DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE TESTKING EXAM QUESTIONS - RELIABLE DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE EXAM QUESTION

Databricks-Certified-Data-Engineer-Associate Testking Exam Questions - Reliable Databricks-Certified-Data-Engineer-Associate Exam Question

Databricks-Certified-Data-Engineer-Associate Testking Exam Questions - Reliable Databricks-Certified-Data-Engineer-Associate Exam Question

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q57-Q62):

NEW QUESTION # 57
Which of the following is stored in the Databricks customer's cloud account?

  • A. Cluster management metadata
  • B. Notebooks
  • C. Data
  • D. Databricks web application
  • E. Repos

Answer: C


NEW QUESTION # 58
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

  • A. Replace format("delta") with format("stream")
  • B. Replace predict with a stream-friendly prediction function
  • C. Replace "transactions" with the path to the location of the Delta table
  • D. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
  • E. Replace spark.read with spark.readStream

Answer: E

Explanation:
Explanation
https://docs.databricks.com/en/structured-streaming/delta-lake.html


NEW QUESTION # 59
In order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing, which of the following two approaches is used by Spark to record the offset range of the data being processed in each trigger?

  • A. Checkpointing and Idempotent Sinks
  • B. Replayable Sources and Idempotent Sinks
  • C. Checkpointing and Write-ahead Logs
  • D. Write-ahead Logs and Idempotent Sinks
  • E. Structured Streaming cannot record the offset range of the data being processed in each trigger.

Answer: C

Explanation:
Structured Streaming uses checkpointing and write-ahead logs to record the offset range of the data being processed in each trigger. This ensures that the engine can reliably track the exact progress of the processing and handle any kind of failure by restarting and/or reprocessing. Checkpointing is the mechanism of saving the state of a streaming query to fault-tolerant storage (such as HDFS) so that it can be recovered after a failure. Write-ahead logs are files that record the offset range of the data being processed in each trigger and are written to the checkpoint location before the processing starts. These logs are used to recover the query state and resume processing from the last processed offset range in case of a failure. Reference: Structured Streaming Programming Guide, Fault Tolerance Semantics


NEW QUESTION # 60
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when it is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

  • A. They can reduce the cluster size of the SQL endpoint.
  • B. They can set up the dashboard's SQL endpoint to be serverless.
  • C. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
  • D. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
  • E. They can turn on the Auto Stop feature for the SQL endpoint.

Answer: E


NEW QUESTION # 61
Which of the following is hosted completely in the control plane of the classic Databricks architecture?

  • A. Databricks web application
  • B. Worker node
  • C. Driver node
  • D. JDBC data source
  • E. Databricks Filesystem

Answer: A

Explanation:
The Databricks web application is the user interface that allows you to create and manage workspaces, clusters, notebooks, jobs, and other resources. It is hosted completely in the control plane of the classic Databricks architecture, which includes the backend services that Databricks manages in your Databricks account. The other options are part of the compute plane, which is where your data is processed by compute resources such as clusters. The compute plane is in your own cloud account and network.
References: Databricks architecture overview, Security and Trust Center


NEW QUESTION # 62
......

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