DP-203 Practice Test 5 — 25 Questions

Question 1

Refer to the exhibit. A Bicep file is used to deploy an Azure Synapse Analytics workspace. What is the purpose of the 'purviewConfiguration' property?

Accepted Answer

It links the workspace to a Microsoft Purview account for data lineage and cataloging. The 'purviewConfiguration' property in a Bicep file for Azure Synapse Analytics links the workspace to a Microsoft Purview account. This integration enables automated data lineage tracking, cataloging, and discovery across the Synapse environment, allowing users to search for and govern data assets directly from Purview. Without this property, the Synapse workspace operates independently of Purview's unified data governance capabilities.

Answer

It configures automated backups of the Synapse workspace

Answer

It enables monitoring of data movement by Azure Monitor

Answer

It connects the workspace to a data catalog for pipeline sources

Question 2

Your organization uses Azure Data Lake Storage Gen2 with hierarchical namespace enabled. You need to implement a monitoring strategy to detect and alert on unusual access patterns that could indicate a security breach. Which THREE services or features should you use? (Choose three.)

Accepted Answer

Enable Microsoft Defender for Storage to get security alerts about unusual access patterns.. Option A is correct because Microsoft Defender for Storage analyzes data-plane telemetry on the storage account and raises security alerts for suspicious activity such as unusual access patterns, anomalous data exfiltration, or access from unusual locations, which directly addresses breach detection. Option C is correct because Microsoft Sentinel can ingest storage logs and use analytics rules (including built-in anomaly and threat-detection templates) to correlate events and alert on anomalous access patterns across the environment. Option D is correct because enabling diagnostic settings on the storage account is the prerequisite that exports read, write, and delete data-plane logs (the StorageRead/StorageWrite/StorageDelete categories) to a Log Analytics workspace, Event Hub, or storage account, providing the raw telemetry that Sentinel and Defender for Storage rely on for anomaly detection. Option B does not belong because Azure Policy enforces configuration and compliance (for example, requiring encryption or HTTPS), but it does not detect or alert on unusual access patterns. Option E does not belong because Azure Monitor Metrics provides aggregate numeric time-series data such as transaction counts, latency, and availability, not per-request access detail needed to identify anomalous access patterns.

Answer

Apply Azure Policy to enforce encryption and access policies.

Answer

Use Azure Monitor Metrics to track storage account transactions and latency.

Question 3

You are securing an Azure Data Lake Storage Gen2 account that contains sensitive data. Which TWO of the following should you implement to protect data from unauthorized access?

Accepted Answer

Configure ACLs to grant least privilege to users and groups. Option A is correct because Azure Data Lake Storage Gen2 uses POSIX-style access control lists (ACLs) at both the directory and file level, and configuring ACLs to grant least privilege ensures users and groups only receive the specific read/write/execute permissions they require, directly preventing unauthorized access to sensitive data. Option B is correct because private endpoints assign a private IP address from your virtual network to the storage account, removing exposure to the public internet and restricting access to only clients within the approved virtual network or connected networks. Option C is incorrect because setting a default ACL that allows read access to all authenticated users violates least privilege and would broaden, not restrict, access to sensitive data. Option D is incorrect because CORS rules only control which web origins can make cross-origin browser requests to the service; they do not authenticate users or prevent unauthorized direct access. Option E is incorrect because enabling large file shares only increases the maximum capacity and file size limits of the file share, and has no effect on access control or authorization.

Answer

Set the default ACL to allow read access for all authenticated users

Answer

Enable CORS rules to allow only specific origins

Answer

Enable large file shares on the storage account

Question 4

You need to transform data in Azure Synapse Analytics using a language that supports procedural logic and error handling. Which option should you use?

Accepted Answer

T-SQL stored procedures. T-SQL stored procedures are the correct choice because they support procedural logic (e.g., IF/ELSE, loops, TRY/CATCH) and error handling within Azure Synapse Analytics dedicated SQL pools. This allows you to encapsulate complex data transformation logic, handle runtime errors gracefully, and manage transactions, which is not possible with declarative objects like views or external tables.

Answer

CREATE VIEW

Answer

PolyBase

Answer

CREATE EXTERNAL TABLE

Question 5

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. In Azure Purview, to automatically scan and classify sensitive data, you create a scan rule set that includes classification rules for the specific sensitive data types (e.g., credit card numbers, SSNs). The scan rule set is applied to the scan of the Azure Data Lake Storage Gen2 account, and Purview uses built-in or custom classification rules to identify and tag sensitive data.

Answer

Azure Information Protection (AIP) scanner

Answer

Microsoft Defender for Cloud

Answer

Azure Policy with built-in guest configuration

Question 6

Your team has deployed an Azure Stream Analytics job that writes output to Azure Cosmos DB. You need to monitor the job for data latency and ensure it meets a service-level agreement (SLA) of under 10 seconds from input to output. Which metric should you track in Azure Monitor?

Accepted Answer

Watermark delay.. Watermark delay is the correct metric to monitor for data latency because it measures the maximum time between an input event being received and the corresponding output being produced. A watermark delay consistently under 10 seconds ensures the SLA is met. Output events (A) track the number of output events, not latency. Runtime errors (B) indicate failures, not latency. Input events (D) track the number of input events, not latency.

Answer

Output events.

Answer

Runtime errors.

Answer

Input events.

Question 7

A data engineer needs to store semi-structured JSON logs for analysis using Azure Synapse Serverless SQL. Which file format should be used for optimal query performance?

Accepted Answer

Parquet. Parquet is correct because it is a columnar storage format that enables predicate pushdown and compression, significantly reducing the amount of data scanned by Azure Synapse Serverless SQL for analytical queries on semi-structured JSON logs. This format aligns with the engine's design for high-performance read operations on large datasets, unlike row-oriented formats that require full file scans.

Answer

Avro

Answer

CSV

Answer

JSON

Question 8

Refer to the exhibit. You submit a Spark job in Azure Synapse Analytics using the Azure CLI. The job runs slowly during the shuffle phase. The input data is about 200 GB. Which configuration change would best improve performance for this shuffle-heavy workload?

Accepted Answer

Increase spark.sql.shuffle.partitions to 800.. Spark's shuffle phase is governed by spark.sql.shuffle.partitions, which defaults to 200. With 200 GB of input, 200 partitions means roughly 1 GB per partition, causing large shuffle blocks, disk spills, and long task runtimes. Raising it to 800 creates smaller, more parallel partitions that better utilize the cluster's cores and reduce per-task memory pressure, which is the standard tuning lever for shuffle-heavy jobs.

Answer

Increase the number of executors to 4.

Answer

Change executor size to 'Large' to increase memory per executor.

Answer

Decrease spark.sql.shuffle.partitions to 200 to reduce overhead.

Question 9

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 10

You are reviewing an Azure PowerShell script that sets permissions on a directory in Azure Data Lake Storage Gen2. The script sets a default ACL for a user on the path 'sales/2024/01/'. What is the effect of the -DefaultScope parameter?

Accepted Answer

The ACL is inherited by all new child items created under this directory.. The -DefaultScope parameter in Azure Data Lake Storage Gen2 sets a default ACL entry. Default ACLs do not set permissions on the current directory; instead, they define permissions that are inherited by new child items (files and subdirectories) created under that directory. Therefore, option B is correct. Option A is incorrect because default ACLs do not replace the access ACL; access ACLs are set separately without -DefaultScope. Option C is incorrect because default ACLs do not apply to existing items; they only affect future items. Option D is incorrect because default ACLs apply to both new files and new subdirectories.

Answer

The ACL replaces the existing access ACL on the directory.

Answer

The ACL is applied to all existing files and subdirectories recursively.

Answer

The ACL is applied only to files, not subdirectories.

Question 11

You are designing a data processing solution in Azure using Azure Data Lake Storage Gen2 as the storage layer. You need to ensure that data ingested from various sources is immutable and can be used for both batch and streaming workloads. Which storage design pattern should you implement?

Accepted Answer

Implement a medallion architecture with bronze, silver, and gold layers.. The medallion architecture (bronze, silver, gold) is the correct pattern because it enforces immutability at the bronze layer (raw ingested data is never modified), while providing progressively refined, query-optimized views for both batch and streaming workloads in Azure Data Lake Storage Gen2. This design supports schema-on-read, enables reprocessing from raw data, and aligns with lakehouse principles for unified analytics.

Answer

Store data in a normalized relational database structure.

Answer

Use a data vault model with hubs, links, and satellites.

Answer

Design a star schema with fact and dimension tables.

Question 12

Your company uses Azure Data Factory to orchestrate data movement. You need to monitor pipeline runs across multiple factories and create a dashboard that shows success and failure rates over the past 30 days. What is the most efficient approach?

Accepted Answer

Configure diagnostic settings for each Data Factory to send logs to a Log Analytics workspace, then create a workbook using KQL queries.. Diagnostic settings in Azure Data Factory can route ActivityRuns, PipelineRuns, TriggerRuns, and other logs to a Log Analytics workspace. Once logs from multiple factories land in the same workspace, KQL queries in an Azure Monitor workbook can aggregate success/failure counts across all factories over a 30-day window, giving a single consolidated dashboard. This is the most efficient, scalable approach for multi-factory monitoring.

Answer

Use the Data Factory monitoring UI to view runs for each factory individually.

Answer

Enable Azure Storage Analytics and query the logs stored in a storage account.

Answer

Create alert rules in Azure Monitor for each pipeline failure and aggregate manually.

Question 13

You are migrating a large on-premises SQL Server database to Azure Synapse Analytics. The database includes tables with up to 500 million rows and frequent updates. You need to minimize data movement during the migration while ensuring optimal query performance in the dedicated SQL pool. Which table design strategy should you use?

Accepted Answer

Use round-robin tables for staging tables and hash-distributed tables for large fact tables on a key column.. It uses round-robin tables for staging to minimize data movement during the initial load, then hash-distributes large fact tables on a key column to optimize query performance by collocating rows with the same distribution key on the same compute node. This balances the need for fast ingestion with efficient parallel query execution in Azure Synapse Analytics dedicated SQL pools.

Answer

Use hash-distributed tables for all tables and clustered columnstore indexes.

Answer

Use replicated tables for all fact tables and hash-distributed tables for dimension tables.

Answer

Use round-robin tables for all tables to simplify the migration.

Question 14

Your organization uses Azure Data Lake Storage Gen2 (ADLS Gen2) and wants to transform data using Azure Databricks. The data is stored in Parquet format. You need to read the data into a Spark DataFrame. Which DataFrame reader method should you use?

Accepted Answer

spark.read.parquet(). The data is stored in Parquet format, and the Spark DataFrame reader method `spark.read.parquet()` is specifically designed to read Parquet files, which is a columnar storage format optimized for big data processing in Azure Databricks.

Answer

spark.read.avro()

Answer

spark.read.json()

Answer

spark.read.csv()

Question 15

You are running a Python script in Azure Databricks that reads a CSV file from DBFS. The script runs successfully in an interactive notebook but fails when executed as a job with the error: 'Path does not exist: dbfs:/tmp/data.csv'. What is the most likely cause?

Accepted Answer

The file was uploaded to the workspace filesystem, not to DBFS.. The most likely cause is that the file was uploaded to the workspace filesystem (Workspace), not to DBFS. In interactive notebooks, the workspace filesystem is accessible via a symlink that makes it appear under `dbfs:/`, but when running as a job, the path `dbfs:/tmp/data.csv` does not resolve to workspace files. The file must be in the DBFS root or mounted location to be accessible via that path. Option D is correct. Option A is incorrect because both interactive and job clusters support Python. Option B is incorrect because file size is not indicated as an issue. Option C is incorrect because the job cluster automatically has permission to access DBFS.

Answer

The job is using a different runtime that does not support Python.

Answer

The file is too large for DBFS.

Answer

The job cluster does not have permission to access DBFS.

Question 16

You are designing a streaming data solution for IoT devices that generate 10,000 events per second. The data must be processed with sub-second latency and then stored in Azure Data Lake Storage Gen2 for archival. Which Azure service should you use for the stream processing?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is the correct choice because it is purpose-built for real-time stream processing with sub-second latency, and it natively integrates with Azure Data Lake Storage Gen2 for output. It can handle 10,000 events per second using its streaming unit scaling, and its SQL-like query language allows for low-latency transformations without the overhead of cluster management.

Answer

Azure HDInsight including Spark Structured Streaming

Answer

Azure Data Factory

Answer

Azure Event Hubs

Question 17

A manufacturing company uses Azure Data Lake Storage Gen2 to store IoT sensor data. The data arrives in JSON format with a nested structure. You need to transform the data into a tabular format for downstream analytics using Azure Synapse Pipelines. Which data flow transformation should you use?

Accepted Answer

Flatten transformation. The Flatten transformation in mapping data flows unpacks nested arrays into rows. Option A is wrong because the Aggregate transformation groups data but does not flatten nested structures. Option C is wrong because the Window transformation calculates aggregated values over a range of rows. Option D is wrong because the Pivot transformation rotates rows to columns.

Answer

Aggregate transformation

Answer

Window transformation

Answer

Pivot transformation

Question 18

Which TWO options are valid methods to load data from on-premises SQL Server into Azure Synapse Analytics?

Accepted Answer

SQL Server Integration Services (SSIS) package. Both SQL Server Integration Services (SSIS) and Azure Data Factory are fully capable of loading data from on-premises SQL Server into Azure Synapse Analytics. SSIS provides a mature, high-performance ETL tool that can directly target Synapse using the SQL Server Destination or the Azure Synapse Analytics Destination. Azure Data Factory, with its self-hosted integration runtime and incremental copy feature, offers a modern, cloud-native orchestration solution for scheduled and reliable data ingestion. PolyBase from external table, while useful for querying external data, is not primarily a data loading method; it requires additional steps to persist the data into Synapse tables and does not directly load from on-premises SQL Server without external staging.

Answer

PolyBase from external table

Question 19

You need to process a large dataset that contains personally identifiable information (PII). The data must be anonymized before being used for analytics. Which Azure service should you use to apply column-level masking dynamically?

Accepted Answer

Azure Synapse Analytics dynamic data masking. Azure Synapse Analytics provides dynamic data masking at the column level. Azure Purview is for data governance. Azure API Management is for APIs. Azure Data Lake Storage does not provide masking.

Answer

Azure API Management policies

Answer

Azure Data Lake Storage access control lists (ACLs)

Answer

Azure Purview classification and labeling

Question 20

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. You need to choose the most appropriate service for near real-time ingestion with minimal latency. What should you use?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is the correct choice because it is purpose-built for near real-time stream processing with sub-second latency, directly integrates with Azure Event Hubs as an input source and dedicated SQL pool as an output sink, and provides a SQL-like query language for defining transformations. This minimizes architectural complexity and latency compared to other services.

Answer

Azure Databricks with Structured Streaming

Answer

Azure Data Factory

Answer

Azure Functions with Event Hub trigger