TEST DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER QUESTIONS FEE, DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER VALID TEST PATTERN

Test Databricks-Certified-Professional-Data-Engineer Questions Fee, Databricks-Certified-Professional-Data-Engineer Valid Test Pattern

Test Databricks-Certified-Professional-Data-Engineer Questions Fee, Databricks-Certified-Professional-Data-Engineer Valid Test Pattern

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Databricks Certified Professional Data Engineer exam is a comprehensive assessment that covers a wide range of topics related to data engineering using Databricks. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and performance-based tasks that require candidates to demonstrate their ability to design, build, and optimize data pipelines using Databricks. Databricks-Certified-Professional-Data-Engineer exam is available online and can be taken from anywhere in the world, making it a convenient option for data professionals who want to validate their expertise in Databricks. Upon successful completion of the exam, candidates will receive a Databricks Certified Professional Data Engineer certification, which will demonstrate their proficiency in data engineering using Databricks.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q62-Q67):

NEW QUESTION # 62
Which Python variable contains a list of directories to be searched when trying to locate required modules?

  • A. ,sys.path
  • B. pylib.source
  • C. os-path
  • D. pypi.path
  • E. importlib.resource path

Answer: A


NEW QUESTION # 63
The marketing team is launching a new campaign to monitor the performance of the new campaign for the first two weeks, they would like to set up a dashboard with a refresh schedule to run every 5 minutes, which of the below steps can be taken to reduce of the cost of this refresh over time?

  • A. Reduce the max size of auto scaling from 10 to 5
  • B. Reduce the size of the SQL Cluster size
  • C. Always use X-small cluster
  • D. Setup the dashboard refresh schedule to end in two weeks
  • E. Change the spot instance policy from reliability optimized to cost optimized

Answer: D

Explanation:
Explanation
The answer is Setup the dashboard refresh schedule to end in two weeks


NEW QUESTION # 64
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property"contains_pii" = true.
The following SQL DDL statement is executed to create a new table:

Which command allows manual confirmation that these three requirements have been met?

  • A. DESCRIBE EXTENDED dev.pii test
  • B. SHOW TABLES dev
  • C. DESCRIBE HISTORY dev.pii test
  • D. SHOW TBLPROPERTIES dev.pii test
  • E. DESCRIBE DETAIL dev.pii test

Answer: A

Explanation:
Explanation
This is the correct answer because it allows manual confirmation that these three requirements have been met.
The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property
"contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "DESCRIBE EXTENDED" section.


NEW QUESTION # 65
Data science team members are using a single cluster to perform data analysis, although cluster size was chosen to handle multiple users and auto-scaling was enabled, the team realized queries are still running slow, what would be the suggested fix for this?

  • A. Setup multiple clusters so each team member has their own cluster
  • B. Increase the size of the driver node
  • C. Use High concurrency mode instead of the standard mode
  • D. Disable the auto-scaling feature

Answer: C

Explanation:
Explanation
The answer is Use High concurrency mode instead of the standard mode,
https://docs.databricks.com/clusters/cluster-config-best-practices.html#cluster-mode High Concurrency clusters are ideal for groups of users who need to share resources or run ad-hoc jobs.
Databricks recommends enabling autoscaling for High Concurrency clusters.


NEW QUESTION # 66
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

  • A. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
  • B. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024/512), and then write to parquet.
  • C. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
  • D. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
  • E. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.

Answer: A


NEW QUESTION # 67
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