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

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Last Update: Nov 22, 2024
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Databricks-Certified-Professional-Data-Engineer
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The Databricks Databricks-Certified-Professional-Data-Engineer exam is ideal for experienced data engineers seeking to validate their proficiency in building and managing advanced data pipelines on the Databricks Lakehouse Platform. If you possess strong skills in Apache Spark, Delta Lake, MLflow, and Databricks tools, this certification will showcase your expertise to potential employers.
The Databricks Databricks-Certified-Professional-Data-Engineer Exam is a proctored certification exam that consists of 60 multiple-choice questions. The time limit for the exam is 120 minutes.
There are no prerequisites for taking the Databricks Databricks-Certified-Professional-Data-Engineer Exam. However, related training is highly recommended.

The Databricks Databricks-Certified-Professional-Data-Engineer Exam assesses an individual’s ability to use Databricks to perform advanced data engineering tasks. The exam covers:

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  • Data Processing (30%)
  • Data Modeling (20%)
  • Security and Governance (10%)
  • Monitoring and Logging (10%)
  • Testing and Deployment (10%)
  • The Databricks-Certified-Professional-Data-Engineer Exam assesses an individual’s ability to use Databricks to perform advanced data engineering tasks, including building optimized and cleaned ETL pipelines, modeling data into a lakehouse, and ensuring that data pipelines are secure, reliable, monitored, and tested before deployment. The exam covers Databricks Tooling, Data Processing, Data Modeling, Security and Governance, Monitoring and Logging, and Testing and Deployment.
  • The Databricks-Certified-Professional-Data-Scientist Exam assesses an individual’s ability to use Databricks to perform advanced data science tasks, including building and deploying machine learning models, using MLflow to track experiments, and using Delta Lake to manage data. The exam covers Databricks Tooling, Data Processing, Machine Learning, and Data Management.

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Databricks Certified Data Engineer Professional Exam Questions and Answers

Questions 1

A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.

Streaming DataFrame df has the following schema:

"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"

Code block:

Databricks-Certified-Professional-Data-Engineer Question 1

Choose the response that correctly fills in the blank within the code block to complete this task.

Options:

A.

to_interval("event_time", "5 minutes").alias("time")

B.

window("event_time", "5 minutes").alias("time")

C.

"event_time"

D.

window("event_time", "10 minutes").alias("time")

E.

lag("event_time", "10 minutes").alias("time")

Questions 2

The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:

Databricks-Certified-Professional-Data-Engineer Question 2

The table daily_store_sales contains all the information needed to update store_sales_summary. The schema for this table is:

store_id INT, sales_date DATE, total_sales FLOAT

If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?

Options:

A.

Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.

B.

Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.

C.

Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.

D.

Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.

E.

Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.

Questions 3

Which distribution does Databricks support for installing custom Python code packages?

Options:

A.

sbt

B.

CRAN

C.

CRAM

D.

nom

E.

Wheels

F.

jars

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