RELEVANT DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER EXAM DUMPS | DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER RELIABLE EXAM TIPS

Relevant Databricks-Certified-Professional-Data-Engineer Exam Dumps | Databricks-Certified-Professional-Data-Engineer Reliable Exam Tips

Relevant Databricks-Certified-Professional-Data-Engineer Exam Dumps | Databricks-Certified-Professional-Data-Engineer Reliable Exam Tips

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To take the Databricks Databricks-Certified-Professional-Data-Engineer Exam, candidates must first complete the Databricks Certified Associate Data Analyst and Databricks Certified Associate Data Engineer exams. These exams provide a foundation of knowledge and skills that are necessary to pass the professional-level exam. Candidates must also have experience working with Databricks and be familiar with its various features and capabilities.

The DCPDE certification is an excellent way for data professionals to demonstrate their expertise in the Databricks platform. Databricks Certified Professional Data Engineer Exam certification is recognized globally and is highly valued by employers looking for data professionals with expertise in Databricks. The DCPDE certification provides professionals with the opportunity to enhance their career prospects and increase their earning potential.

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Databricks Certified Professional Data Engineer is a certification exam offered by Databricks for data engineers. Databricks-Certified-Professional-Data-Engineer Exam evaluates the ability of a candidate to design and implement data solutions using Databricks. Databricks is a unified data analytics platform that enables data teams to collaborate on data engineering, machine learning, and analytics tasks. Databricks Certified Professional Data Engineer Exam certification is designed to validate the skills and proficiency of data engineers in using Databricks for data engineering tasks.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q100-Q105):


NEW QUESTION # 100
You would like to build a spark streaming process to read from a Kafka queue and write to a Delta table every
15 minutes, what is the correct trigger option

  • A. trigger(15)

  • B. trigger(processingTime = 15)

  • C. trigger("15 minutes")

  • D. trigger(processingTime = "15 Minutes")

  • E. trigger(process "15 minutes")


Answer: D

Explanation:
Explanation
The answer is trigger(processingTime = "15 Minutes")
Triggers:
*Unspecified
This is the default. This is equivalent to using processingTime="500ms"
*Fixed interval micro-batches .trigger(processingTime="2 minutes")
The query will be executed in micro-batches and kicked off at the user-specified intervals
*One-time micro-batch .trigger(once=True)
The query will execute a single micro-batch to process all the available data and then stop on its own
*One-time micro-batch.trigger .trigger(availableNow=True) -- New feature a better version of (once=True) Databricks supports trigger(availableNow=True) in Databricks Runtime 10.2 and above for Delta Lake and Auto Loader sources. This functionality combines the batch processing approach of trigger once with the ability to configure batch size, resulting in multiple parallelized batches that give greater control for right-sizing batches and the resultant files.

 

NEW QUESTION # 101
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

  • A. workspace

  • B. libraries

  • C. configure

  • D. jobs

  • E. fs


Answer: E

Explanation:
The libraries command group allows you to install, uninstall, and list libraries on Databricks clusters. You can use the libraries install command to install a custom Python Wheel on a cluster by specifying the --whl option and the path to the wheel file. For example, you can use the following command to install a custom Python Wheel named mylib-0.1-py3-none-any.whl on a cluster with the id 1234-567890-abcde123:
databricks libraries install --cluster-id 1234-567890-abcde123 --whl
dbfs:/mnt/mylib/mylib-0.1-py3-none-any.whl
This will upload the custom Python Wheel to the cluster and make it available for use with a production job.
You can also use the libraries uninstall command to uninstall a library from a cluster, and the libraries list command to list the libraries installed on a cluster.
References:
* Libraries CLI (legacy): https://docs.databricks.com/en/archive/dev-tools/cli/libraries-cli.html
* Library operations: https://docs.databricks.com/en/dev-tools/cli/commands.html#library-operations
* Install or update the Databricks CLI: https://docs.databricks.com/en/dev-tools/cli/install.html

 

NEW QUESTION # 102
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

  • A. Cluster: Existing All-Purpose Cluster;
    Retries: None;
    Maximum Concurrent Runs: 1

  • B. Cluster: Existing All-Purpose Cluster;
    Retries: Unlimited;
    Maximum Concurrent Runs: 1

  • C. Cluster: New Job Cluster;
    Retries: Unlimited;
    Maximum Concurrent Runs: Unlimited

  • D. Cluster: New Job Cluster;
    Retries: None;
    Maximum Concurrent Runs: 1

  • E. Cluster: New Job Cluster;
    Retries: Unlimited;
    Maximum Concurrent Runs: 1


Answer: E

Explanation:
Maximum concurrent runs: Set to 1. There must be only one instance of each query concurrently active. Retries: Set to Unlimited. https://docs.databricks.com/en/structured-streaming/query-recovery.html

 

NEW QUESTION # 103
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then
perform a streaming write into a new table. The code block used by the data engineer is below:
1. (spark.table("sales")
2. .withColumn("avg_price", col("sales") / col("units"))
3. .writeStream
4. .option("checkpointLocation", checkpointPath)
5. .outputMode("complete")
6. ._____
7. .table("new_sales")
8.)
If the data engineer only wants the query to execute a single micro-batch to process all of the available data,
which of the following lines of code should the data engineer use to fill in the blank?

  • A. .processingTime("once")

  • B. .trigger(processingTime="once")

  • C. .trigger(continuous="once")

  • D. .processingTime(1)

  • E. .trigger(once=True)


Answer: E

 

NEW QUESTION # 104
Operations team is using a centralized data quality monitoring system, a user can publish data quality metrics through a webhook, you were asked to develop a process to send messages using a webhook if there is atleast one duplicate record, which of the following approaches can be taken to integrate an alert with current data quality monitoring system

  • A. Setup an alert with custom template

  • B. Setup an alert to send an email, use python to parse email, and publish a webhook message

  • C. Setup an alert with custom Webhook destination

  • D. Use notebook and Jobs to use python to publish DQ metrics

  • E. Setup an alert with dynamic template


Answer: C

Explanation:
Explanation
Alerts supports multiple destinations, email is the default destination.
Alert destinations | Databricks on AWS
Graphical user interface, application Description automatically generated


 

NEW QUESTION # 105
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