DP-203 Practice Test 15 — 15 Questions

Question 1

Which of the following are valid activities in an Azure Data Factory pipeline? (Choose three.)

Accepted Answer

Copy. Option A, Copy, is a valid Azure Data Factory pipeline activity that moves data from a source dataset to a sink dataset, supporting dozens of connectors and optional staging, so it is correct. Option B, Databricks Notebook, is a valid ADF activity that runs an Azure Databricks notebook against a linked Databricks workspace, passing parameters and returning output, so it is correct. Option D, Execute Pipeline, is a valid ADF activity that invokes another pipeline as a child pipeline, enabling modular and reusable orchestration, so it is correct. Option C, Assign, is not an ADF pipeline activity; variable assignment in ADF is done with the Set Variable activity (and Append Variable), while Assign is a control-flow activity in Azure Logic Apps, so it does not belong here.

Answer

Assign

Question 2

Refer to the exhibit. You are reviewing a Data Factory JSON definition. The factory has a user-assigned managed identity configured. However, the linked service to Azure Storage uses an account key. What security improvement should you recommend?

Accepted Answer

Modify the linked service to use the managed identity for authentication. Since the Data Factory already has a user-assigned managed identity, the most secure improvement is to reconfigure the Azure Storage linked service to authenticate via that managed identity instead of an account key. Managed identities eliminate the need to store and rotate secrets, and Azure RBAC can grant the identity the minimum required role (e.g., Storage Blob Data Contributor) on the storage account. This is Microsoft's recommended authentication pattern for ADF linked services.

Answer

Add a firewall rule to limit access to the storage account

Answer

Remove the managed identity and use a service principal

Answer

Keep the account key but store it in Azure Key Vault

Question 3

An organization is using Azure Synapse Analytics and wants to implement column-level security to restrict access to sensitive columns. Which feature should they use?

Accepted Answer

Column-level security using GRANT. Column-level security in Azure Synapse Analytics is implemented using GRANT statements on specific columns, restricting access to sensitive columns. Option A is incorrect because dynamic data masking obfuscates data at query time but does not prevent access. Option B is incorrect because Azure Purview is a data governance service, not for access control. Option D is incorrect because row-level security filters rows, not columns.

Answer

Dynamic data masking

Answer

Azure Purview

Answer

Row-level security

Question 4

Which THREE considerations are important when designing a table distribution strategy for an Azure Synapse Analytics dedicated SQL pool? (Choose three.)

Accepted Answer

Align distribution keys on tables that are frequently joined together. Option A is correct because co-locating the distribution key on tables that are frequently joined ensures matching rows land on the same distribution, letting joins execute locally and eliminating costly shuffle (data movement) operations. Option B is correct because a distribution key with many unique values spreads rows evenly across the 60 distributions, minimizing data skew that would otherwise create hot distributions and slow query performance. Option E is correct because table size and join frequency are the core drivers of the distribution decision: small tables may be replicated, while large frequently-joined tables should be hash-distributed on a shared key. Option C is not correct because round-robin is generally recommended for staging or temporary tables, not large fact tables, which benefit from hash distribution on a join/filter column. Option D is not correct because replicated tables are intended for small dimension tables (roughly under 2 GB compressed); replicating large tables consumes excessive storage and rebuild time on every write.

Answer

Use round-robin distribution for large fact tables to distribute data evenly

Answer

Use replicated tables for large tables to avoid data movement

Question 5

Your organization uses Azure Data Lake Storage Gen2 and needs to prevent accidental deletion of data by enabling soft delete. You also need to ensure that deleted blobs are recoverable for 30 days. What should you configure?

Accepted Answer

Enable blob soft delete and set retention period to 30 days.. Blob soft delete enables recovery of deleted blobs within a specified retention period (30 days in this case). Option A is incorrect because blob snapshots are point-in-time copies that require manual management and do not provide automatic recovery of deleted blobs. Option B is incorrect because Azure Backup is designed for virtual machines and other Azure resources, not for blob-level recovery in Data Lake Storage Gen2. Option C is incorrect because container soft delete deletes entire containers, not individual blobs.

Answer

Enable blob snapshots and set them to expire after 30 days.

Answer

Use Azure Backup to create daily backups of the storage account.

Answer

Enable container soft delete with a retention period of 30 days.

Question 6

Drag and drop the steps to convert data from CSV to Parquet format using Azure Data Factory into the correct order.

Accepted Answer

Define source CSV dataset, then define sink Parquet dataset, then create copy activity with mapping, then run and monitor.. Define source (CSV) and sink (Parquet) datasets, then a copy activity with mapping, run, and monitor.

Answer

Define sink Parquet dataset, then define source CSV dataset, then create copy activity with mapping, then run and monitor.

Answer

Define source CSV dataset, then create copy activity with mapping, then define sink Parquet dataset, then run and monitor.

Answer

Create copy activity with mapping, then define source CSV dataset, then define sink Parquet dataset, then run and monitor.

Question 7

Your team uses Azure Synapse Analytics serverless SQL pool to query Parquet files in Azure Data Lake Storage Gen2. The query performance is inconsistent, and some queries take a long time to execute. You need to improve query performance. What should you do?

Accepted Answer

Create statistics on the columns used in joins and filters. (Create statistics on the columns used in joins and filters) is correct because serverless SQL pool relies on statistics for optimal query plans. Option A (Increase the maximum degree of parallelism) is not directly applicable. Option C (Convert to CSV) would degrade performance. Option D (Use a dedicated SQL pool) may be an option but not the best immediate step.

Answer

Increase the MAXDOP setting in the query

Answer

Move the data to a dedicated SQL pool

Answer

Convert the Parquet files to CSV format

Question 8

You are optimizing the performance of a large-scale batch processing job in Azure Databricks. The job reads data from Azure Data Lake Storage Gen2, performs transformations, and writes results back. You notice that the job is I/O bound. Which THREE strategies can improve performance? (Choose three.)

Accepted Answer

Use Delta Lake format and optimize the table with Z-ordering on frequently filtered columns.. Option A is correct because Delta Lake with Z-ordering co-locates related data in the same set of files, enabling data-skipping so the I/O-bound job reads far fewer bytes when filtering on those columns. Option B is correct because increasing DataFrame partitions spreads the read/transform workload across more concurrent tasks, raising parallelism and better saturating the storage throughput available to the cluster. Option C is correct because caching the DataFrame in memory (or Delta cache) prevents repeated reads from ADLS Gen2, directly cutting the disk I/O that is the bottleneck. Option D is not correct because reducing shuffle partitions lowers parallelism for wide transformations and typically increases per-task data volume, which does not relieve I/O-bound reads. Option E is not correct because autoscaling adds compute nodes but does not by itself reduce the I/O volume or improve data layout, so it is not a targeted fix for an I/O-bound workload.

Answer

Reduce the number of shuffle partitions to minimize data movement.

Answer

Enable autoscaling on the cluster to add more nodes during processing.

Question 9

You have an Azure Synapse Analytics dedicated SQL pool with a table that uses hash distribution on CustomerID. You notice that queries joining this table with another table on OrderDate are slow. What is the most likely cause?

Accepted Answer

The join columns are not aligned; data must be shuffled across distributions. D is correct because in Azure Synapse Analytics dedicated SQL pools, hash distribution distributes rows across distributions based on a hash of the distribution key (CustomerID). When joining on OrderDate, which is not the distribution key, the join columns are not aligned across distributions. This forces data movement (shuffling) where rows from one or both tables must be redistributed to match the join key, causing significant performance degradation.

Answer

The table is not partitioned by OrderDate

Answer

Statistics on the join columns are outdated

Answer

The table should use round-robin distribution instead

Question 10

A data engineer is setting up Azure Data Lake Storage Gen2 for a new project. The security requirement is to prevent direct access to the storage account from the internet while allowing access from a specific virtual network. Which network security feature should be enabled?

Accepted Answer

Firewall and virtual network service endpoints. Firewall and virtual network service endpoints allow you to restrict access to Azure Data Lake Storage Gen2 to only traffic originating from a specific virtual network, effectively blocking all internet-based access. This is achieved by configuring a service endpoint on the subnet and a firewall rule on the storage account that denies all traffic except that from the designated virtual network.

Answer

Azure Private Endpoint

Answer

Shared access signature (SAS)

Answer

Azure Defender for Storage

Question 11

A company uses Azure Data Lake Storage Gen2 with hierarchical namespace enabled. They need to restrict a specific application's access to only write files in a particular directory without being able to read or list files. Which type of permission should be assigned?

Accepted Answer

Configure an access control list (ACL) that grants execute and write permissions to the application's service principal.. In ADLS Gen2 with hierarchical namespace, POSIX-style ACLs are the granular permission mechanism. To allow an application to write files in a directory without reading or listing, you grant the service principal 'execute' (traverse) and 'write' permissions on the directory. Execute allows traversal through the directory, and write allows creating files within it; without read, the principal cannot list directory contents. This is the least-privilege approach for the stated requirement.

Answer

Configure a firewall rule to allow only the application's IP address.

Answer

Generate a shared access signature (SAS) with write and list permissions.

Answer

Assign the Storage Blob Data Contributor role at the storage account level.

Question 12

Which THREE metrics should you monitor for an Azure Synapse Analytics dedicated SQL pool to ensure optimal performance?

Accepted Answer

tempdb usage. Monitoring tempdb usage (A) is essential because dedicated SQL pool queries spill to tempdb for sorts, hash joins, and other operations, and high tempdb utilization can indicate insufficient memory or poorly designed queries that degrade performance. DWU usage (B) is a key metric because it reflects how much of the provisioned Data Warehouse Unit capacity is being consumed; consistently high DWU usage signals that the pool may need scaling or workload tuning. Queued queries (C) should be monitored because a growing queue indicates that concurrency limits or resource contention are delaying query execution, directly affecting performance. The unmarked options do not belong: login failures (D) is a security/connectivity metric rather than a performance indicator, and total storage size (E) relates to capacity planning and cost, not to query performance optimization.

Answer

Login failures

Answer

Total storage size

Question 13

You are using Azure Synapse Analytics dedicated SQL pool to run a query that joins a large fact table (10 billion rows) and a small dimension table (1 million rows). The query is slow. Which distribution strategy should you use for the dimension table to improve performance?

Accepted Answer

Replicate the dimension table to all compute nodes.. Replicating the small dimension table (1 million rows) to all compute nodes eliminates data movement during the join with the large fact table (10 billion rows). In Azure Synapse dedicated SQL pool, replicated tables store a full copy on each distribution, so the join can be performed locally on every node without shuffling data across the network, drastically reducing query latency.

Answer

Round-robin distribute the dimension table.

Answer

Hash-distribute the dimension table on its primary key.

Answer

Hash-distribute the dimension table on the foreign key column.

Question 14

Which THREE components are valid parts of the Microsoft Purview Data Map? (Choose THREE)

Accepted Answer

Scan rule sets. In Microsoft Purview Data Map, scan rule sets (A) are valid components because they define which file types and system types are scanned and which classification rules are applied during a scan. Data sources (D) are valid because the Data Map registers and stores metadata about sources such as Azure SQL Database, SQL Server, and Microsoft Fabric, which are then scanned and cataloged. Classifications (E) are valid because they are the built-in or custom classification definitions (for example, credit card number or passport number) that scans use to tag assets in the Data Map. Sensitivity labels (B) are not a Data Map component; they belong to Microsoft Purview Information Protection and are applied to content, not to the Data Map's scanning/cataloging structure. Data flows (C) are not a Data Map component either; data flows are associated with Azure Data Factory/Synapse pipelines rather than the Purview Data Map's scanning and metadata model.

Answer

Sensitivity labels

Answer

Data flows

Question 15

Which THREE statements are true about partitioning in Azure Synapse Analytics dedicated SQL pool?

Accepted Answer

Partition switching can be used to quickly load data into a table.. Option A is correct because partition switching (ALTER TABLE ... SWITCH PARTITION) is a metadata-only operation that instantly moves a fully prepared staging table's partition into the target table, making it a fast way to load data. Option C is correct because in a clustered columnstore index each partition is stored as its own set of rowgroups, so partition boundaries also define rowgroup boundaries. Option E is correct because creating too many partitions (especially small ones) increases metadata overhead, causes rowgroup fragmentation, and degrades query performance due to reduced segment elimination efficiency. Option B is not correct because partitions and distributions are independent constructs; partitioning does not automatically align with the 60 distributions, and alignment must be managed explicitly. Option D is not correct because partitioning is supported on clustered columnstore, clustered rowstore, and heap tables in dedicated SQL pools, not only on clustered rowstore indexes.

Answer

Partitions are automatically aligned with distributions.

Answer

Partitioning is only supported on tables with clustered rowstore indexes.