspects high rowgroup elimination. Which action should be taken to improve performance?

Accepted Answer

Reorganize or rebuild the columnstore index on the table.. Reorganizing or rebuilding the columnstore index compresses fragmented rowgroups and merges small rowgroups into optimal sizes (typically 102,400 rows per rowgroup). This directly addresses the degraded rowgroup elimination: when rowgroups are too small or fragmented, the engine cannot efficiently skip entire rowgroups during partition-level scans, causing more I/O and slower performance.

Answer

Change the table distribution to hash-distributed on the partition key.

Answer

Drop and recreate the affected partitions.

Answer

Update statistics on the partitioned column.

Question 5

Drag and drop the steps to configure Azure Stream Analytics job with event input and Power BI output into the correct order.

Accepted Answer

Step 1: Set up Event Hub as data source, Step 2: Create Stream Analytics job, Step 3: Configure input (Event Hub), Step 4: Configure output (Power BI), Step 5: Write the query, Step 6: Start the job. First, set up the event hub as the data source. Then create the Stream Analytics job, configure input and output, write the query, and start it.

Answer

Step 1: Set up Event Hub as data source, Step 2: Configure input (Event Hub), Step 3: Create Stream Analytics job, Step 4: Configure output (Power BI), Step 5: Write the query, Step 6: Start the job

Answer

Step 1: Set up Event Hub as data source, Step 2: Create Stream Analytics job, Step 3: Configure output (Power BI), Step 4: Configure input (Event Hub), Step 5: Write the query, Step 6: Start the job

Answer

Step 1: Set up Event Hub as data source, Step 2: Create Stream Analytics job, Step 3: Configure input (Event Hub), Step 4: Write the query, Step 5: Configure output (Power BI), Step 6: Start the job

Question 6

A company uses Azure Synapse Analytics with a dedicated SQL pool. They need to ensure that a team of data scientists can query all tables in the 'sales' schema but cannot modify any data or schema objects. Which role should the team be assigned?

Accepted Answer

db_datareader. The `db_datareader` role grants read-only access to all user tables in a database, allowing the team to query all tables in the 'sales' schema without the ability to modify data or schema objects. This aligns perfectly with the requirement for data scientists to perform SELECT queries only.

Answer

db_owner

Answer

db_ddladmin

Answer

db_datawriter

Question 7

You are designing a data processing solution using Azure Databricks. You need to read data from Azure Data Lake Storage Gen2, transform it using Spark SQL, and write to a Delta table. Which TWO configurations are required to ensure optimal performance for large datasets?

Accepted Answer

Use Delta Lake's OPTIMIZE command to compact small files.. The OPTIMIZE command in Delta Lake compacts small files into larger ones, reducing the number of files that Spark must read during subsequent queries and writes. This is critical for large datasets where many small files can cause significant overhead in file listing and task scheduling. Option C is correct because Z-order optimization on frequently filtered columns improves data skipping, allowing Delta Lake to prune irrelevant files during scans, which dramatically reduces I/O and speeds up query performance.

Answer

Disable automatic schema detection to reduce overhead.

Answer

Cache the entire DataFrame in memory after reading.

Answer

Enable auto-compaction in Spark configuration.

Question 8

You are designing a data processing solution for a retail company that uses Azure Databricks. The solution needs to process streaming sales data from Event Hubs and batch data from Azure Data Lake Storage Gen2. You need to ensure that the solution can handle late-arriving data and maintain exactly-once semantics. Which TWO technologies should you use?

Accepted Answer

Delta Lake. Delta Lake is correct because it provides ACID transactions, schema enforcement, and time travel capabilities, which are essential for handling late-arriving data and ensuring exactly-once semantics when combined with Structured Streaming. It allows you to merge late records into existing Delta tables using merge operations (upserts) without corrupting the data state.

Answer

PolyBase

Answer

Azure Stream Analytics

Answer

Azure Data Factory

Question 9

You are reviewing a Spark job definition in Azure Synapse Analytics. The job aggregates sales data. The job runs successfully but takes longer than expected. You notice that dynamic allocation is disabled and the executor instances are fixed at 10. The cluster has a maximum of 20 nodes. What is the most likely reason for the slow performance?

Accepted Answer

The job cannot scale out beyond 10 executors because dynamic allocation is disabled.. With dynamic allocation disabled and executor instances fixed at 10, the Spark job cannot utilize additional cluster resources even though the cluster supports up to 20 nodes. This means the job is artificially constrained to 10 executors, limiting parallelism and causing slower performance despite available compute capacity.

Answer

The file path is incorrect, causing data read errors.

Answer

The job is not parallelized because of a single partition.

Answer

The executor memory is too low for the aggregation.

Question 10

You are building a real-time dashboard that displays sales data from an Azure SQL Database. The dashboard must refresh every 30 seconds with minimal latency. You need to choose the appropriate Azure service for data processing and visualization. Which service should you use?

Accepted Answer

Power BI with DirectQuery mode and configure automatic page refresh.. Power BI with DirectQuery mode is the correct choice because it allows real-time queries directly against Azure SQL Database, supporting automatic page refresh every 30 seconds with minimal latency. Azure Analysis Services requires data processing and is not designed for sub-minute refreshes. Azure Data Explorer is optimized for time-series data, not for direct connection to Azure SQL Database for real-time dashboards. Azure Synapse Serverless SQL pool is meant for querying data lakes, not for real-time visualization with frequent refreshes.

Answer

Azure Analysis Services with a tabular model and a scheduled refresh every 30 seconds.

Answer

Azure Data Explorer (ADX) with a continuous export to Power BI.

Answer

Azure Synapse Serverless SQL pool with Power BI import mode.

Question 11

You are designing a data storage solution for a global e-commerce company. The company needs to store clickstream data from millions of users with high write throughput and low-latency reads for real-time analytics. The data is semi-structured and includes nested JSON objects. Which Azure data store should you recommend?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is the correct choice because it provides a multi-model, globally distributed database service with guaranteed single-digit-millisecond read and write latencies at the 99th percentile, making it ideal for high-throughput clickstream ingestion and real-time analytics. Its native support for semi-structured data and nested JSON objects via the SQL API (or MongoDB API) allows direct storage and querying of complex event payloads without schema flattening. Additionally, Cosmos DB offers automatic indexing and tunable consistency levels to balance performance and data freshness for global e-commerce scenarios.

Answer

Azure Table Storage

Answer

Azure SQL Database

Answer

Azure Blob Storage

Question 12

A data engineering team uses Azure Data Factory to load data from Azure SQL Database to Azure Data Lake Storage Gen2. They notice that the pipeline runs fail intermittently due to transient errors. They need to implement a retry policy with exponential backoff. What is the most efficient way to achieve this?

Accepted Answer

Configure the 'Retry' property on the copy activity with a count and exponential backoff interval. Azure Data Factory natively supports configuring a 'Retry' property on activities, including Copy activities, with an exponential backoff interval. This built-in mechanism automatically retries the activity upon transient failures without requiring custom logic, making it the most efficient and maintainable approach for handling intermittent errors.

Answer

Use a 'Validation' activity before the copy to check source availability

Answer

Create a custom .NET activity to handle retries

Answer

Add a 'Until' loop with a wait activity in the pipeline

Question 13

Which TWO actions help optimize data storage costs in Azure Data Lake Storage Gen2?

Accepted Answer

Configure lifecycle management policies to move data to cool or archive tiers.. Option C is correct because lifecycle management policies automatically transition blobs from hot to cool or archive access tiers (or delete them) based on rules such as last-modified or last-access time, and the archive tier offers the lowest storage cost per GB, directly reducing storage spend. Option E is correct because locally redundant storage (LRS) replicates data three times within a single datacenter in the primary region and is the cheapest redundancy option, making it cost-effective for temporary or non-critical data that does not require regional or geographic durability. Option A is not a cost optimization—soft delete retains deleted blobs and continues to bill for that retained data, potentially increasing cost. Option B is not a cost optimization because GRS replicates data to a secondary region, roughly doubling storage cost compared to LRS. Option D is not a cost optimization because customer-managed keys affect encryption control and key management (e.g., Key Vault costs) rather than reducing storage costs.

Answer

Enable soft delete for blobs.

Answer

Enable geo-redundant storage (GRS) for the storage account.

Answer

Enable encryption at rest using customer-managed keys.

Question 14

You are designing a data lake architecture using Azure Data Lake Storage Gen2. The data will be ingested from multiple sources with varying schemas. You need to organize the data in a way that supports both batch and streaming analytics while maintaining data lineage. Which folder structure convention should you use?

Accepted Answer

Use a medallion architecture with three layers: bronze (raw), silver (cleaned), gold (aggregated).. The medallion architecture (bronze, silver, gold) is the recommended pattern for Azure Data Lake Storage Gen2 when handling multiple sources with varying schemas. It supports both batch and streaming by storing raw data in bronze, applying incremental transformations in silver, and serving aggregated views in gold, while maintaining data lineage through clear layer boundaries and audit columns.

Answer

Organize by ingestion date only, with subfolders for each source.

Answer

Organize by source system, then by date.

Answer

Organize by file format (CSV, Parquet, JSON) and date.

Question 15

Which TWO of the following are supported storage options for use as a source in Azure Synapse Pipeline Copy Activity?

Accepted Answer

Azure Data Lake Storage Gen2. Azure Data Lake Storage Gen2 is a supported source for Azure Synapse Pipeline Copy Activity because it combines a hierarchical file system with Azure Blob Storage APIs, enabling efficient data ingestion. The Copy Activity can read data from ADLS Gen2 using the AzureBlobFS linked service, which supports both file and folder-level reads for structured and unstructured data.

Answer

Azure Analysis Services

Answer

Azure Cognitive Search

Answer

Azure Purview