DP-900 Describe an analytics workload on Azure Practice Test 13 — 15 Questions

Question 1

Which THREE components are typically part of a modern data warehouse architecture on Azure? (Choose three.)

Accepted Answer

Azure Data Factory. Azure Data Factory is correct because it serves as the cloud-based ETL (Extract, Transform, Load) service that orchestrates and automates data movement and transformation across various sources and destinations. In a modern data warehouse architecture, Data Factory is used to ingest raw data from on-premises or cloud sources, transform it using mapping data flows or external compute (e.g., Azure Databricks), and load it into the data warehouse or data lake for analytics. It provides a code-free visual interface or SDK-based control for scheduling and monitoring pipelines, making it essential for the ingestion and preparation layer.

Answer

Azure Stream Analytics

Answer

Azure Cosmos DB

Question 2

A data analyst needs to create a real-time dashboard in Power BI that displays streaming data from Azure Event Hubs. The data must be refreshed every second. Which Power BI feature should they use?

Accepted Answer

Streaming dataset. A is correct because Power BI's streaming dataset feature is specifically designed to handle real-time data ingestion and visualization with sub-second latency. It supports direct integration with Azure Event Hubs, allowing the dashboard to refresh every second without the need for scheduled refresh or query-based retrieval.

Answer

Import mode with scheduled refresh

Answer

DirectQuery

Answer

Power BI Dataflows

Question 3

Which TWO options are valid ways to ingest data into Azure Data Lake Storage Gen2?

Accepted Answer

AzCopy. AzCopy (C) is a valid ingestion method because it is a command-line utility designed to copy data to and from Azure Storage, including Data Lake Storage Gen2, using commands like `azcopy copy` with blob or ADLS Gen2 endpoints. Azure Data Factory (E) is also correct because it provides connectors and copy activities that can load data from numerous sources into ADLS Gen2 as a sink. Power BI (A) is primarily a visualization and reporting service, not a data ingestion tool for ADLS Gen2. Azure SQL Database (B) is a relational database service and can be a source or target, but it is not itself a valid ingestion mechanism into ADLS Gen2. Azure DevOps (D) is a development lifecycle platform and does not provide native data ingestion into ADLS Gen2.

Answer

Power BI

Answer

Azure SQL Database

Answer

Azure DevOps

Question 4

Your company has a data pipeline in Azure Data Factory that runs daily. Recently, the pipeline started failing with timeouts. You suspect a downstream database is slow. What should you do to monitor and alert on pipeline run duration?

Accepted Answer

Configure alerts in Azure Monitor based on pipeline run duration. Azure Monitor can be configured to create alerts based on metrics such as pipeline run duration. When the duration exceeds a threshold, an alert triggers, allowing proactive notification of slow downstream databases. This directly addresses the need to monitor and alert on pipeline performance issues.

Answer

Query the pipeline runs in Log Analytics

Answer

Check Azure Service Health for issues

Answer

Use Azure Advisor to check performance recommendations

Question 5

A company wants to analyze customer feedback from surveys and social media. The data includes both structured (ratings) and unstructured (comments) text. They plan to use Azure Cognitive Services for sentiment analysis. Which service should they use for text analytics?

Accepted Answer

Azure AI Language. Azure AI Language (formerly part of Azure Cognitive Services) provides pre-built text analytics capabilities, including sentiment analysis, key phrase extraction, and language detection. This service is specifically designed to process unstructured text data like survey comments and social media posts, making it the correct choice for analyzing customer feedback.

Answer

Azure Synapse Analytics

Answer

Azure Cosmos DB

Answer

Azure Machine Learning

Question 6

Which THREE components are part of an end-to-end analytics solution on Azure?

Accepted Answer

Data ingestion (Azure Data Factory). Option A (Data ingestion via Azure Data Factory) is correct because an end-to-end analytics solution must first bring data from disparate sources into Azure, and Azure Data Factory is the cloud ETL/orchestration service that performs this ingestion and pipeline movement. Option D (Data storage via Azure Data Lake Storage) is correct because ingested data must land in a scalable, durable repository, and ADLS Gen2 provides hierarchical namespace storage optimized for big-data analytics workloads. Option C (Data visualization via Power BI) is correct because the final stage of an end-to-end analytics solution is delivering insights to users, which Power BI accomplishes through interactive reports and dashboards. Option B (Azure Virtual Machines) is not part of the core analytics pipeline; VMs are generic IaaS compute and are not a required analytics component. Option E (Microsoft Entra ID) is an identity and access management service, not an analytics pipeline stage, so it does not belong among the three components.

Answer

Azure Virtual Machines

Answer

Microsoft Entra ID

Question 7

An organization needs to run complex queries on petabytes of data stored in Azure Data Lake Storage. They want to use serverless compute to avoid managing infrastructure. Which Azure service should they use?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool is the correct choice because it provides serverless compute that can run complex T-SQL queries directly against data stored in Azure Data Lake Storage without requiring any infrastructure management. It uses a pay-per-query billing model and can scale automatically to handle petabytes of data, making it ideal for ad-hoc analytics on large-scale data lakes.

Answer

Azure Analysis Services

Answer

Azure HDInsight

Answer

Azure SQL Database

Question 8

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 9

A company uses Azure Stream Analytics to process IoT data from thousands of devices. They need to store the results in a way that supports fast querying for historical analysis. Which output sink should they use?

Accepted Answer

Azure Data Lake Storage Gen2. Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct output sink because it combines a hierarchical namespace with Azure Blob Storage's scalable object storage, enabling fast querying for historical analysis via tools like Azure Synapse Analytics, PolyBase, or Apache Spark. ADLS Gen2 supports high-throughput writes from Stream Analytics and allows efficient directory-level operations and fine-grained access control, which are critical for large-scale IoT data analytics.

Answer

Azure Table Storage

Answer

Azure Blob Storage

Answer

Azure Event Hubs

Question 10

Which TWO tools can be used to transform data in an Azure data pipeline?

Accepted Answer

Azure Databricks notebooks. Azure Databricks notebooks (C) are correct because they run Apache Spark code (Python, Scala, SQL, R) that can perform arbitrary data transformations such as filtering, joining, aggregating, and cleansing at scale within an Azure data pipeline. Azure Data Factory Data Flow (E) is correct because it provides a visual, code-free way to build scalable data transformation logic (mapping data flows) that executes on Azure Databricks-backed Spark clusters as part of an ADF pipeline. Power BI Desktop (A) is a reporting and visualization tool, not a pipeline data transformation service. Microsoft Purview (B) is a data governance, cataloging, and lineage service rather than a transformation engine. Azure Storage Explorer (D) is a client utility for browsing and managing storage accounts, not for transforming data.

Answer

Power BI Desktop

Answer

Microsoft Purview

Answer

Azure Storage Explorer

Question 11

A data analyst needs to create interactive reports from data stored in an Azure SQL Database. They want to use a self-service tool that requires minimal IT support. Which tool should they use?

Accepted Answer

Power BI Desktop. Power BI Desktop is a self-service business intelligence tool designed for creating interactive reports and dashboards with minimal IT support. It connects directly to Azure SQL Database, allowing analysts to import or query data using DirectQuery, and provides drag-and-drop visualizations without requiring database administration skills.

Answer

Azure Synapse Studio

Answer

SQL Server Management Studio

Answer

Azure Data Studio

Question 12

Refer to the exhibit. An Azure Data Factory pipeline JSON is shown. What does this pipeline do?

Accepted Answer

Copies filtered sales data from on-premises to Azure SQL Database. The pipeline uses a Copy activity with a source dataset pointing to an on-premises SQL Server (via a self-hosted integration runtime) and a sink dataset pointing to an Azure SQL Database. The source query includes a WHERE clause filtering sales data by a date range, so only filtered data is copied. This matches option C.

Answer

Copies sales data from Azure SQL Database to on-premises

Answer

Copies all sales data from on-premises to Azure SQL Database

Answer

Copies sales data from on-premises to Azure Data Lake Storage

Question 13

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 14

A company uses Azure Databricks for data engineering. They need to ensure that only authorized users can access the workspace, and they want to use single sign-on (SSO) with their existing identity provider. Which integration should they configure?

Accepted Answer

Microsoft Entra ID (Azure AD). Microsoft Entra ID (Azure AD) is the identity and access management service that provides SSO capabilities for Azure Databricks. By integrating Azure Databricks with Entra ID, you can enforce conditional access policies and authenticate users via your existing identity provider using protocols like SAML 2.0 or OAuth 2.0, ensuring only authorized users access the workspace.

Answer

Microsoft Defender XDR

Answer

Microsoft Intune

Answer

Azure Key Vault

Question 15

A company plans to implement a near-real-time analytics solution for streaming IoT sensor data. Which Azure service should they use to ingest and process the data streams?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is a real-time event processing engine designed to ingest, process, and analyze high-velocity streaming data from sources like IoT sensors. It supports SQL-based queries to transform and route data streams to outputs such as Power BI or Azure Synapse, making it ideal for near-real-time analytics.

Answer

Azure Data Factory

Answer

Azure Synapse Analytics

Answer

Azure Data Lake Storage Gen2