DP-900 Describe an analytics workload on Azure Practice Test 2 — 15 Questions

Question 1

A company uses Azure Data Factory to copy data from an on-premises SQL Server to Azure Data Lake Storage Gen2. The transfer must be accelerated using WAN optimization. Which Data Factory feature should the company enable?

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

Accepted Answer

Deploy a self-hosted integration runtime on-premises.. A self-hosted integration runtime (IR) is required to connect to on-premises SQL Server and can be configured with data compression and other network optimizations to improve transfer performance over WAN. It is the foundational component for hybrid data movement, enabling secure and efficient data transfer. Options A and C are general performance features but do not specifically address WAN optimization. Option D is incorrect because PolyBase is designed for loading data into Azure Synapse Analytics, not Azure Data Lake Storage Gen2, and therefore staging with PolyBase does not apply to the target destination.

Answer

Use the Copy Activity with automatic partitioning.

Answer

Enable parallel copy within the Copy Activity.

Answer

Use staging with Azure Blob Storage and PolyBase.

Question 2

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 is correct because it is a real-time analytics service designed to ingest, process, and analyze high volumes of streaming data from various sources (like Event Hubs or IoT Hubs) with low latency. It can perform transformations, aggregations, and anomaly detection on the fly. Power BI is correct because it can connect to real-time data sources like Azure Stream Analytics or Event Hubs to create streaming datasets and dashboards that update automatically with low latency. It supports push datasets and real-time tiles, enabling live visualization of streaming data without batch processing delays.

Answer

Azure Data Factory

Answer

Azure Data Lake Storage Gen2

Answer

Azure HDInsight with Spark

Question 3

Which TWO Azure services can be used to perform data transformation in a serverless manner? (Choose two.)

Accepted Answer

Azure Synapse serverless SQL pool. Azure Synapse serverless SQL pool (Option B) is correct because it allows you to query and transform data directly from data lake files (e.g., Parquet, CSV) using T-SQL without provisioning any dedicated compute resources. It uses a pay-per-query model, making it inherently serverless for data transformation tasks.

Azure Data Factory mapping data flows (Option C) is also correct. Mapping data flows provide a visual, code-free way to transform data at scale. While they leverage Spark clusters under the hood, these clusters are fully managed by Azure Data Factory. Users define the transformations, and ADF provisions, scales, and terminates the compute resources automatically, abstracting away server management. This aligns with the serverless paradigm where you pay for execution time and data processed, not for provisioned servers.

Answer

Azure Databricks with Apache Spark

Answer

Azure SQL Database

Answer

Azure HDInsight with Hive

Question 4

A company uses Azure Synapse Analytics to run large-scale analytics on sales data. They need to ensure that the workload can automatically scale based on demand without manual intervention. What feature should they configure?

Accepted Answer

Auto-pause and auto-resume. Auto-pause and auto-resume is the correct feature because it automatically stops compute resources when the dedicated SQL pool is idle, scaling down to zero, and resumes on demand when a query or activity is detected. This effectively scales the workload based on demand without manual intervention, meeting the requirement for automatic scaling. While it does not dynamically adjust compute size during active workloads, it provides cost-effective scaling by eliminating compute costs during inactivity and automatically restoring resources when needed.

Answer

Read Scale-out replicas

Answer

Elastic Database Pools

Answer

Manual scale-up during peak hours

Question 5

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

Accepted Answer

AzCopy. AzCopy is a command-line utility designed specifically for copying data to and from Azure Blob Storage and Azure Data Lake Storage Gen2 using REST APIs. It supports high-performance, parallel transfers and can handle large datasets efficiently, making it a valid ingestion tool. Azure Data Factory (ADF) is a cloud-based data integration service that allows you to create, schedule, and orchestrate data workflows. It provides a wide range of connectors, including one for Azure Data Lake Storage Gen2, enabling robust and scalable data ingestion from various sources.

Answer

Power BI

Answer

Azure SQL Database

Answer

Azure DevOps

Question 6

Refer to the exhibit. An administrator runs an Azure CLI command to show the status of a Synapse SQL pool. The output shown is returned. What does this output indicate about the SQL pool?

Accepted Answer

The SQL pool is online and available for queries.. The explanation refers to `"status": "Online"` as being present in the output. If this is indeed what the exhibit shows, then the explanation correctly justifies option B. However, the exhibit itself must be provided for a complete verification.

Answer

The SQL pool was restored from a backup.

Answer

The SQL pool is paused.

Answer

The SQL pool is currently being restored.

Question 7

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

Accepted Answer

Data ingestion (Azure Data Factory). Data ingestion using Azure Data Factory (ADF) is correct because ADF is the primary cloud-based ETL/ELT service for orchestrating and automating data movement from various sources into Azure storage or compute. It supports over 90 built-in connectors, scheduled triggers, and data flows, making it essential for the 'ingest' phase of an analytics pipeline.

Data storage using Azure Data Lake Storage (ADLS) is correct because ADLS Gen2 is optimized for big data analytics workloads, offering a highly scalable and cost-effective storage solution that integrates seamlessly with other Azure analytics services. It serves as the central repository for raw and processed data.

Data visualization using Power BI is correct because Power BI is a leading business intelligence tool that allows users to create interactive dashboards and reports from various data sources, including those stored in Azure. It is crucial for presenting insights derived from the analytics pipeline to end-users.

Answer

Azure Virtual Machines

Answer

Microsoft Entra ID

Question 8

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

Accepted Answer

Azure Databricks notebooks. Azure Databricks notebooks are correct because they provide an interactive, collaborative environment for data transformation using Apache Spark. You can write code in Python, Scala, SQL, or R to perform complex ETL operations, and the notebooks integrate directly with Azure Data Factory as a compute target in a pipeline. Azure Data Factory Data Flows (Mapping Data Flows) are also correct as they provide a visual, code-free environment within Azure Data Factory for designing and executing data transformation logic. They leverage Spark clusters managed by ADF to perform ETL/ELT operations at scale, making them a direct tool for data transformation within an Azure data pipeline.

Answer

Power BI Desktop

Answer

Microsoft Purview

Answer

Azure Storage Explorer

Question 9

Which TWO Azure services can be used to perform interactive ad-hoc analytics on large datasets using Apache Spark?

Accepted Answer

Azure Databricks. Azure Databricks provides a fully managed Apache Spark platform optimized for interactive ad-hoc analytics, offering collaborative notebooks, auto-scaling clusters, and a high-performance runtime for data engineering, data science, and machine learning workloads. Azure Synapse Analytics also offers Apache Spark pools (both dedicated and serverless) that are well-suited for interactive ad-hoc analytics on large datasets. Synapse Spark pools allow users to run Spark jobs, perform data exploration, and build machine learning models within a unified analytics platform, leveraging its integration with other Synapse capabilities.

Answer

Azure Analysis Services

Answer

Azure HDInsight

Answer

Azure Data Lake Storage

Question 10

A company wants to provide self-service analytics to business users, allowing them to create reports and dashboards from data stored in Azure Data Lake Storage. Which tool should they use?

Accepted Answer

Power BI. Power BI is the correct tool because it provides self-service analytics capabilities, allowing business users to connect to data stored in Azure Data Lake Storage, create interactive reports and dashboards, and share insights without requiring deep technical skills. Options A, B, and D are incorrect: Azure Synapse Studio is a big data and data warehousing tool, not designed for self-service business user dashboard creation; Azure Machine Learning studio is for building, training, and deploying machine learning models, not for creating reports; Azure Data Studio is a database management and query tool, not a reporting tool.

Answer

Azure Synapse Studio

Answer

Azure Machine Learning studio

Answer

Azure Data Studio

Question 11

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. Azure Synapse Analytics is a unified analytics platform that brings together enterprise data warehousing and Big Data analytics, serving as the core processing and querying engine for the data warehouse. Azure Data Lake Storage Gen2 provides highly scalable and cost-effective storage for all data, both structured and unstructured, acting as the foundational data lake for raw and processed data within the architecture.

Answer

Azure Cosmos DB

Answer

Azure Analysis Services

Question 12

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. Option D is also correct as Stream Analytics is commonly used to ingest and process IoT sensor data, allowing for real-time analysis to detect anomalies or trigger alerts when predefined thresholds are exceeded.

Answer

Building and training a machine learning model

Answer

Orchestrating complex data pipelines with dependencies

Answer

Batch processing of historical sales data

Question 13

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 pipeline uses a self-hosted integration runtime to connect to an on-premises SQL Server. For the copy activity to succeed, a self-hosted integration runtime must be installed on a machine that has network access to the on-premises SQL Server. Option B correctly identifies that the missing component is the self-hosted integration runtime not being installed on premises. Option A is incorrect because the Azure SQL Database firewall is not relevant for on-premises connectivity. Option C is incorrect because the SQL query is valid (as per the exhibit). Option D is incorrect because the on-premises SQL Server does not need a public endpoint; the self-hosted IR connects privately.

Answer

The Azure SQL Database firewall must allow Azure services

Answer

The SQL query is invalid

Answer

The on-premises SQL Server must have a public endpoint

Question 14

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 service principal does not have the Storage Blob Data Contributor role assigned at the container level.. Azure Data Lake Storage Gen2 requires that RBAC roles, such as Storage Blob Data Contributor, be assigned at the container (file system) level to grant access to the hierarchical namespace via the DFS endpoint. Assigning the role at the storage account level does not automatically apply to individual containers; the role must be explicitly scoped to the container. In this scenario, the service principal has the role at the account level but lacks it at the container level, causing the 'Access Denied' error when the Spark job tries to access Parquet files through the DFS endpoint. Options A and C are less likely because Storage Blob Data Owner is not required, and a firewall would typically cause a different error (e.g., network connectivity). Option B is misleading because the Storage Blob Data Contributor role does grant permissions on the DFS endpoint when properly scoped.

Answer

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

Answer

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

Answer

The storage account has a firewall enabled that blocks the Databricks cluster IP.

Question 15

Your company needs to build an analytics solution that can handle both batch and streaming data from IoT devices. The solution must allow complex event processing and real-time dashboards. Which Azure service should you use as the primary data ingestion and processing layer?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is the correct choice because it is designed for real-time stream processing, enabling complex event processing (CEP) through SQL-like queries and direct output to real-time dashboards like Power BI. While it does not handle batch processing natively, the requirement for real-time dashboards and CEP makes Stream Analytics the primary ingestion and processing layer, with batch processing typically handled by complementary services like Azure Data Lake or Azure Synapse.

Answer

Azure Data Factory

Answer

Azure Databricks

Answer

Azure Synapse Analytics