DP-900 Practice Test 33 — 15 Questions

Question 1

You are deploying the above ARM template snippet for a storage account. What is the effect of setting 'isHnsEnabled' to true?

Accepted Answer

Enables Azure Data Lake Storage Gen2.. Setting 'isHnsEnabled' to true enables the Hierarchical Namespace (HNS) feature on the Azure Storage account, which is the core requirement for Azure Data Lake Storage Gen2. This allows the storage account to support a file system-like directory structure with POSIX-compliant access control lists, enabling analytics workloads to use both blob and file system semantics.

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Enables Azure Blob Storage lifecycle management.

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Enables geo-redundant storage (GRS).

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Enables Azure Files share.

Question 2

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.

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Azure Analysis Services

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Azure HDInsight

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Azure SQL Database

Question 3

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 4

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.

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Azure Table Storage

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Azure Blob Storage

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Azure Event Hubs

Question 5

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 6

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.

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Azure Data Factory

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Azure Synapse Analytics

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Azure Data Lake Storage Gen2

Question 7

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 8

Refer to the exhibit. You execute the above T-SQL statements in Azure Synapse Analytics. What is the purpose of this code?

Accepted Answer

To create an external table that can query Parquet files stored in Azure Data Lake Storage Gen2.. The T-SQL code creates an external data source pointing to Azure Data Lake Storage Gen2, an external file format for Parquet, and an external table that references the Parquet files. This allows querying the Parquet files directly without importing them into the database, which is the definition of an external table in Azure Synapse Analytics.

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To create a view over the Parquet files.

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To import data from Parquet files into a permanent table in Synapse.

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To create a regular table in the Synapse database.

Question 9

Refer to the exhibit. You have an Azure Data Factory pipeline definition as shown. The pipeline fails with a 'Source not found' error. The BlobInputDataset points to a container that exists. What is the most likely cause?

Accepted Answer

The dataset's file path is incorrect or no files match the pattern.. The 'Source not found' error in Azure Data Factory indicates that the source dataset cannot locate the specified file or blob. Since the container exists, the most likely cause is that the file path defined in the dataset is incorrect or that no files match the specified pattern (e.g., wildcard or prefix). This is a common configuration issue when the dataset's folder path or file name does not correspond to the actual blob location.

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The Azure Blob Storage container is empty.

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The Azure Data Factory managed identity does not have access to the storage account.

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The SQL sink database does not exist.

Question 10

Your company is developing a new analytics solution to track customer sentiment from social media feeds. The data arrives as a continuous stream of JSON messages. The solution must process the data in near real-time, enrich it with customer profile data stored in Azure Cosmos DB, and then store the results in a data lake for historical analysis. The team wants to use a low-code approach for the data processing logic. You are considering the following architectures: A) Use Azure Event Hubs to ingest the stream, Azure Stream Analytics to process and enrich the data using Cosmos DB as a reference data source, and output to Azure Data Lake Storage Gen2. B) Use Azure IoT Hub to ingest the stream, Azure Databricks to process the data, and write to Azure Blob Storage. C) Use Azure Event Hubs to ingest the stream, Azure Functions to process each message, query Cosmos DB for enrichment, and write to Azure Data Lake Storage Gen2. D) Use Azure Event Hubs to ingest the stream, Azure Data Factory to execute a mapping data flow for enrichment, and write to Azure Data Lake Storage Gen2. Which architecture best meets the requirements of near real-time processing, enrichment, and low-code?

Accepted Answer

Option A. Azure Stream Analytics provides a low-code, SQL-based approach for near real-time processing, and it can natively enrich streaming data by using Azure Cosmos DB as a reference data source via a JOIN operation. The output is directly written to Azure Data Lake Storage Gen2, meeting all requirements without custom code.

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Option C

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Option D

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Option B

Question 11

A small business wants to start using Azure for analytics. They have a few CSV files stored on-premises that they want to analyze. They have no budget for complex infrastructure and prefer a fully managed, serverless solution. They need to create interactive visualizations and share them with their team. The data does not change frequently, so they are okay with daily refreshes. Which of the following options should they choose? A) Upload the CSV files to Azure Data Lake Storage Gen2, use Azure Databricks to create a data processing pipeline, and then use Power BI to visualize the results. B) Upload the CSV files to Azure Blob Storage, use Azure Data Factory to load the data into Azure SQL Database, and then use Power BI to connect and visualize. C) Upload the CSV files to OneDrive for Business, use Power BI Desktop to import the data, and publish to Power BI Service with scheduled refresh. D) Upload the CSV files to Azure Data Lake Storage Gen2, use Azure Synapse Serverless SQL pool to query the data, and then use Power BI to connect. Which option is the simplest and most cost-effective?

Accepted Answer

Option C. It uses OneDrive for Business as a simple storage location, Power BI Desktop for importing CSV data, and Power BI Service for publishing and sharing interactive visualizations with scheduled daily refresh. This is fully managed, serverless, and requires no complex infrastructure, aligning perfectly with the small business's budget and simplicity requirements.

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Option B

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Option D

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Option A

Question 12

Refer to the exhibit. A data engineer wants to ensure that all Azure Storage accounts used for analytics use customer-managed keys. They apply this Azure Policy. What is the outcome?

Accepted Answer

The policy denies creation of StorageV2 accounts that use Microsoft-managed keys. The Azure Policy in the exhibit uses the 'Deny' effect on storage accounts that do not use customer-managed keys (i.e., that use Microsoft-managed keys). This means any attempt to create or update a storage account without customer-managed encryption will be blocked. Option D correctly identifies that the policy denies creation of StorageV2 accounts (or any storage account type covered by the policy scope) that use Microsoft-managed keys.

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The policy audits existing storage accounts for compliance

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The policy allows all storage accounts whether they use customer-managed keys or not

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The policy only applies to storage accounts with hierarchical namespace enabled

Question 13

Which TWO are benefits of using Azure SQL Database elastic pools?

Accepted Answer

Predictable pricing for a group of databases. Azure SQL Database elastic pools provide predictable pricing because you pay for a fixed set of resources (eDTUs or vCores) allocated to the pool, regardless of how many databases use them. This allows you to budget for a group of databases with variable usage patterns without incurring per-database costs, making it cost-effective for workloads with intermittent or unpredictable demand.

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Unlimited storage per database

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Isolated performance for each database

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Support for databases over 1 TB each

Question 14

Drag and drop the steps to create an Azure Stream Analytics job in the correct order.

Accepted Answer

Create the Stream Analytics job, then configure inputs, then configure outputs, then define the transformation query.. Creating a Stream Analytics job involves setting up the job parameters, then configuring inputs, outputs, and the transformation query.

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Create the Stream Analytics job, then define the transformation query, then configure inputs, then configure outputs.

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Configure inputs, then configure outputs, then create the Stream Analytics job, then define the transformation query.

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Define the transformation query, then create the Stream Analytics job, then configure inputs, then configure outputs.

Question 15

Match each Azure data tool to its purpose.

Accepted Answer

Azure Data Lake Storage: A scalable and secure data lake for high-performance analytics.. The correct matches are: Azure Data Lake Storage for scalable data lake storage, Azure Data Factory for data integration, and Azure Stream Analytics for real-time stream processing. Azure Synapse Analytics is a unified analytics platform, and Azure Databricks is an Apache Spark-based analytics platform. Common confusions include mixing up Synapse Analytics with Data Factory, and Databricks with Stream Analytics.

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Azure Synapse Analytics: A cloud-based data integration service.

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Azure Databricks: A real-time stream processing engine.