A retail company streams point-of-sale transactions from thousands of stores into Azure Event Hubs. The data is then processed and loaded into an Azure Synapse Analytics dedicated SQL pool for reporting. The company now wants to include historical clickstream logs that arrive in large batches every night. Which approach should you recommend to integrate the clickstream data into the existing analytics pipeline?

Send the clickstream logs to Event Hubs and process them with the same streaming pipeline as the point-of-sale data.

Use Azure Data Factory to copy the clickstream logs to Azure Data Lake Storage, then transform and load them into the dedicated SQL pool on a schedule.

Load the clickstream logs directly into the dedicated SQL pool using PolyBase, bypassing any intermediate processing.

Store the clickstream logs in Azure Blob Storage and query them directly from Power BI without loading into the SQL pool.

A company currently uses Azure Data Lake Storage, Azure Synapse Analytics, and Power BI separately. They want to migrate to a more integrated solution that reduces the need for custom data movement between services. They also want to keep using their existing Power BI reports. Which Microsoft Fabric capability should they adopt?

OneLake and Synapse Data Warehouse in Fabric

Microsoft Certified:Azure Data Fundamentals

Domain 4Objective 1

Describe Common Elements of Large-Scale Analytics DP-900 Practice Questions (Page 4)

Part of the Describe an analytics workload on Azure domain, which accounts for 25–30% of the DP-900 exam. Microsoft does not publish an official question count, but from its 45-minute exam (~20–30 total, ~5–9 in this domain), expect 2–3 from this objective — we provide 35 practice questions to prepare you well beyond it. (estimate)

35questions here

7free pages

6concepts

25–30%of the exam

Questions 16–20

16foundation · easy
A business intelligence team needs to run complex SQL queries on structured, cleansed data to produce daily sales reports. The data is already transformed and loaded into a schema-optimized store. Which type of analytical data store is best suited for this workload?
Select an answer first
17foundation · easy
An analytics solution must ingest data from a relational database, JSON files from an API, and images from a content management system. Which characteristic of big data does this scenario best illustrate?
Select an answer first
18application · medium
A retail company streams point-of-sale transactions from thousands of stores into Azure Event Hubs. The data is then processed and loaded into an Azure Synapse Analytics dedicated SQL pool for reporting. The company now wants to include historical clickstream logs that arrive in large batches every night. Which approach should you recommend to integrate the clickstream data into the existing analytics pipeline?
Select an answer first
19expert · hard
A company currently uses Azure Data Lake Storage, Azure Synapse Analytics, and Power BI separately. They want to migrate to a more integrated solution that reduces the need for custom data movement between services. They also want to keep using their existing Power BI reports. Which Microsoft Fabric capability should they adopt?
Select an answer first
20foundation · easy
A data science team wants to use Apache Spark to process large datasets and build machine learning models in a collaborative environment. Which Azure service provides a managed Apache Spark platform with integrated notebooks and MLflow?
Select an answer first

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Describe Common Elements of Large-Scale Analytics DP-900 Practice Questions (Page 4)

Questions 16–20

A business intelligence team needs to run complex SQL queries on structured, cleansed data to produce daily sales reports. The data is already transformed and loaded into a schema-optimized store. Which type of analytical data store is best suited for this workload?

An analytics solution must ingest data from a relational database, JSON files from an API, and images from a content management system. Which characteristic of big data does this scenario best illustrate?

A data science team wants to use Apache Spark to process large datasets and build machine learning models in a collaborative environment. Which Azure service provides a managed Apache Spark platform with integrated notebooks and MLflow?

Keep studying — Describe an analytics workload on Azure