DP-900 Describe an analytics workload on Azure Practice Test 13 — 15 Questions

Question 1

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.

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Azure Machine Learning

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Azure DevOps

Question 2

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.

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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 3

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 4

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.

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Derived column transformation

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Aggregate transformation

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Window transformation

Question 5

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.

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Replace PolyBase with Azure Data Factory for data movement.

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Migrate from serverless SQL pool to dedicated SQL pool.

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Move the underlying data to Azure Data Lake Storage Gen2.

Question 6

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.

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

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

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

Question 7

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.

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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.

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

Question 8

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.

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

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Import data with scheduled refresh every 15 minutes.

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Create a Power BI dataflow to transform the data.

Question 9

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.

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Change the input serialization from JSON to Avro.

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Increase the batch size of writes to Azure SQL Database.

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

Question 10

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.

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Azure Machine Learning compute instances.

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Power BI with dataflows.

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Azure HDInsight with Jupyter notebooks.

Question 11

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.

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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 12

Which THREE components are typically part of a modern data warehouse architecture on Azure? (Choose three.)

Accepted Answer

Azure Data Factory. Azure Data Factory is correct because it serves as the cloud-based ETL (Extract, Transform, Load) service that orchestrates and automates data movement and transformation across various sources and destinations. In a modern data warehouse architecture, Data Factory is used to ingest raw data from on-premises or cloud sources, transform it using mapping data flows or external compute (e.g., Azure Databricks), and load it into the data warehouse or data lake for analytics. It provides a code-free visual interface or SDK-based control for scheduling and monitoring pipelines, making it essential for the ingestion and preparation layer.

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Azure Stream Analytics

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Azure Cosmos DB

Question 13

A data analyst needs to create a real-time dashboard in Power BI that displays streaming data from Azure Event Hubs. The data must be refreshed every second. Which Power BI feature should they use?

Accepted Answer

Streaming dataset. A is correct because Power BI's streaming dataset feature is specifically designed to handle real-time data ingestion and visualization with sub-second latency. It supports direct integration with Azure Event Hubs, allowing the dashboard to refresh every second without the need for scheduled refresh or query-based retrieval.

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Import mode with scheduled refresh

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DirectQuery

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Power BI Dataflows

Question 14

Your company has a data pipeline in Azure Data Factory that runs daily. Recently, the pipeline started failing with timeouts. You suspect a downstream database is slow. What should you do to monitor and alert on pipeline run duration?

Accepted Answer

Configure alerts in Azure Monitor based on pipeline run duration. Azure Monitor can be configured to create alerts based on metrics such as pipeline run duration. When the duration exceeds a threshold, an alert triggers, allowing proactive notification of slow downstream databases. This directly addresses the need to monitor and alert on pipeline performance issues.

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Query the pipeline runs in Log Analytics

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Check Azure Service Health for issues

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Use Azure Advisor to check performance recommendations

Question 15

A company wants to analyze customer feedback from surveys and social media. The data includes both structured (ratings) and unstructured (comments) text. They plan to use Azure Cognitive Services for sentiment analysis. Which service should they use for text analytics?

Accepted Answer

Azure AI Language. Azure AI Language (formerly part of Azure Cognitive Services) provides pre-built text analytics capabilities, including sentiment analysis, key phrase extraction, and language detection. This service is specifically designed to process unstructured text data like survey comments and social media posts, making it the correct choice for analyzing customer feedback.

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Azure Synapse Analytics

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Azure Cosmos DB

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Azure Machine Learning