DP-900 Describe an analytics workload on Azure Practice Test 14 — 15 Questions

Question 1

An organization has a data lake that contains both structured and unstructured data. They need to catalog the data assets and enable data discovery for users. Which Azure service should they use?

Accepted Answer

Microsoft Purview. Microsoft Purview is a unified data governance service that helps you manage and govern your on-premises, multicloud, and software-as-a-service (SaaS) data. It provides automated data discovery, sensitive data classification, and end-to-end data lineage, making it the correct choice for cataloging both structured and unstructured data assets in a data lake and enabling data discovery for users.

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Azure Data Factory

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Azure Synapse Analytics

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Azure Data Lake Storage

Question 2

A financial services company needs to run ad-hoc SQL queries on petabytes of data stored in Azure Data Lake Storage without provisioning a dedicated data warehouse. Which Azure service should they use?

Accepted Answer

Azure Synapse Analytics serverless SQL pool. Azure Synapse Analytics serverless SQL pool is the correct choice because it allows you to run ad-hoc SQL queries directly against data in Azure Data Lake Storage without provisioning any dedicated compute resources. It uses a pay-per-query model, automatically scaling compute to handle petabytes of data, making it ideal for intermittent, exploratory workloads.

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Azure Analysis Services

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Azure SQL Database

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Azure Data Lake Storage

Question 3

A company runs a SQL Server database on an Azure virtual machine. They need to offload reporting queries to a read-only copy without modifying the application. Which Azure service should they use?

Accepted Answer

Azure SQL Database with read scale-out. Azure SQL Database with read scale-out (C) is correct because it creates a read-only replica of the database that can handle reporting queries without modifying the application. The application simply adds `ApplicationIntent=ReadOnly` to the connection string, and the Azure gateway automatically routes read-only queries to the secondary replica, offloading the primary from reporting workloads.

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Azure Analysis Services

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Azure SQL Managed Instance

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Azure Synapse Analytics dedicated SQL pool

Question 4

A data engineer needs to transform and clean data from multiple sources before loading it into Azure Synapse Analytics. Which Azure service should they use for this ETL process?

Accepted Answer

Azure Data Factory. Azure Data Factory is the correct service because it is a cloud-based ETL (Extract, Transform, Load) service designed specifically to orchestrate and automate data movement and transformation from multiple sources. It provides built-in connectors for various data stores and supports data flows for cleaning and transforming data before loading it into Azure Synapse Analytics.

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Azure Analysis Services

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Azure Databricks

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Azure Stream Analytics

Question 5

A company uses Azure Data Lake Storage Gen2 as a data lake. They need to enforce row-level security for sensitive data so that sales representatives can only see rows for their assigned region. Which approach should they use?

Accepted Answer

Load data into Azure Synapse Analytics dedicated SQL pool and implement row-level security (RLS). Row-level security (RLS) in Azure Synapse Analytics dedicated SQL pool allows you to restrict data access at the row level based on a user's identity or group membership. By loading the data into a dedicated SQL pool and defining a security policy with a predicate function that filters rows by region, you can ensure sales representatives only see rows for their assigned region. This is the correct approach because RLS is designed specifically for this purpose and integrates with Azure Active Directory for user authentication.

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Apply sensitivity labels in Microsoft Purview

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Use Azure RBAC roles on the storage account

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Use Azure Data Lake Storage Gen2 access control lists (ACLs) on folders per region

Question 6

An organization wants to build a real-time dashboard that visualizes IoT sensor data as it arrives. Which Azure service should they use for processing the streaming data?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is a real-time analytics service designed to process streaming data from sources like IoT devices. It can ingest data from Azure Event Hubs or IoT Hub, apply SQL-based queries to detect patterns or anomalies, and output results to a dashboard or storage with sub-second latency, making it ideal for real-time IoT dashboards.

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Azure Analysis Services

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Azure Data Factory

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Azure Databricks

Question 7

A company is designing a data analytics solution. They need to store large volumes of raw data in its native format and support schema-on-read for data science exploration. Which storage technology should they use?

Accepted Answer

Azure Data Lake Storage Gen2. Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct choice because it combines a hierarchical namespace with Azure Blob Storage's scalable object storage, allowing raw data to be stored in its native format (e.g., CSV, JSON, Parquet) without transformation. It supports schema-on-read, meaning the schema is applied at query time (e.g., via Apache Spark or Azure Synapse SQL), which is ideal for data science exploration where the data structure may not be predefined.

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Azure Blob Storage

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Azure Cosmos DB

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Azure SQL Database

Question 8

A data analyst needs to create an interactive report that combines sales data from Azure SQL Database and Azure Cosmos DB. The report must refresh daily. Which tool should they use?

Accepted Answer

Power BI. Power BI is the correct tool because it is designed for creating interactive reports and dashboards, and it can directly connect to both Azure SQL Database and Azure Cosmos DB as data sources. Its scheduled refresh capability allows the report to refresh daily without manual intervention, meeting the requirement for an interactive, combined report.

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Azure Data Factory

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Azure Synapse Studio

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Azure Analysis Services

Question 9

A company uses Azure Synapse Analytics for their data warehouse. They notice that queries against the fact table are slow. The fact table is hash-distributed on OrderID. Most queries filter by CustomerID. What should they do to improve performance?

Accepted Answer

Change the distribution column to CustomerID. The fact table is hash-distributed on OrderID, but queries filter by CustomerID. This causes data movement across nodes for each query, as the filter column doesn't align with the distribution key. Changing the distribution column to CustomerID ensures that rows for the same CustomerID are co-located on the same compute node, eliminating unnecessary data shuffling and improving query performance.

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Change to round-robin distribution

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Use rowstore instead of columnstore

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Replicate the fact table to all compute nodes

Question 10

A business user wants to ask natural language questions about their data in Power BI and get answers without writing DAX. Which Power BI feature should they use?

Accepted Answer

Q&A visual. The Q&A visual in Power BI allows users to type natural language questions about their data and receive answers in the form of charts or tables, without needing to write DAX expressions. It uses an underlying natural language engine that interprets the query and automatically generates the appropriate visual or summary. This directly matches the business user's requirement for a no-code, natural language interface.

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Copilot for Microsoft 365

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Power Automate

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Quick Insights

Question 11

A company ingests streaming data from thousands of devices into Azure Event Hubs. They need to transform and aggregate the data in real time before storing it in Azure Data Lake Storage Gen2. Which Azure service should they use between Event Hubs and ADLS Gen2?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is purpose-built for real-time data processing and analytics, allowing you to define SQL-like queries to transform and aggregate streaming data from Event Hubs before outputting it directly to Azure Data Lake Storage Gen2. It provides exactly-once delivery semantics and low-latency processing, making it the ideal service for this ingestion-to-storage pipeline.

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Azure Functions

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Azure Databricks

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Azure Data Factory

Question 12

Which TWO Azure services can be used to build a data pipeline that moves data from on-premises SQL Server to Azure Synapse Analytics?

Accepted Answer

Azure Data Factory. Azure Data Factory (A) is correct because it is a cloud-based ETL and data integration service that provides built-in connectors for both on-premises SQL Server (via self-hosted integration runtime) and Azure Synapse Analytics, enabling you to create, schedule, and orchestrate data pipelines that move and transform data between these sources.

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Azure Machine Learning

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Azure Stream Analytics

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Azure Analysis Services

Question 13

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

Accepted Answer

Azure Data Factory. Azure Data Factory is a cloud-based ETL and data integration service that orchestrates and automates the movement and transformation of data. In a modern data warehouse architecture, it is used to ingest data from various sources, perform transformations, and load the data into the data warehouse or data lake. This makes it a core component for the 'ingest' and 'prepare' stages of the architecture.

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Azure Cosmos DB

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Azure Analysis Services

Question 14

Which TWO are valid use cases for Azure Stream Analytics?

Accepted Answer

Real-time fraud detection on credit card transactions. Azure Stream Analytics is a real-time event processing engine designed for analyzing high volumes of fast-moving streaming data. Option C is correct because Stream Analytics can process credit card transactions in real time, applying pattern matching and anomaly detection to identify potentially fraudulent activity as it occurs.

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Building and training a machine learning model

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Orchestrating complex data pipelines with dependencies

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Batch processing of historical sales data

Question 15

Refer to the exhibit. You deploy this Azure Stream Analytics job. The job runs but no data is written to the Azure SQL Database table. What is the most likely cause?

Accepted Answer

The TumblingWindow has not yet elapsed. The TumblingWindow function in Azure Stream Analytics processes data in fixed, non-overlapping time intervals. Since the job runs but no data is written, the most likely cause is that the first window has not yet elapsed, meaning no aggregation has been triggered to output results. Stream Analytics only emits results when a tumbling window completes, so data will not appear in the SQL Database until the window duration passes.

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The query syntax is invalid

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The Event Hub name is incorrect

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The SKU does not support SQL output