Explain Considerations of Model-Driven Telemetry (including Data Consumption and Data Storage) 350-901 Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   Which protocol is specifically designed for streaming telemetry data using gRPC and supports both periodic and on-change subscriptions?

   • ASNMP
   • BNETCONF
   • CRESTCONF
   • DgNMI
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2. 17expert · hard

   A network automation team is using telemetry to detect anomalies and trigger automated remediation. They have a stream processing platform that analyzes the data. They need to ensure that the remediation actions are only triggered when a condition persists for a certain duration, to avoid false positives. Which processing approach should they use?

   • AStore the data in a time-series database and run a batch job every hour to check for high values and trigger remediation.
   • BUse a simple threshold in the collector to trigger a webhook when a value exceeds a limit.
   • CUse a windowed aggregation in the stream processing platform to count the number of consecutive high-value readings over a time window, and trigger only if the count exceeds a threshold.
   • DTrigger remediation immediately on the first high-value reading.
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4. 18foundation · easy

   What is the purpose of a retention policy in telemetry data storage?

   • ATo determine the encoding format for stored telemetry data.
   • BTo define how long telemetry data is kept before it is deleted or aggregated.
   • CTo specify which telemetry data is streamed from devices.
   • DTo ensure telemetry data is encrypted at rest.
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5. 19foundation · easy

   What is the purpose of triggering automated actions based on telemetry data analysis?

   • ATo eliminate the need for human network administrators.
   • BTo ensure that all telemetry data is stored permanently.
   • CTo enable rapid response to network conditions without manual intervention.
   • DTo replace the need for network monitoring dashboards.
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6. 20expert · hard

   A company is building a telemetry pipeline that must handle sudden spikes in data volume from thousands of devices. They need to ensure that no data is lost during spikes and that the data can be replayed if a downstream system fails. They also want to perform real-time analytics on the data. Which architecture should they use?

   • AStream telemetry to Kafka, use Kafka Streams for real-time analytics, and store the raw data in Kafka with a retention period.
   • BStream telemetry to a message queue like RabbitMQ, and have a separate analytics platform consume from the queue.
   • CStream telemetry directly to a time-series database and use SQL queries for real-time analytics.
   • DStream telemetry to a file system and use a batch processing framework like Hadoop for analytics.
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