ExpertMedium1 markMultiple Choice
AZ-305 · Question 28 · Domain 2.4: Non-Relational Data Storage
Your company is building a centralized logging and analytics platform for a fleet of autonomous drones.
The drones generate 5 TB of time-series log data daily. Data scientists need to run complex, ad-hoc analytical queries over billions of rows of this telemetry data to identify flight anomalies. The queries must return results in seconds.
Which TWO Azure services are best suited for storing and querying this massive volume of time-series log data? (Select TWO)
Your company is building a centralized logging and analytics platform for a fleet of autonomous drones.
The drones generate 5 TB of time-series log data daily. Data scientists need to run complex, ad-hoc analytical queries over billions of rows of this telemetry data to identify flight anomalies. The queries must return results in seconds.
Which TWO Azure services are best suited for storing and querying this massive volume of time-series log data? (Select TWO)
Answer options:
A.
Azure Cosmos DB
B.
Azure Data Explorer
C.
Azure Cache for Redis
D.
Kusto Query Language (KQL)
E.
Azure Table Storage
How to approach this question
Identify the Azure service purpose-built for time-series log analytics and its associated query language.
Full Answer
Azure Data Explorer, Kusto Query Language (KQL)
Azure Data Explorer (ADX) is a highly scalable data exploration service designed specifically for log and telemetry data. It excels at handling massive volumes of append-only, time-series data and allows for ad-hoc querying with sub-second latency. To query data in ADX, you use the Kusto Query Language (KQL), which is optimized for searching and analyzing text and time-series data. Cosmos DB is too expensive for this volume of append-only logs, and Table Storage cannot do complex analytics.
Common mistakes
Selecting Cosmos DB. While it handles JSON well, ADX is the architectural standard for high-volume time-series telemetry analytics.
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