DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE EXAM LABS - EXAM DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE FLASHCARDS

Databricks-Certified-Data-Engineer-Associate Exam Labs - Exam Databricks-Certified-Data-Engineer-Associate Flashcards

Databricks-Certified-Data-Engineer-Associate Exam Labs - Exam Databricks-Certified-Data-Engineer-Associate Flashcards

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The Databricks Databricks-Certified-Data-Engineer-Associate exam is intended for data engineers, data architects, and developers who are responsible for designing, building, and maintaining data pipelines. Databricks Certified Data Engineer Associate Exam certification exam is comprised of 60 multiple-choice questions, and candidates have 90 minutes to complete the exam. Databricks-Certified-Data-Engineer-Associate Exam measures candidates' knowledge and skills in various areas, including data ingestion, data transformation, and data processing.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q59-Q64):

NEW QUESTION # 59
A data engineer has a Job with multiple tasks that runs nightly. Each of the tasks runs slowly because the clusters take a long time to start.
Which of the following actions can the data engineer perform to improve the start up time for the clusters used for the Job?

  • A. They can use endpoints available in Databricks SQL
  • B. They can configure the clusters to autoscale for larger data sizes
  • C. They can configure the clusters to be single-node
  • D. They can use jobs clusters instead of all-purpose clusters
  • E. They can use clusters that are from a cluster pool

Answer: E

Explanation:
The best action that the data engineer can perform to improve the start up time for the clusters used for the Job is to use clusters that are from a cluster pool. A cluster pool is a set of idle clusters that can be used by jobs or interactive sessions. By using a cluster pool, the data engineer can avoid the cluster creation time and reduce the latency of the tasks. Cluster pools also offer cost savings and resource efficiency, as they can be shared by multiple users and jobs.
Option A is not relevant, as endpoints available in Databricks SQL are used for creating and managing SQL analytics workloads, not for improving cluster start up time.
Option B is not correct, as jobs clusters and all-purpose clusters have similar start up times. Jobs clusters are clusters that are dedicated to run a single job and are terminated when the job is completed. All-purpose clusters are clusters that can be used for multiple purposes, such as interactive sessions, notebooks, or multiple jobs. Both types of clusters can benefit from using a cluster pool.
Option C is not advisable, as configuring the clusters to be single-node will reduce the parallelism and performance of the tasks. Single-node clusters are clusters that have only one worker node and are typically used for testing or development purposes. They are not suitable for running production jobs that require high scalability and fault tolerance.
Option E is not helpful, as configuring the clusters to autoscale for larger data sizes will not affect the start up time of the clusters. Autoscaling is a feature that allows clusters to dynamically adjust the number of worker nodes based on the workload. It can help optimize the resource utilization and cost efficiency of the clusters, but it does not speed up the cluster creation process.
Reference:
Cluster Pools
Jobs
Clusters
[Databricks Data Engineer Professional Exam Guide]


NEW QUESTION # 60
A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?

  • A. The OPTIMIZE command was nun on the table
  • B. The VACUUM command was run on the table
  • C. The DELETE HISTORY command was run on the table
  • D. The TIME TRAVEL command was run on the table
  • E. The HISTORY command was run on the table

Answer: C


NEW QUESTION # 61
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

  • A. autoloader
  • B. sqlite
  • C. DELTA
  • D. org.apache.spark.sql.sqlite
  • E. org.apache.spark.sql.jdbc

Answer: B

Explanation:
In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is "sqlite" because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, "sqlite" would be appropriate here. Reference:
Create a table using JDBC
JDBC connection string
SQLite JDBC driver


NEW QUESTION # 62
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Development mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

  • A. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
  • B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.
  • C. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
  • D. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
  • E. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.

Answer: A

Explanation:
Explanation
You can optimize pipeline execution by switching between development and production modes. Use the Delta Live Tables Environment Toggle Icon buttons in the Pipelines UI to switch between these two modes. By default, pipelines run in development mode.
When you run your pipeline in development mode, the Delta Live Tables system does the following:
Reuses a cluster to avoid the overhead of restarts. By default, clusters run for two hours when development mode is enabled. You can change this with the pipelines.clusterShutdown.delay setting in the Configure your compute settings.
Disables pipeline retries so you can immediately detect and fix errors.
In production mode, the Delta Live Tables system does the following:
Restarts the cluster for specific recoverable errors, including memory leaks and stale credentials.
Retries execution in the event of specific errors, for example, a failure to start a cluster.
https://docs.databricks.com/en/delta-live-tables/updates.html#optimize-execution


NEW QUESTION # 63
A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.
Which of the following describes why Auto Loader inferred all of the columns to be of the string type?

  • A. All of the fields had at least one null value
  • B. Auto Loader cannot infer the schema of ingested data
  • C. Auto Loader only works with string data
  • D. JSON data is a text-based format
  • E. There was a type mismatch between the specific schema and the inferred schema

Answer: D


NEW QUESTION # 64
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