A company needs to build a real-time dashboard that shows sales metrics. They also need to store the raw data for historical analysis. They are considering Azure Stream Analytics and Azure Data Factory. What is the key trade-off?

Stream Analytics provides real-time processing but is not designed for batch historical loads

Data Factory provides real-time processing but is more expensive

Data Factory is easier to use for real-time dashboards

Stream Analytics can replace Data Factory for all data movement

A company wants to use Microsoft Fabric to enable their analysts to create reports and dashboards directly from data stored in OneLake, without moving data to a separate BI tool. Which Fabric workload should they use?

Synapse Data Engineering in Fabric

A logistics company needs to process incoming sensor data from delivery vehicles to detect anomalies in near real-time. The data arrives continuously, and the company wants to trigger alerts when anomalies are detected. Which processing approach should you recommend?

Interactive querying with Azure Synapse Serverless SQL

Batch processing with Azure Data Factory on a nightly schedule

Stream processing with Azure Stream Analytics

Lambda architecture with a batch layer only

A retail chain wants to analyze customer purchase data from both online and in-store transactions. The online data is streaming, while the in-store data is batch. They need to combine these data sources for a unified view. They also want to use machine learning to predict customer churn. Which architecture should you recommend?

Use Azure Event Hubs for streaming online data, Azure Data Factory for batch in-store data, and Azure Databricks for processing and ML.

Use Azure Stream Analytics for both online and in-store data, and Azure Machine Learning for churn prediction.

Use Azure Data Factory for both online and in-store data, and Azure Synapse Analytics for ML.

Use Azure IoT Hub for online data, Azure Data Lake Storage for in-store data, and Azure Analysis Services for ML.

Microsoft Certified:Azure Data Fundamentals

Domain 4Objective 1

Describe Common Elements of Large-Scale Analytics DP-900 Practice Questions (Page 5)

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 21–25

21expert · hard
A company needs to build a real-time dashboard that shows sales metrics. They also need to store the raw data for historical analysis. They are considering Azure Stream Analytics and Azure Data Factory. What is the key trade-off?
Select an answer first
22application · medium
A company wants to use Microsoft Fabric to enable their analysts to create reports and dashboards directly from data stored in OneLake, without moving data to a separate BI tool. Which Fabric workload should they use?
Select an answer first
23application · medium
A logistics company needs to process incoming sensor data from delivery vehicles to detect anomalies in near real-time. The data arrives continuously, and the company wants to trigger alerts when anomalies are detected. Which processing approach should you recommend?
Select an answer first
24foundation · easy
An organization wants to unify its data engineering, data science, and business intelligence workloads in a single platform that provides a lakehouse architecture with OneLake storage. Which Microsoft service provides this capability?
Select an answer first
25expert · hard
A retail chain wants to analyze customer purchase data from both online and in-store transactions. The online data is streaming, while the in-store data is batch. They need to combine these data sources for a unified view. They also want to use machine learning to predict customer churn. Which architecture should you recommend?
Select an answer first

Finished these 5 questions?

Review the revealed explanations, or continue through the curriculum.

Next 5 questions →Back to DP-900

Free Basic Practice is a study aid with revealable answers — not a scored exam. Examers.io is independent and not affiliated with or endorsed by Microsoft. “DP-900” is a trademark of its owner, used for identification only.

Describe Common Elements of Large-Scale Analytics DP-900 Practice Questions (Page 5)

Questions 21–25

A company needs to build a real-time dashboard that shows sales metrics. They also need to store the raw data for historical analysis. They are considering Azure Stream Analytics and Azure Data Factory. What is the key trade-off?

A company wants to use Microsoft Fabric to enable their analysts to create reports and dashboards directly from data stored in OneLake, without moving data to a separate BI tool. Which Fabric workload should they use?

A logistics company needs to process incoming sensor data from delivery vehicles to detect anomalies in near real-time. The data arrives continuously, and the company wants to trigger alerts when anomalies are detected. Which processing approach should you recommend?

An organization wants to unify its data engineering, data science, and business intelligence workloads in a single platform that provides a lakehouse architecture with OneLake storage. Which Microsoft service provides this capability?

Keep studying — Describe an analytics workload on Azure