DP-203 Multiple Select — 60 Questions

Question 1

You are designing a data processing solution in Azure that must handle both batch and streaming data. The solution should use a common storage layer for both and support schema evolution. Which TWO technologies should you recommend?

Accepted Answer

Azure Event Hubs. Azure Event Hubs is correct because it is a fully managed, real-time data ingestion service that can capture streaming data and store it in Azure Data Lake Storage Gen2 or Blob Storage, enabling a unified storage layer for both batch and streaming pipelines. It supports schema evolution through Avro or JSON serialization, allowing downstream consumers to adapt to schema changes without breaking existing processes.

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Azure SQL Database

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Apache Kafka on HDInsight

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Azure Data Lake Storage Gen2

Question 2

Which TWO actions are appropriate when designing a data processing solution that must meet strict SLAs for latency and throughput?

Accepted Answer

Partition data by date and hour to improve query performance. Partitioning data by date and hour (Option A) is appropriate because it enables partition elimination, where queries only scan relevant partitions rather than the entire dataset. This directly reduces latency and improves throughput by minimizing I/O and compute resources needed for time-range queries, which is critical for meeting strict SLAs in data processing solutions.

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Process all data synchronously to ensure consistency

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Use a single large cluster for all workloads to simplify management

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Use a single node for orchestration to reduce complexity

Question 3

Which THREE factors should be considered when choosing between Azure Stream Analytics and Azure Databricks for a real-time data processing solution?

Accepted Answer

Need for complex transformations and machine learning model integration. Option B is correct because Azure Databricks provides native support for complex transformations (e.g., windowed aggregations, multi-step ETL) and seamless integration with machine learning libraries (e.g., MLflow, Spark MLlib), which are not natively available in Azure Stream Analytics. Stream Analytics uses a SQL-like query language and is optimized for simpler, declarative transformations, making Databricks the better choice when advanced analytics or ML model scoring is required in real-time pipelines.

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Integration with Power BI for real-time dashboards

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Maximum allowed latency for late-arriving data

Question 4

Which TWO of the following are valid methods to secure data at rest in Azure Data Lake Storage Gen2?

Accepted Answer

Use customer-managed keys in Azure Key Vault. Option C is correct because using customer-managed keys (CMK) in Azure Key Vault allows you to control and rotate the encryption keys used for Azure Storage Service Encryption (SSE), providing an additional layer of security for data at rest. This is a valid method to secure data at rest in Azure Data Lake Storage Gen2, as it ensures that only authorized parties with access to the key vault can decrypt the data.

Answer

Assign RBAC roles for data access

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Configure storage firewall rules

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Enable TLS 1.2 for all connections

Question 5

Which THREE of the following are required to implement column-level security in Azure Synapse Analytics dedicated SQL pool?

Accepted Answer

A GRANT statement on specific columns to users or roles. Option A is correct because column-level security in Azure Synapse Analytics dedicated SQL pool is implemented using GRANT statements on specific columns. By granting SELECT on only certain columns to a user or role, you restrict access to sensitive data at the column level without needing to create views or modify schemas. This is the native mechanism provided by SQL Server and Azure Synapse for column-level security.

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A VIEW that selects only the allowed columns

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A DENY statement on specific columns to users or roles

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A row-level security policy must be in place

Question 6

Which THREE factors should you consider when choosing between rowstore and columnstore indexes in Azure Synapse Analytics?

Accepted Answer

The table size is expected to be over 1 TB.. Option C is correct because columnstore indexes in Azure Synapse Analytics are optimized for large-scale data warehousing workloads, where table sizes exceeding 1 TB benefit from high compression and columnar storage, significantly improving scan and aggregation performance. Rowstore indexes, in contrast, are less efficient for such large datasets due to higher I/O and storage overhead.

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The table contains many NULL values in indexed columns.

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The table will be partitioned frequently.

Question 7

Which THREE components are part of a defense-in-depth strategy for data security in Azure?

Accepted Answer

Network security groups (NSGs) on subnets. Network security groups (NSGs) are a fundamental component of a defense-in-depth strategy because they provide network-layer segmentation and filtering. By applying NSGs to subnets, you can control inbound and outbound traffic based on source/destination IP addresses, ports, and protocols, creating a perimeter defense that limits lateral movement in case of a breach.

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Azure Policy to enforce tagging

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Dynamic data masking for all databases

Question 8

A company uses Azure Synapse Analytics dedicated SQL pool for a data warehouse. They notice that some queries are using more memory than expected, causing resource contention. Which TWO actions should they take to diagnose and optimize memory usage?

Accepted Answer

Increase the resource class for the users running the heavy queries.. Option B is correct because increasing the resource class for users running heavy queries allocates more memory to those queries, reducing resource contention by ensuring they have sufficient memory to execute efficiently. Option D is correct because querying sys.dm_pdw_exec_requests DMV allows you to identify queries with high memory grants, which is the first step in diagnosing which queries are consuming excessive memory and need optimization.

Answer

Enable result-set caching.

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Scale up the DWU setting.

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Rebuild clustered columnstore indexes.

Question 9

Which TWO actions should you take to ensure that only authorized users can access sensitive data in an Azure Synapse Analytics dedicated SQL pool?

Accepted Answer

Implement row-level security. Row-level security (RLS) and column-level security are the two correct actions because they directly restrict data access at the row and column granularity within a dedicated SQL pool. RLS uses security predicates to filter which rows a user can query, while column-level security denies access to specific columns for unauthorized principals. Both are native features of Azure Synapse dedicated SQL pools that enforce authorization on the data plane.

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Configure Azure Active Directory authentication

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Enable dynamic data masking on all columns

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Enable transparent data encryption

Question 10

A data engineer is optimizing an Azure Data Lake Storage Gen2 account used for big data analytics. The account contains billions of small files (under 1 MB). The analytics jobs are slow and cost more than expected. Which THREE actions should the engineer take to improve performance and reduce costs?

Accepted Answer

Convert data to columnar file formats such as Parquet.. Option A is correct because converting data to columnar formats like Parquet reduces the amount of data read during analytics queries, as only the necessary columns are scanned. This significantly improves query performance and lowers I/O costs, especially for big data workloads on Azure Data Lake Storage Gen2.

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Move data to the cool tier to reduce storage costs.

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Enable soft delete to protect against accidental deletion.

Question 11

You are monitoring an Azure Data Lake Storage Gen2 account that stores streaming data from IoT devices. You notice that query performance on the data in Parquet format is degrading over time. You need to improve query performance for both current and future data. Which TWO actions should you take?

Accepted Answer

Partition the data by a column commonly used in filter conditions.. Partitioning the data by a column commonly used in filter conditions (e.g., date, device ID) enables predicate pushdown in query engines like Azure Synapse or Spark, allowing them to skip irrelevant partitions and scan only the necessary files. This directly addresses the performance degradation by reducing the amount of data read during queries, and it benefits both current and future data when applied consistently.

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Move frequently accessed data to Azure SQL Database.

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Enable soft delete on the storage account to optimize read performance.

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Migrate the data to Azure NetApp Files for lower latency.

Question 12

A data engineering team is designing a batch processing solution using Azure Databricks. The data is stored in Azure Data Lake Storage Gen2 (ADLS Gen2) and must be processed daily with minimal cost. The team needs to choose between using a Delta Lake table or a Parquet file format for the processed output. Which TWO factors should the team consider when making this decision?

Accepted Answer

Delta Lake provides time travel capabilities for accessing historical data versions.. Option A is correct because Delta Lake's time travel feature allows querying previous versions of data using a timestamp or version number, which is essential for auditing, rollback, and reproducing historical reports. This capability is built on the transaction log that tracks every change, making it a key differentiator from plain Parquet files.

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Parquet is easier to implement for schema evolution than Delta Lake.

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Delta Lake reduces storage costs by automatically compressing data.

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Parquet files are not natively supported by Azure Databricks.

Question 13

You are designing a stream processing solution using Azure Stream Analytics. The job must reference a static lookup table (product catalog) stored in Azure Blob Storage. The catalog is updated once daily. The job should automatically pick up the latest version without restarting. Which two configurations are required? (Choose two.)

Accepted Answer

Set the reference input's 'Path pattern' to include date and time placeholders. Option B is correct because Azure Stream Analytics reference data inputs support path pattern placeholders like {date} and {time} to dynamically resolve the latest blob file. This allows the job to automatically load a new version of the static lookup table when the blob is updated, without requiring a job restart. The path pattern must be structured to match the naming convention of the uploaded file, such as 'catalog/{date}/{time}/products.csv'.

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Configure the reference input with a static blob path

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Use Azure Event Grid to trigger job restart on blob update

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Store the reference data in Azure SQL Database instead of Blob Storage

Question 14

Which THREE factors should you consider when designing a monitoring strategy for Azure Synapse Analytics dedicated SQL pool performance?

Accepted Answer

Use dynamic management views (DMVs) to identify long-running queries.. Option A is correct because dynamic management views (DMVs) in Azure Synapse Analytics dedicated SQL pool, such as sys.dm_pdw_exec_requests and sys.dm_pdw_request_steps, provide real-time insight into query execution, allowing you to identify long-running queries, monitor resource consumption, and detect performance bottlenecks. This is a foundational monitoring practice for tuning workload performance.

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Ensure data is evenly distributed across distributions.

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Configure automatic index rebuild for columnstore indexes.

Question 15

Which TWO actions should you take to reduce costs associated with an Azure Synapse Dedicated SQL Pool that is used for reporting during business hours only?

Accepted Answer

Pause the pool during non-business hours.. Option A is correct because pausing a Dedicated SQL Pool stops billing for compute resources (DWU) while retaining storage costs. Since the pool is only needed for reporting during business hours, pausing it during non-business hours directly eliminates compute charges for that period, which is the most significant cost driver.

Answer

Enable advanced data compression on all tables.

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Scale down the pool during business hours.

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Change the distribution of large tables to ROUND_ROBIN.

Question 16

A company uses Azure Databricks to process streaming data from Event Hubs. The data is written to a Delta table. The job occasionally fails due to checkpoint corruption. Which THREE measures should you implement to improve reliability?

Accepted Answer

Configure checkpointing to a durable storage like Azure Data Lake Storage.. Option A is correct because checkpointing to durable storage like Azure Data Lake Storage (ADLS) ensures that streaming progress metadata is persisted across cluster restarts and failures. ADLS provides high durability and availability, preventing checkpoint corruption that can occur with local or ephemeral storage, thereby enabling exactly-once processing guarantees in Structured Streaming.

Answer

Increase the batch interval to reduce load.

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Increase the cluster size to handle spikes.

Question 17

A company uses Azure Synapse Analytics with a dedicated SQL pool. Data engineers need to implement column-level security so that only users with the 'Manager' role can see salary columns. Which TWO actions should they take?

Accepted Answer

Create a security policy with a filter predicate on the salary column using the function, and set the state to ON with BLOCK predicate.. Option C is correct because column-level security in Azure Synapse Analytics dedicated SQL pools is implemented via a security policy that uses a filter predicate (a function) to restrict access to specific columns. Setting the state to ON with a BLOCK predicate ensures that unauthorized users cannot see the salary column. Option D is correct because the inline table-valued function is the predicate function that checks role membership (e.g., using IS_MEMBER('Manager')) and returns 1 or 0, which the security policy uses to filter rows.

Answer

Create a stored procedure that checks the user's role and returns the appropriate columns.

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Grant the 'Manager' role SELECT permission on the security policy.

Answer

Use GRANT SELECT ON OBJECT::[dbo].[Employee](Salary) TO [Manager] to grant access to the salary column.

Question 18

You are designing data security for an Azure Data Lake Storage Gen2 account that stores sensitive customer data. You need to ensure that only authorized users can access the data and that access can be audited. Which TWO actions should you implement?

Accepted Answer

Configure diagnostic settings to send logs to a Log Analytics workspace.. Option A is correct because configuring diagnostic settings to send logs to a Log Analytics workspace enables auditing of data access events, such as read, write, and delete operations, which is essential for compliance and security monitoring. Option E is correct because Azure AD authentication provides fine-grained access control using role-based access control (RBAC) and eliminates the need for shared keys, aligning with the principle of least privilege.

Answer

Use storage account access keys (Shared Key) for authentication.

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Generate shared access signatures (SAS) with a long expiry time.

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Configure a firewall to allow access from all Azure services.

Question 19

You are designing a data transformation pipeline using Azure Databricks. The pipeline reads from Azure Data Lake Storage Gen2, performs aggregations, and writes to a Synapse dedicated SQL pool. Which three configurations should you implement to optimize performance and minimize cost? (Choose three.)

Accepted Answer

Enable Delta Lake on the storage account. Option A is correct because enabling Delta Lake on the storage account allows you to use Delta tables, which provide ACID transactions, scalable metadata handling, and unified batch/streaming capabilities. This is essential for reliable and performant data transformations in Azure Databricks, especially when reading from ADLS Gen2 and writing to Synapse.

Answer

Use a single-node cluster to reduce cost

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Disable autoscaling to avoid cost variability

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Use default Spark shuffle partitions (200)

Question 20

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 Azure Data Lake Storage Gen2 supports lifecycle management policies that automatically transition data to cooler tiers (cool or archive) based on age or usage patterns. Moving infrequently accessed data to lower-cost tiers directly reduces storage costs without manual intervention.

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Enable soft delete for blobs.

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

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Enable encryption at rest using customer-managed keys.