DP-203 Practice Test 17 — 15 Questions

Question 1

Which TWO techniques can you use to handle schema drift in Azure Data Factory mapping data flows?

Accepted Answer

Enable 'Allow schema drift' in the source transformation. Option A is correct because enabling 'Allow schema drift' on the source transformation in a mapping data flow lets the flow read columns that are not defined in the dataset schema at design time, so newly arriving columns flow through instead of being dropped. Option C is correct because column pattern matching (rule-based mapping) lets you define rules such as name or type patterns so that columns with similar names are automatically mapped without editing the flow for every new column. Option B is not appropriate because manually adding a derived column for each new column defeats the purpose of automated drift handling and requires redeployment for every schema change. Option D is wrong because assertion rules validate row conditions and can fail or reject rows, but they do not adapt the flow to new or renamed columns. Option E is wrong because a fixed schema mapping ignores unknown columns, which is the opposite of handling schema drift.

Answer

Use derived column transformation to handle each new column manually

Answer

Use assertion rules to reject rows with unknown columns

Answer

Use a fixed schema mapping to ignore unknown columns

Question 2

Your company uses Azure Purview for data governance. You need to ensure that sensitive data in Azure Data Lake Storage Gen2 is automatically detected and classified. What should you configure in Purview?

Accepted Answer

Create a scan rule set that includes built-in classification rules for sensitive data types.. In Microsoft Purview, you can create scan rule sets that include built-in classification rules to automatically detect sensitive data types during scanning. Option A is incorrect because sensitivity labels are applied after classification, not for detection. Option B is incorrect because Microsoft Defender for Cloud's data sensitivity discovery is a different feature; Purview itself handles classification. Option C is incorrect because Azure Policy enforces compliance rules, not data classification at the file level.

Answer

Apply sensitivity labels to the storage account using Microsoft Purview Information Protection.

Answer

Enable Microsoft Defender for Cloud's data sensitivity discovery.

Answer

Use Azure Policy to enforce tagging of resources containing sensitive data.

Question 3

A company uses Azure Synapse Analytics dedicated SQL pool for data warehousing. They notice that queries against a large fact table are slow. The table is hash-distributed on ProductID, but many queries filter on OrderDate. What should the data engineer do to improve query performance?

Accepted Answer

Change the distribution to hash on OrderDate. Changing the distribution key to OrderDate aligns the physical data layout with the most common query filter predicate. In a dedicated SQL pool, hash distribution distributes rows across distributions based on the hash of the distribution column. When queries filter on OrderDate, a hash on OrderDate enables partition elimination and distribution-level pruning, reducing data movement and improving scan performance.

Answer

Change the distribution to round-robin

Answer

Replicate the table to all distributions

Answer

Create a columnstore index on OrderDate

Question 4

You are designing a data pipeline to ingest streaming data from IoT devices into Azure Synapse Analytics. The data must be available for querying with minimal latency, but you also need to handle spikes in throughput without data loss. Which service should you use as the ingestion layer?

Accepted Answer

Azure Event Hubs. Azure Event Hubs is the correct choice because it is a fully managed, real-time data ingestion service optimized for high-throughput streaming data from millions of IoT devices. It provides at-least-once delivery, supports partitioning for massive scale, and integrates natively with Azure Synapse Analytics via the Synapse Pipeline or Event Hubs Capture to handle throughput spikes without data loss.

Answer

Azure Data Lake Storage Gen2

Answer

Azure Blob Storage

Answer

Azure IoT Hub

Question 5

You have an Azure Data Factory pipeline that copies data from an FTP server to Azure Blob Storage. The pipeline runs successfully most of the time, but occasionally fails with a 'FTP server connection refused' error during peak hours. You need to minimize these failures with minimal cost. What should you do?

Accepted Answer

Add a retry policy to the copy activity with a backoff interval.. A retry policy with a backoff interval on the copy activity handles transient 'connection refused' errors during peak hours by automatically reattempting after a delay, smoothing over temporary FTP server saturation. It is the lowest-cost, least-invasive fix and directly targets intermittent failures without infrastructure changes.

Answer

Set up Azure ExpressRoute to improve network reliability.

Answer

Migrate the FTP server to SFTP.

Answer

Increase the parallel copy count in the copy activity.

Question 6

You are designing a data processing solution in Azure Data Factory that uses mapping data flows. You need to perform type conversions on incoming data. Which two transformations can be used to change data types? (Choose two.)

Accepted Answer

Derived Column. Options B and E are correct. Derived Column can change data types through expressions, and Select can cast types during column projection. Option C (Assert) is incorrect because it only validates and routes rows based on conditions; it does not convert data types. Option A (Conditional Split) and Option D (Sort) are also incorrect.

Answer

Conditional Split

Answer

Assert

Answer

Sort

Question 7

You are analyzing a Kusto query in Azure Data Explorer that calculates total sales per product for January 2024 and filters for products with sales over 10,000. The query uses the materialize() function. You notice that the query runs slower than expected. What is the primary reason the materialize() function may not be providing the expected performance benefit in this query?

Accepted Answer

The materialize() result is referenced only once in the query, so materialization adds unnecessary overhead. Materialize() only provides performance benefit when the materialized result is referenced multiple times. In this query, the materialized result is used only once, so the overhead of materialization (storing the result in memory) outweighs any benefit, potentially making the query slower. Option A is incorrect because there is no join with a Products table; the query uses a single table. Option B is incorrect because summarize does not inherently materialize results; it computes aggregations on the fly. Option C is incorrect because datetime range filters in Kusto are sargable and do not cause full table scans.

Answer

The join with the Products table forces a shuffle that bypasses the materialized result

Answer

The query uses summarize, which already materializes results internally

Answer

The datetime range filter is not sargable, causing full table scan

Question 8

You need to ensure that data in an Azure Data Lake Storage Gen2 account is encrypted at rest using a customer-managed key. Which feature should you configure?

Accepted Answer

Azure Key Vault integration with Storage Service Encryption. Customer-managed keys (CMK) for Azure Storage are implemented by integrating the storage account with Azure Key Vault (or Managed HSM) and configuring the account's encryption key to reference a key stored there. Storage Service Encryption (SSE) then uses that key to wrap the account's data encryption key, giving the customer control over key lifecycle and revocation.

Answer

Azure Information Protection

Answer

Azure Storage Service Encryption with Microsoft-managed keys

Answer

Azure Disk Encryption

Question 9

Your company uses Azure Blob Storage to store backups. You need to ensure that data is encrypted at rest using a customer-managed key stored in Azure Key Vault. Which feature should you enable?

Accepted Answer

Azure Storage Service Encryption with customer-managed keys. Azure Storage Service Encryption (SSE) encrypts data at rest and supports customer-managed keys stored in Azure Key Vault. Option A is incorrect because Azure Purview is a data governance service, not for encryption. Option B is incorrect because Azure Disk Encryption is used for virtual machine disks, not Blob Storage. Option D is incorrect because Azure Information Protection is for classification and labeling. Therefore, option C is correct.

Answer

Azure Purview

Answer

Azure Disk Encryption

Answer

Azure Information Protection

Question 10

Refer to the exhibit. You have a mapping data flow in Azure Data Factory that aggregates sales data. The data flow runs successfully but the sink table contains only the total sum per run instead of per product. What is missing?

Accepted Answer

The aggregate transformation does not have a groupBy column. In an ADF mapping data flow, the Aggregate transformation computes aggregates based on the columns listed in the Group By tab. If no groupBy column is specified, the transformation treats the entire dataset as a single group, producing one row with the total sum. Adding the product column to groupBy makes the aggregation produce one row per product.

Answer

The source dataset is not filtering by date

Answer

The data flow is missing a filter transformation

Answer

The sink dataset is not configured to append

Question 11

Refer to the exhibit. You are deploying an Azure Synapse Analytics workspace using an ARM template. The template defines a managed virtual network integration runtime. You need to ensure that the integration runtime can run mapping data flows with a time-to-live (TTL) of 10 minutes. What is the purpose of the 'timeToLive' property in this configuration?

Accepted Answer

It defines how long the cluster will be kept alive after a data flow completes, allowing subsequent data flows to reuse the cluster.. The 'timeToLive' property in an Azure Synapse Analytics managed virtual network integration runtime controls how long the cluster remains alive after a mapping data flow completes. By setting a TTL of 10 minutes, subsequent data flows can reuse the same warm cluster, avoiding the 5–10 minute cold start time for new clusters. This optimizes performance and reduces latency for consecutive data flow executions.

Answer

It sets the timeout for the integration runtime to connect to the data sources.

Answer

It specifies the maximum duration a data flow activity can run before timing out.

Answer

It determines the maximum number of concurrent data flows that can run on the cluster.

Question 12

A company uses Azure Synapse Analytics dedicated SQL pool. They notice that some queries are slow due to high data movement. What should you do to minimize data movement for queries that join large fact tables?

Accepted Answer

Hash-distribute the fact tables on the join keys.. Hash-distributing the fact tables on the join keys ensures that rows with the same join key value are placed on the same distribution node. This eliminates the need to shuffle data across nodes during the join, minimizing data movement and improving query performance in Azure Synapse dedicated SQL pool.

Answer

Use round-robin distribution for all tables.

Answer

Partition both tables on the join keys.

Answer

Use replicated tables for all large fact tables.

Question 13

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 14

Your organization uses Azure Data Lake Storage Gen2 with hierarchical namespace enabled. You need to implement a security strategy that allows users to read only specific folders within a container. Which authorization method should you use?

Accepted Answer

Access control lists (ACLs) on the folder. ACLs (Access Control Lists) in Azure Data Lake Storage Gen2 can be applied to individual folders, enabling granular read permissions. Option A is incorrect because a storage account shared key grants full access to the entire account. Option B is incorrect because Azure RBAC roles like Storage Blob Data Contributor apply at the container level, affecting all folders within. Option C is incorrect because shared access signatures (SAS) can be scoped to a container or a file, but not to a specific folder within a container.

Answer

Storage account shared key

Answer

Azure RBAC roles (e.g., Storage Blob Data Contributor) at the container level

Answer

Shared access signatures (SAS) with folder-level permissions

Question 15

Your organization uses Azure Purview for data governance. You need to ensure that sensitive data is properly classified and that access to it is monitored. Which THREE actions should you take? (Choose three.)

Accepted Answer

Create custom sensitivity labels in Microsoft Purview Information Protection and apply them to data sources.. Option C is correct because Microsoft Purview Information Protection sensitivity labels (created in the compliance portal) are the mechanism for tagging data with sensitivity classifications, and Purview can extend these labels to data sources such as Azure SQL, Storage, and Synapse for consistent classification. Option D is correct because integrating Azure Purview with Microsoft Defender for Cloud Apps enables monitoring of user access and activities on sensitive data that Purview has classified, providing anomaly detection and governance over access. Option E is correct because Azure Purview's automated scanning uses built-in and custom classification rules to discover and classify sensitive data (for example, credit card or national ID patterns) across registered sources, which is the core way to ensure data is properly classified. Option A is not correct because Azure Policy initiatives enforce configuration and compliance states on resources; they do not perform data classification of the contents within storage accounts. Option B is not correct because Azure Sentinel is a SIEM/SOAR solution for security analytics and threat detection, not a data classification engine for ingested data.

Answer

Define Azure Policy initiatives to enforce classification on all storage accounts.

Answer

Use Azure Sentinel to classify data as it is ingested.