DATABRICKS-CERTIFIED-DATA-ENGINEER-PROFESSIONAL LATEST EXAM GUIDE HELP YOU PASS EXAM WITH HIGH PASS RATE - BRAINDUMPSPASS

Databricks-Certified-Data-Engineer-Professional Latest Exam Guide Help You Pass Exam with High Pass Rate - BraindumpsPass

Databricks-Certified-Data-Engineer-Professional Latest Exam Guide Help You Pass Exam with High Pass Rate - BraindumpsPass

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

  • A. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
  • B. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
  • C. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
  • D. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.

Answer: B

Explanation:
Maintaining data quality rules in a centralized Delta table allows for the reuse of these rules across multiple DLT (Delta Live Tables) pipelines. By storing these rules outside the pipeline's target schema and referencing the schema name as a pipeline parameter, the team can apply the same set of data quality checks to different tables within the pipeline. This approach ensures consistency in data quality validations and reduces redundancy in code by not having to replicate the same rules in each DLT notebook or file.


NEW QUESTION # 72
A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

  • A. Ganglia
  • B. Executor's log file
  • C. Cluster Event Log
  • D. Workspace audit logs
  • E. Driver's log file

Answer: C

Explanation:
The Cluster Event Log in Databricks will show the timeline for cluster resizing events, including details about when and why a cluster was resized (scaled up or down). This log would help the workspace administrator determine the causes of cluster scaling, whether due to many concurrent users submitting jobs or a few users running resource-intensive queries.


NEW QUESTION # 73
In order to facilitate near real-time workloads, a data engineer is creating a helper function to Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

  • A.
  • B.
  • C.
  • D. Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
  • E.

Answer: A

Explanation:
https://docs.databricks.com/en/ingestion/auto-loader/schema.html


NEW QUESTION # 74
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

  • A. Use &sh install in a notebook cell
  • B. Run source env/bin/activate in a notebook setup script
  • C. Use &Pip install in a notebook cell
  • D. Install libraries from PyPi using the cluster UI

Answer: D

Explanation:
Installing a Python package scoped at the notebook level to all nodes in the currently active cluster in Databricks can be achieved by using the Libraries tab in the cluster UI. This interface allows you to install libraries across all nodes in the cluster. While the %pip command in a notebook cell would only affect the driver node, using the cluster UI ensures that the package is installed on all nodes.


NEW QUESTION # 75
Which statement describes Delta Lake Auto Compaction?

  • A. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
    Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
  • B. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
  • C. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
  • D. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
  • E. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.

Answer: B

Explanation:
This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones. Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB. Auto Compaction only compacts files that have not been compacted previously.


NEW QUESTION # 76
......

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