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

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

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 Microsoft Entra ID 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 2

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 3

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 4

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 5

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 6

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 7

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 8

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 9

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.

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Azure Cosmos DB

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Azure Analysis Services

Question 10

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 fully managed, real-time stream processing engine designed to ingest high-velocity event streams from sources like Event Hubs and IoT Hub and run continuous SQL-like queries over them, so option C (real-time fraud detection on credit card transactions) is correct because it requires low-latency, continuous evaluation of streaming transaction events to flag suspicious patterns as they occur. Option D (processing IoT sensor data and alerting when thresholds are exceeded) is also correct because Stream Analytics natively integrates with IoT Hub/Event Hubs and can emit alerts to outputs such as Service Bus, Event Grid, or Functions whenever a sliding or tumbling window query detects a threshold breach. Option A is incorrect because building and training ML models is the role of Azure Machine Learning, not a stream-processing service. Option B is incorrect because orchestrating pipelines with dependencies is handled by Azure Data Factory or Synapse Pipelines, not Stream Analytics. Option E is incorrect because batch processing of historical sales data is better served by Azure Data Factory, Synapse, Databricks, or HDInsight rather than a real-time streaming engine.

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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 11

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

Question 12

Refer to the exhibit. You have created this Azure Data Factory pipeline. When you run it, the copy activity fails with a connectivity error. What is the most likely missing component?

Accepted Answer

A self-hosted integration runtime is not installed on premises. The correct answer is B: a self-hosted integration runtime is not installed on premises. When Azure Data Factory must copy data to or from an on-premises SQL Server, the copy activity cannot reach it directly; it requires a self-hosted integration runtime installed on a machine in the on-premises network to act as the bridge, and its absence produces exactly this connectivity error. Option A is wrong because the Azure SQL Database firewall applies to Azure SQL Database, not to an on-premises SQL Server. Option C is wrong because an invalid SQL query would cause a query/syntax error, not a connectivity failure. Option D is wrong because exposing the on-premises SQL Server via a public endpoint is not the required or recommended solution; the self-hosted integration runtime is.

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The Azure SQL Database firewall must allow Azure services

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The SQL query is invalid

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The on-premises SQL Server must have a public endpoint

Question 13

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.

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The summarize operator syntax is wrong

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The render command is not supported

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The time range is incorrect

Question 14

Your company runs a sales analytics dashboard on Power BI that refreshes every hour from Azure Synapse Analytics. During peak hours, the dashboard refresh fails with a 'timeout' error. Which action should you take FIRST to resolve the issue?

Accepted Answer

Implement incremental refresh in Power BI to refresh only changed data.. Implementing incremental refresh in Power BI reduces the amount of data loaded during each refresh cycle, which directly addresses timeout errors by limiting the refresh to only changed or new data rather than the entire dataset. This is the most efficient first step to reduce refresh duration without changing the underlying architecture or data source connection mode.

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Scale up the Azure Synapse dedicated SQL pool to handle more concurrent queries.

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Configure the dashboard to use DirectQuery instead of Import mode.

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Export the data to CSV files and load into Power BI from Azure Blob Storage.

Question 15

A retail company uses Azure Data Lake Storage Gen2 as a data lake and Azure Databricks for ETL. They notice that a Spark job reading Parquet files from the data lake fails with an 'Access Denied' error when the job runs as a service principal. The service principal has Storage Blob Data Contributor role on the storage account. What is the most likely cause?

Accepted Answer

The storage account has a firewall enabled that blocks the Databricks cluster IP.. Azure RBAC role assignments are inherited down the resource hierarchy. If a service principal has Storage Blob Data Contributor at the storage account scope, that permission applies to all containers (file systems) in the account, including access through the ADLS Gen2 DFS endpoint (dfs.core.windows.net). Therefore, the account-level assignment in the scenario would not by itself cause an 'Access Denied' error. The most likely cause is that the storage account firewall or network restrictions are blocking the Databricks cluster's IP address, which produces an access/authorization failure when the Spark job attempts to read the Parquet files. Storage Blob Data Owner is not required, and the role does not need to be re-assigned at the container level.

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The service principal needs the Storage Blob Data Owner role instead.

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The service principal lacks the necessary permissions on the Data Lake Storage Gen2 endpoint (dfs.core.windows.net).

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The service principal does not have the Storage Blob Data Contributor role assigned at the container level.