A data engineering team uses Azure Databricks for complex ETL jobs. They have a critical job that must complete within a specific time window. The team is considering using a job cluster with autoscaling. What is the primary trade-off they should consider?

Autoscaling requires a fixed cluster size to be effective

Autoscaling is only available for interactive clusters, not job clusters

Autoscaling always reduces cost but may increase job failure rates

Autoscaling may add latency during cluster startup and scaling, potentially affecting the job's completion time

A global e-commerce company has a data pipeline that ingests clickstream data from web servers into Azure Data Lake Storage. The data volume spikes during sales events, and the company needs to ensure that the ingestion can handle the spikes without data loss. They also need to process the data with low latency for real-time dashboards. Which combination of services should you recommend?

Azure IoT Hub and Azure Functions

Azure Service Bus and Azure Logic Apps

Azure Data Factory with a tumbling window trigger and Azure Databricks

Azure Event Hubs with auto-inflate and Azure Stream Analytics

A company wants to implement a lakehouse architecture where they can store both raw and curated data, run SQL queries, and create reports in Power BI. They want to minimize the number of separate services to manage. Which Microsoft Fabric capability should you use?

Synapse Data Engineering and Synapse Data Science in Fabric

Synapse Data Warehouse and Power BI in Fabric

Microsoft Certified:Azure Data Fundamentals

Domain 4Objective 1

Describe Common Elements of Large-Scale Analytics DP-900 Practice Questions (Page 7)

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 31–35

31foundation · easy
A data processing solution must transform raw streaming data into a summarized format and make the results available to a real-time dashboard within seconds. Which processing consideration is most critical for this workload?
Select an answer first
32foundation · easy
A team needs to run real-time analytics on streaming data from IoT devices and produce alerts when certain thresholds are exceeded. Which Azure service is best suited for this real-time stream processing requirement?
Select an answer first
33expert · hard
A data engineering team uses Azure Databricks for complex ETL jobs. They have a critical job that must complete within a specific time window. The team is considering using a job cluster with autoscaling. What is the primary trade-off they should consider?
Select an answer first
34expert · hard
A global e-commerce company has a data pipeline that ingests clickstream data from web servers into Azure Data Lake Storage. The data volume spikes during sales events, and the company needs to ensure that the ingestion can handle the spikes without data loss. They also need to process the data with low latency for real-time dashboards. Which combination of services should you recommend?
Select an answer first
35application · medium
A company wants to implement a lakehouse architecture where they can store both raw and curated data, run SQL queries, and create reports in Power BI. They want to minimize the number of separate services to manage. Which Microsoft Fabric capability should you use?
Select an answer first

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Describe Common Elements of Large-Scale Analytics DP-900 Practice Questions (Page 7)

Questions 31–35

A data processing solution must transform raw streaming data into a summarized format and make the results available to a real-time dashboard within seconds. Which processing consideration is most critical for this workload?

A team needs to run real-time analytics on streaming data from IoT devices and produce alerts when certain thresholds are exceeded. Which Azure service is best suited for this real-time stream processing requirement?

A data engineering team uses Azure Databricks for complex ETL jobs. They have a critical job that must complete within a specific time window. The team is considering using a job cluster with autoscaling. What is the primary trade-off they should consider?

A company wants to implement a lakehouse architecture where they can store both raw and curated data, run SQL queries, and create reports in Power BI. They want to minimize the number of separate services to manage. Which Microsoft Fabric capability should you use?

Keep studying — Describe an analytics workload on Azure