DP-900 Describe an analytics workload on Azure Practice Test 12 — 15 Questions

Question 1

A company runs a critical workload in Azure Synapse Analytics. They need to ensure that if a single node fails, the data in the control node and compute nodes is not lost. Which configuration should they use?

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

Accepted Answer

No additional configuration is needed; Synapse provides built-in fault tolerance.. Azure Synapse Analytics (formerly SQL DW) is a distributed MPP (Massively Parallel Processing) system that automatically replicates data across multiple internal copies within the control node and compute nodes. This built-in fault tolerance ensures that if a single node fails, data is not lost because Synapse maintains at least three synchronous replicas of all data and metadata. No additional configuration is required for node-level failure protection.

Answer

Deploy a SQL Server Always On availability group.

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Configure active geo-replication.

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Use a Windows Server Failover Cluster.

Question 2

A data analyst needs to create a real-time dashboard in Power BI that refreshes every second from an Azure Stream Analytics job. Which Power BI feature should they use?

Accepted Answer

Streaming dataset. B is correct because a streaming dataset in Power BI is designed to ingest real-time data from sources like Azure Stream Analytics and automatically update visuals as new data arrives. This feature supports push-based updates at sub-second intervals, making it ideal for a dashboard that refreshes every second without requiring manual or scheduled refresh cycles.

Answer

Scheduled refresh

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DirectQuery

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Import mode

Question 3

Which TWO Azure services can be used to perform interactive data analytics on large datasets without managing infrastructure? (Choose two.)

Accepted Answer

Azure Synapse Analytics Serverless SQL pool. Azure Synapse Analytics Serverless SQL pool (A) is correct because it lets you run T-SQL queries directly against files in Azure Data Lake Storage using a pay-per-query, on-demand model with no cluster provisioning or infrastructure management, making it ideal for interactive exploration of large datasets. Azure Databricks (C) is correct because it provides an Apache Spark-based, fully managed analytics platform where the service handles cluster provisioning, scaling, and maintenance, enabling interactive notebooks and SQL analytics on large datasets without the user managing infrastructure. Azure SQL Database (B) is a managed relational database for transactional/OLTP workloads rather than an interactive big-data analytics service, so it does not fit. Azure Data Factory (D) is a data integration and orchestration service for pipelines and ETL/ELT movement, not an interactive analytics engine. Azure Data Lake Storage Gen2 (E) is a storage layer for holding data, not a compute/analytics service that performs interactive queries.

Answer

Azure SQL Database

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Azure Data Factory

Answer

Azure Data Lake Storage Gen2

Question 4

Which THREE components are part of Microsoft Fabric's end-to-end analytics platform? (Choose three.)

Accepted Answer

Synapse Data Engineering. Synapse Data Engineering is a core component of Microsoft Fabric, providing a unified platform for data ingestion, transformation, and orchestration using Spark and pipelines. It integrates seamlessly with OneLake for storage and Power BI for visualization, forming part of Fabric's end-to-end analytics solution.

Answer

Azure Machine Learning

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Azure DevOps

Question 5

Which TWO of the following are benefits of using a data lake architecture? (Choose two.)

Accepted Answer

Ability to store raw data in its native format. A data lake architecture is designed to store raw data in its native format without requiring schema-on-write transformations. This allows organizations to ingest data as-is from various sources, preserving the original structure and enabling schema-on-read flexibility for analytics.

Answer

ACID transactions for all operations

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Optimized for high-frequency OLTP workloads

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Built-in data governance without additional tools

Question 6

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.

Answer

Enables Azure Blob Storage lifecycle management.

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Enables geo-redundant storage (GRS).

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Enables Azure Files share.

Question 7

You are reviewing the Azure Data Factory mapping data flow configuration above. Which transformation is missing to ensure that only sales from the current year are loaded?

Accepted Answer

Filter transformation. The Filter transformation is used in mapping data flows to restrict rows based on a condition. To load only sales from the current year, you would apply a filter condition such as `year(SalesDate) == year(currentDate())`, which removes all rows not matching the current year. This is the correct transformation for row-level filtering.

Answer

Derived column transformation

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Aggregate transformation

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Window transformation

Question 8

A company uses Azure Synapse Analytics to run large-scale batch processing jobs every night. The jobs currently take 6 hours to complete, but the business requires completion within 4 hours. Which action should the company take to improve job performance?

Accepted Answer

Increase the data warehouse units (DWUs) for the dedicated SQL pool.. Increasing the data warehouse units (DWUs) for the dedicated SQL pool scales the compute resources (CPU, memory, and I/O bandwidth) available to the Synapse SQL pool. This directly reduces the execution time of batch processing jobs by allowing more parallel processing, enabling the 6-hour job to complete within the required 4-hour window.

Answer

Replace PolyBase with Azure Data Factory for data movement.

Answer

Migrate from serverless SQL pool to dedicated SQL pool.

Answer

Move the underlying data to Azure Data Lake Storage Gen2.

Question 9

A healthcare organization must build an analytics solution that processes streaming patient vitals data and provides real-time dashboards. The solution must also store historical data for compliance audits. Which combination of Azure services should the organization use?

Accepted Answer

Azure Stream Analytics for real-time processing and Azure SQL Database for historical storage and dashboards.. Azure Stream Analytics is purpose-built for real-time processing of streaming data, such as patient vitals, and can output directly to Power BI for live dashboards. Azure SQL Database provides a relational store for historical data, supporting compliance audits with point-in-time restore and long-term retention. This combination meets both real-time and historical requirements without unnecessary complexity.

Answer

Azure Synapse Analytics for real-time processing and Azure Blob Storage for archival.

Answer

Azure Event Hubs for ingestion and Azure Data Lake Storage for storage, with Power BI for dashboards.

Answer

Azure HDInsight with Apache Spark for streaming and Azure Cosmos DB for storage.

Question 10

A multinational corporation uses Azure Data Factory to orchestrate data pipelines across multiple regions. The company notices that pipeline runs in the West Europe region consistently fail due to throttling errors from the source database. The source database is an Azure SQL Database in the same region. The company needs to reduce throttling while maintaining pipeline throughput. What should the company do?

Accepted Answer

Configure auto-scale on the Azure SQL Database to handle higher load.. The throttling errors originate from the Azure SQL Database source, which has resource limits (DTU or vCore-based). Configuring auto-scale on the Azure SQL Database dynamically adjusts its performance tier to handle higher concurrency and throughput, directly addressing the root cause of throttling while maintaining pipeline throughput. This aligns with the requirement to reduce throttling without reducing workload.

Answer

Increase the maximum number of Data Factory activity retries.

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Stage extracted data in Azure Blob Storage before loading into the destination.

Answer

Use a self-hosted integration runtime in the same region to execute pipelines.

Question 11

A retail company uses Power BI to create sales reports. The data source is an Azure SQL Database that updates every 15 minutes. The reports must reflect near real-time data without manual refresh. Which Power BI feature should the company use?

Accepted Answer

Use DirectQuery mode to connect to the Azure SQL Database.. DirectQuery mode allows Power BI to query the Azure SQL Database directly without importing data, ensuring that reports reflect the current state of the database each time a report is viewed. Since the database updates every 15 minutes, DirectQuery provides near real-time data without requiring manual or scheduled refresh operations.

Answer

Use the on-premises data gateway to connect to Azure SQL Database.

Answer

Import data with scheduled refresh every 15 minutes.

Answer

Create a Power BI dataflow to transform the data.

Question 12

A company uses Azure Stream Analytics to process IoT data from thousands of devices. The output is written to Azure SQL Database for reporting. Recently, the job latency increased significantly. The company suspects that the SQL Database is throttling writes. Which action should the company take to reduce latency?

Accepted Answer

Switch the output to Azure Cosmos DB with sufficient RU/s and use change feed to sync to SQL Database.. The latency is caused by Azure SQL Database throttling writes due to its row-based storage and limited write throughput. By switching the output to Azure Cosmos DB with sufficient Request Units per second (RU/s), the Stream Analytics job can write at high speed without throttling, and the change feed can then asynchronously sync data to Azure SQL Database for reporting, decoupling the write bottleneck.

Answer

Change the input serialization from JSON to Avro.

Answer

Increase the batch size of writes to Azure SQL Database.

Answer

Increase the number of Streaming Units for the Stream Analytics job.

Question 13

A data scientist needs to perform exploratory data analysis on a large dataset stored in Azure Data Lake Storage Gen2 using Python notebooks. The solution must minimize infrastructure management. Which Azure service should the data scientist use?

Accepted Answer

Azure Databricks with collaborative notebooks.. Azure Databricks provides a fully managed, collaborative notebook environment optimized for big data analytics and machine learning. It integrates natively with Azure Data Lake Storage Gen2, allowing the data scientist to perform exploratory data analysis (EDA) using Python notebooks without managing any underlying infrastructure. This minimizes operational overhead while providing autoscaling clusters and built-in Spark capabilities.

Answer

Azure Machine Learning compute instances.

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Power BI with dataflows.

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Azure HDInsight with Jupyter notebooks.

Question 14

A company uses Azure Synapse Analytics to run both interactive queries and large batch loads. The interactive queries must have consistent performance regardless of batch load activity. Which Synapse feature should the company use?

Accepted Answer

Workload management with workload isolation.. Workload management with workload isolation in Azure Synapse Analytics allows you to reserve resources for specific workloads, such as interactive queries, ensuring they have consistent performance even when large batch loads are running. By creating a workload group with 'REQUEST_MIN_RESOURCE_PERCENT' set to a non-zero value, you guarantee a minimum amount of resources are always available for that group, preventing contention from other workloads.

Answer

Result-set caching for frequently run queries.

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Materialized views for aggregate data.

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Data compression with columnstore indexes.

Question 15

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 (E) is correct because it is a fully managed, real-time stream processing engine that ingests data from sources such as Azure Event Hubs, IoT Hub, or Blob Storage, runs continuous SQL-like queries over the streaming data, and outputs results to sinks like Power BI with sub-second to low-latency processing. Power BI (A) is correct because it provides the dashboarding and visualization layer for real-time analytics, supporting streaming datasets and push datasets that render live tiles and dashboards with low latency when fed by services such as Azure Stream Analytics. Together, Stream Analytics handles ingestion and real-time computation while Power BI delivers the low-latency dashboards, which is exactly the scenario described. Azure Data Factory (B) is a batch-oriented data integration and orchestration service, not a real-time streaming engine, so it does not fit. Azure Data Lake Storage Gen2 (C) is a scalable storage layer for batch and analytical workloads, not a streaming ingestion or dashboarding service. Azure HDInsight with Spark (D) can process streaming data via Spark Structured Streaming, but it is a cluster-based big data service rather than a purpose-built low-latency real-time analytics and dashboarding solution, and it does not itself provide dashboards.

Answer

Azure Data Factory

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Azure Data Lake Storage Gen2

Answer

Azure HDInsight with Spark