Analytics workloads on Azure
Practise only this domain. 57 questions with explanations, 20 at a time.
Start analytics workloads on AzureSample questions
- A data engineering group needs a managed environment for transforming terabytes of event logs and training models, built around an open-source distributed engine. Which Azure service matches?Easy
- Microsoft folded a massively parallel warehouse service into Synapse Analytics as a dedicated pool. What was that service called before it was renamed?Easy
- A company using Microsoft Fabric wants its warehouses, lakehouses, and reports to read the same underlying files without copying data between silos. Which component provides that shared storage layer?Easy
- A machine learning team needs a shared, notebook-based environment where it can train models on very large tables using Python and Scala, then share its work with colleagues. Which Azure service should it use?Medium
- An Azure Data Factory author needs two kinds of data preparation: a visual transformation graph that runs at scale on Spark, and an interactive, spreadsheet-style cleanup experience for a business user. Which statement correctly describes these two options?Medium
- An online retailer stores orders in Azure Cosmos DB and wants near real-time sales dashboards in Azure Synapse Analytics. The checkout workload must not slow down. Which benefit does Azure Synapse Link provide?Medium
- A payments company must score card transactions for fraud in real time. Requirements: ingest millions of events per second, run a complex ML model on each event, send immediate alerts while also keeping history for later analysis, and guarantee that each transaction is recorded exactly once. Which architecture should it implement?Hard
- A company must copy nightly sales extracts from an on-premises SQL Server and several SaaS APIs into a data lake, then run a transformation step only after each copy finishes. Which Azure service coordinates these steps?Easy
- A research group must analyze several terabytes of sensor logs, which is far too large for one relational server to handle efficiently. They plan to spread the work across a cluster of machines using a distributed engine. What is this approach called?Easy