ExpertHard1 markMultiple Choice
AZ-305 · Question 28 · Domain 2.4: Non-relational data storage
A manufacturing company has 5,000 factory machines equipped with sensors. The sensors generate 10 TB of time-series telemetry data per day.
Data analysts need to perform interactive, ad-hoc queries over the last 6 months of data to identify anomaly patterns. The queries often involve complex aggregations, time-windowing, and joining massive datasets. The solution must provide sub-second query performance.
Which TWO Azure services should you combine to ingest and analyze this data? (Select TWO)
A manufacturing company has 5,000 factory machines equipped with sensors. The sensors generate 10 TB of time-series telemetry data per day.
Data analysts need to perform interactive, ad-hoc queries over the last 6 months of data to identify anomaly patterns. The queries often involve complex aggregations, time-windowing, and joining massive datasets. The solution must provide sub-second query performance.
Which TWO Azure services should you combine to ingest and analyze this data? (Select TWO)
Answer options:
A.
Azure Event Hubs
B.
Azure Cosmos DB Table API
C.
Azure Data Explorer
D.
Azure SQL Database
E.
Azure Cache for Redis
How to approach this question
Identify the ingestion service for high-throughput telemetry (Event Hubs) and the analytics engine optimized for time-series data (Data Explorer).
Full Answer
Azure Event Hubs is the ideal ingestion service for high-throughput streaming data from IoT sensors. Azure Data Explorer (ADX) is a highly scalable data exploration service for log and telemetry data. It natively integrates with Event Hubs and uses KQL to perform lightning-fast, complex aggregations and time-windowing queries over massive time-series datasets.
Common mistakes
Selecting Cosmos DB. While Cosmos DB is fast, ADX is specifically purpose-built and vastly more cost-effective for append-only time-series analytics.
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