DP-900 Practice Test 35 — 15 Questions

Question 1

A company uses Azure Table storage to store session state for a web application. They notice that read latency increases during peak hours. Which design change should they implement to reduce latency?

Accepted Answer

Use a partition key that distributes load evenly, such as UserID. Azure Table storage partitions data based on the partition key. Using a partition key that distributes load evenly, such as UserID, ensures that read requests are spread across multiple partition servers, preventing hot partitions and reducing latency during peak hours.

Answer

Change to Azure Blob storage

Answer

Store large attributes in a separate table

Answer

Switch to Azure Queue storage

Question 2

Your company uses Azure Databricks to process streaming data from Event Hubs. The data is transformed and written to Azure Data Lake Storage Gen2 as Delta tables. You notice that some records are duplicated in the Delta tables. Which configuration change should you make to prevent duplicates?

Accepted Answer

Enable checkpointing in the streaming query to store progress.. Checkpointing in Spark Structured Streaming stores the offset of the last processed event from Event Hubs. When the query restarts, it reads from the checkpointed offset, ensuring each event is processed exactly once and preventing duplicates in the Delta table.

Answer

Add a separate job to deduplicate the Delta table.

Answer

Use Delta Lake's idempotent write support.

Answer

Increase the batch interval in the streaming query.

Question 3

An e-commerce application uses Azure SQL Database. The Orders table stores millions of rows with columns: OrderID (primary key, clustered index), CustomerID, OrderDate, OrderStatus, TotalAmount. Queries frequently filter on OrderDate and OrderStatus, and sort results by OrderDate DESC. Which indexing strategy will most improve query performance for these filters and sort?

Accepted Answer

Create a nonclustered index on OrderDate, OrderStatus and include other columns needed by the query.. A nonclustered index on (OrderDate, OrderStatus) supports both the filter and the sort in a single index seek/scan. SQL Server can use the index to locate rows matching both predicates and return them already sorted by OrderDate DESC without a separate sort operation, which is critical for performance on millions of rows.

Answer

Create a nonclustered index on OrderDate only.

Answer

Create a clustered columnstore index on the table.

Answer

Create a nonclustered index on OrderStatus only.

Question 4

Which TWO Azure services can be used to build a real-time analytics solution that ingests streaming data and provides dashboards with low latency? (Choose two.)

Accepted Answer

Power BI. Azure Stream Analytics (E) is correct because it is a fully managed, real-time stream processing engine that ingests data from sources such as Azure Event Hubs, IoT Hub, or Blob Storage, runs continuous SQL-like queries over the streaming data, and outputs results to sinks like Power BI with sub-second to low-latency processing. Power BI (A) is correct because it provides the dashboarding and visualization layer for real-time analytics, supporting streaming datasets and push datasets that render live tiles and dashboards with low latency when fed by services such as Azure Stream Analytics. Together, Stream Analytics handles ingestion and real-time computation while Power BI delivers the low-latency dashboards, which is exactly the scenario described. Azure Data Factory (B) is a batch-oriented data integration and orchestration service, not a real-time streaming engine, so it does not fit. Azure Data Lake Storage Gen2 (C) is a scalable storage layer for batch and analytical workloads, not a streaming ingestion or dashboarding service. Azure HDInsight with Spark (D) can process streaming data via Spark Structured Streaming, but it is a cluster-based big data service rather than a purpose-built low-latency real-time analytics and dashboarding solution, and it does not itself provide dashboards.

Answer

Azure Data Factory

Answer

Azure Data Lake Storage Gen2

Answer

Azure HDInsight with Spark

Question 5

Refer to the exhibit. A data engineer runs the PowerShell script shown. What is the purpose of this script?

Accepted Answer

List blobs modified in the last 7 days. The script uses `Get-AzStorageBlob` with the `-Prefix` parameter to filter blobs by name, then applies a `Where-Object` filter to select only blobs whose `LastModified` property is greater than or equal to 7 days ago. This effectively lists blobs modified in the last 7 days. The script does not perform any copy or delete operations, and it does not list all blobs without filtering.

Answer

List all blobs in the container

Answer

Copy blobs to another container

Answer

Delete old blobs from the container

Question 6

Which THREE are benefits of using a data warehouse in Azure?

Accepted Answer

Optimizes query performance for analytical workloads. A data warehouse in Azure (e.g., Azure Synapse Analytics) is optimized for analytical workloads through columnar storage and massively parallel processing (MPP), which significantly improves query performance on large datasets. This architecture is designed for read-heavy, aggregation-based queries typical of business intelligence and reporting, not for transactional or real-time operations.

Answer

Stores unstructured data like videos

Answer

Enables real-time streaming analytics

Question 7

Refer to the exhibit. An analyst runs this Kusto Query Language (KQL) query in Azure Data Explorer. What is the primary purpose of this query?

Accepted Answer

Find the top 5 most common event types in Texas. The query uses the `summarize` operator with `count()` to count events per `EventType`, then `top 5 by count_` to return the five event types with the highest counts, filtered to only rows where `State == 'TEXAS'`. This directly finds the top 5 most common event types in Texas.

Answer

Calculate total damage in Texas

Answer

Identify events with the highest damage

Answer

List all storm events in Texas

Question 8

Which THREE components are essential for building a modern data warehouse architecture on Azure?

Accepted Answer

Azure Databricks. Azure Databricks is correct because it provides an Apache Spark-based analytics platform that enables data engineering, data science, and machine learning on large-scale data. In a modern data warehouse architecture, Azure Databricks is used for data transformation, preparation, and advanced analytics, often feeding into or complementing Azure Synapse Analytics for structured querying and reporting.

Answer

Azure Analysis Services

Answer

Azure Cosmos DB

Question 9

A company wants to build a real-time dashboard that visualizes sales data as transactions occur. Which combination of Azure services should they use?

Accepted Answer

Azure Stream Analytics and Power BI. Azure Stream Analytics is a real-time event processing engine that can ingest streaming data (e.g., from Azure Event Hubs or IoT Hub) and output results directly to Power BI via the built-in Power BI output sink. This combination enables a live dashboard that updates automatically as sales transactions occur, without requiring batch processing or manual refresh.

Answer

Azure Synapse Analytics and PolyBase

Answer

Azure Data Explorer and Azure Data Lake Storage

Answer

Azure Analysis Services and Excel

Question 10

A data scientist needs to train a machine learning model using data stored in Azure Data Lake Storage. They want to use a collaborative notebook environment with built-in experiment tracking. Which Azure service should they use?

Accepted Answer

Azure Machine Learning. Azure Machine Learning provides a collaborative notebook environment (Jupyter notebooks) with built-in experiment tracking, model management, and automated ML capabilities. It is the correct choice for training machine learning models with data from Azure Data Lake Storage while tracking experiments.

Answer

Azure Synapse Analytics

Answer

Azure Databricks

Answer

Azure Data Studio

Question 11

An organization has a large dataset stored in Azure Blob Storage. They need to run complex analytics using SQL queries and also want to use the same data for machine learning models. Which Azure service provides both SQL-based analytics and native integration with ML frameworks?

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics is correct because it provides a unified analytics platform that combines enterprise data warehousing with big data analytics. It offers built-in SQL-based querying via dedicated SQL pools or serverless SQL pools, and it natively integrates with machine learning frameworks like Apache Spark and Azure Machine Learning for building and training models on the same data stored in Azure Blob Storage.

Answer

Azure Data Factory

Answer

Azure Analysis Services

Answer

Azure SQL Database

Question 12

Which TWO Azure services can be used to perform data transformation in an analytics pipeline? (Choose two.)

Accepted Answer

Azure Data Factory. Azure Data Factory is a cloud-based ETL service that allows you to create data pipelines to transform data at scale using mapping data flows or by invoking external compute services like Azure Databricks. It supports code-free visual transformations as well as custom code via Azure HDInsight or Databricks, making it a core service for data transformation in analytics pipelines.

Answer

Azure Data Lake Storage Gen2

Answer

Azure Event Hubs

Answer

Power BI

Question 13

Which THREE components are part of a typical modern data warehouse architecture on Azure?

Accepted Answer

Azure Data Factory. Azure Data Factory (A) is correct because it is the orchestration and data-integration service in a modern Azure data warehouse architecture, used to build pipelines that copy and transform data from source systems into the warehouse. Azure Synapse Analytics (D) is correct because it is the core analytics/warehouse engine, providing dedicated SQL pools (formerly SQL DW), serverless SQL pools, and Spark pools for large-scale analytical workloads. Azure Data Lake Storage Gen2 (E) is correct because it serves as the scalable, hierarchical-namespace storage layer (built on Blob Storage with HNS enabled) that holds raw and curated data in formats like Parquet and Delta before and after loading into the warehouse. Azure Cosmos DB (B) is not part of a typical data warehouse architecture; it is a globally distributed operational NoSQL database for transactional (OLTP) workloads, not analytical warehousing. Azure Analysis Services (C) is a semantic/tabular modeling layer for BI, and while it can complement analytics solutions, it is not one of the core components of a modern Azure data warehouse architecture as represented by ingestion, storage, and warehouse compute.

Answer

Azure Cosmos DB

Answer

Azure Analysis Services

Question 14

Refer to the exhibit. A database administrator runs this KQL query in Azure Monitor Log Analytics. The query returns no results. What is the most likely reason?

Accepted Answer

The ResourceType filter is incorrect. The KQL query filters on `ResourceType` with a value that does not match any actual Azure resource type (e.g., a typo or incorrect casing). Since Azure Monitor Log Analytics stores resource types in a specific format (e.g., 'microsoft.compute/virtualmachines'), an incorrect filter will return zero results even if data exists. The query syntax, render command, and time range are all valid, so the filter is the most likely cause.

Answer

The summarize operator syntax is wrong

Answer

The render command is not supported

Answer

The time range is incorrect

Question 15

A company has a Power BI dashboard that refreshes daily from an Azure SQL Database. During refresh, the database experiences high CPU usage that impacts transactional applications. They need to minimize impact while keeping the dashboard up-to-date. What should they do?

Accepted Answer

Configure Azure SQL Database with a readable secondary replica. Configuring Azure SQL Database with a readable secondary replica offloads read-only workloads, such as Power BI refreshes, to the secondary replica. This eliminates CPU contention on the primary replica, protecting transactional applications from performance degradation while keeping the dashboard up-to-date with near-real-time data. Option A (Disable Query Store) is irrelevant to reducing refresh CPU impact. Option B (read-only user) does not physically separate the read workload. Option C (lower-capacity license) does not change query execution location.

Answer

Disable the Query Store in the database

Answer

Create a read-only user for Power BI

Answer

Set Power BI to use a lower-capacity license