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 Databricks-Certified-Professional-Data-Engineer Dumps with Practice Exam Questions Answers

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Databricks Databricks-Certified-Professional-Data-Engineer Practice Exam FAQs

1. What is the Databricks-Certified-Professional-Data-Engineer Exam?

The Databricks-Certified-Professional-Data-Engineer certification assesses an individual’s ability to perform advanced data engineering tasks using Databricks.

2. Who should take Databricks-Certified-Professional-Data-Engineer Exam?

The Databricks-Certified-Professional-Data-Engineer exam is ideal for data engineers with experience using Databricks to design, develop, and deploy advanced data pipelines. It's also relevant for professionals seeking to demonstrate their proficiency in building secure, reliable, and scalable data lakehouse architectures.

3. What are the key topics covered in the Databricks-Certified-Professional-Data-Engineer Exam?

The Databricks-Certified-Professional-Data-Engineer exam evaluates the following areas:

  • Databricks Tooling (20%): Familiarity with Databricks tools.
  • Data Processing (30%): Ability to process data using Databricks.
  • Data Modeling (20%): Understanding of data modeling concepts.
  • Security and Governance (10%): Knowledge of securing data pipelines.
  • Monitoring and Logging (10%): Monitoring and logging best practices.
  • Testing and Deployment (10%): Ensuring reliable and tested pipelines.

4. How many questions are there on the Databricks-Certified-Professional-Data-Engineer Exam?

The Databricks-Certified-Professional-Data-Engineer exam consists of 60 questions.

5. What is the duration of the Databricks-Certified-Professional-Data-Engineer Exam?

The duration of the Databricks-Certified-Professional-Data-Engineer exam is 120 minutes.

6. What is the difference between Databricks-Certified-Professional-Data-Engineer and Databricks-Certified-Professional-Data-Scientist Exams?

Here's a breakdown of the key differences between the Databricks-Certified-Professional-Data-Engineer and Databricks-Certified-Professional-Data-Scientist Exams:

  • Databricks-Certified-Professional-Data-Engineer: The Databricks-Certified-Professional-Data-Engineer Exam assesses an individual’s ability to perform advanced data engineering tasks using Databricks. This certification is valid for 2 years, and recertification is required every two years.
  • Databricks-Certified-Professional-Data-Scientist: The Databricks-Certified-Professional-Data-Scientist Exam focuses on assessing an individual’s ability to perform data science tasks using Databricks. This certification is valid for 2 years, and recertification is required every two years.

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Databricks-Certified-Professional-Data-Engineer Questions and Answers

Question # 1

What statement is true regarding the retention of job run history?

A.

It is retained until you export or delete job run logs

B.

It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3

C.

t is retained for 60 days, during which you can export notebook run results to HTML

D.

It is retained for 60 days, after which logs are archived

E.

It is retained for 90 days or until the run-id is re-used through custom run configuration

Question # 2

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.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.

B.

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.

C.

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.

D.

Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024/512), and then write to parquet.

E.

Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.

Question # 3

In order to facilitate near real-time workloads, a data engineer is creating a helper function to 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.

Option A

B.

Option B

C.

Option C

D.

Option D

E.

Option E

Question # 4

The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.

Which approach will ensure that this requirement is met?

A.

When a database is being created, make sure that the LOCATION keyword is used.

B.

When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.

C.

When data is saved to a table, make sure that a full file path is specified alongside the Delta format.

D.

When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.

E.

When the workspace is being configured, make sure that external cloud object storage has been mounted.

Question # 5

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

A.

Use &Pip install in a notebook cell

B.

Run source env/bin/activate in a notebook setup script

C.

Install libraries from PyPi using the cluster UI

D.

Use &sh install in a notebook cell

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