DATABRICKS DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER EXAM MADE EASY: PREPAWAYTEST'S 3 USER-FRIENDLY QUESTIONS FORMATS

Databricks Databricks-Certified-Professional-Data-Engineer Exam Made Easy: PrepAwayTest's 3 User-Friendly Questions Formats

Databricks Databricks-Certified-Professional-Data-Engineer Exam Made Easy: PrepAwayTest's 3 User-Friendly Questions Formats

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Databricks Certified Professional Data Engineer is an exam designed for professionals who are willing to demonstrate their expertise in building and managing big data pipelines using Databricks. Databricks is a unified analytics platform that provides a collaborative environment for processing large-scale data. The Databricks Certified Professional Data Engineer exam validates the candidate's ability to design, build, and deploy large-scale data processing solutions using Databricks.

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.

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

NEW QUESTION # 62
While investigating a data issue in a Delta table, you wanted to review logs to see when and who updated the table, what is the best way to review this data?

  • A. Check Databricks SQL Audit logs
  • B. Review workspace audit logs
  • C. Review event logs in the Workspace
  • D. Run SQL SHOW HISTORY table_name
  • E. Run SQL command DESCRIBE HISTORY table_name

Answer: A

Explanation:
Explanation
The answer is Run SQL command DESCRIBE HISTORY table_name.
here is the sample data of how DESCRIBE HISTORY table_name looks
* +-------+-------------------+------+--------+---------+--------------------+----+--------+---------+-----------+--------
* |version| timestamp|userId|userName|operation| operationParameters|
job|notebook|clusterId|readVersion|isolationLevel|isBlindAppend| operationMetrics|
* +-------+-------------------+------+--------+---------+--------------------+----+--------+---------+-----------+--------
* | 5|2019-07-29 14:07:47| null| null| DELETE|[predicate -> ["(...|null| null| null| 4| Serializable| false|[numTotalRows -> ...|
* | 4|2019-07-29 14:07:41| null| null| UPDATE|[predicate -> (id...|null| null| null| 3| Serializable| false|[numTotalRows -> ...|
* | 3|2019-07-29 14:07:29| null| null| DELETE|[predicate -> ["(...|null| null| null| 2| Serializable| false|[numTotalRows -> ...|
* | 2|2019-07-29 14:06:56| null| null| UPDATE|[predicate -> (id...|null| null| null| 1| Serializable| false|[numTotalRows -> ...|
* | 1|2019-07-29 14:04:31| null| null| DELETE|[predicate -> ["(...|null| null| null| 0| Serializable| false|[numTotalRows -> ...|
* | 0|2019-07-29 14:01:40| null| null| WRITE|[mode -> ErrorIfE...|null| null| null| null| Serializable| true|[numFiles -> 2, n...|
+-------+-------------------+------+--------+---------+--------------------+----+--------+---------+-----------+--------------+


NEW QUESTION # 63
Which REST API call can be used to review the notebooks configured to run as tasks in a multi-task job?

  • A. /jobs/list
  • B. /jobs/runs/list
  • C. /jobs/get
  • D. /jobs/runs/get-output
  • E. /jobs/runs/get

Answer: C

Explanation:
This is the correct answer because it is the REST API call that can be used to review the notebooks configured to run as tasks in a multi-task job. The REST API is an interface that allows programmatically interacting with Databricks resources, such as clusters, jobs, notebooks, or tables. The REST API uses HTTP methods, such as GET, POST, PUT, or DELETE, to perform operations on these resources. The /jobs/get endpoint is a GET method that returns information about a job given its job ID. The information includes the job settings, such as the name, schedule, timeout, retries, email notifications, and tasks. The tasks are the units of work that a job executes. A task can be a notebook task, which runs a notebook with specified parameters; a jar task, which runs a JAR uploaded to DBFS with specified main class and arguments; or a python task, which runs a Python file uploaded to DBFS with specified parameters. A multi-task job is a job that has more than one task configured to run in a specific order or in parallel. By using the /jobs/get endpoint, one can review the notebooks configured to run as tasks in a multi-task job. Verified Reference: [Databricks Certified Data Engineer Professional], under "Databricks Jobs" section; Databricks Documentation, under "Get" section; Databricks Documentation, under "JobSettings" section.


NEW QUESTION # 64
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source table has been de-duplicated and validated, which statement describes what will occur when this code is executed?

  • A. A batch job will update the gold_customer_lifetime_sales_summary table, replacing only those rows that have different values than the current version of the table, using customer_id as the primary key.
  • B. An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.
  • C. The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.
  • D. An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.
  • E. The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.

Answer: E

Explanation:
This code is using the pyspark.sql.functions library to group the silver_customer_sales table by customer_id and then aggregate the data using the minimum sale date, maximum sale total, and sum of distinct order ids.
The resulting aggregated data is then written to the gold_customer_lifetime_sales_summary table, overwriting any existing data in that table. This is a batch job that does not use any incremental or streaming logic, and does not perform any merge or update operations. Therefore, the code will overwrite the gold table with the aggregated values from the silver table every time it is executed. References:
* https://docs.databricks.com/spark/latest/dataframes-datasets/introduction-to-dataframes-python.html
* https://docs.databricks.com/spark/latest/dataframes-datasets/transforming-data-with-dataframes.html
* https://docs.databricks.com/spark/latest/dataframes-datasets/aggregating-data-with-dataframes.html


NEW QUESTION # 65
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

  • A. Three new jobs named "Ingest new data" will be defined in the workspace, and they will each run once daily.
  • B. Three new jobs named "Ingest new data" will be defined in the workspace, but no jobs will be executed.
  • C. One new job named "Ingest new data" will be defined in the workspace, but it will not be executed.
  • D. The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.
  • E. The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.

Answer: D

Explanation:
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with a name, an existing cluster id, and a notebook task. However, it does not specify any schedule or trigger for the job execution. Therefore, three new jobs with the same name and configuration will be created in the workspace, but none of them will be executed until they are manually triggered or scheduled.
Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; [Databricks Documentation], under "Jobs API - Create" section.


NEW QUESTION # 66
Which REST API call can be used to review the notebooks configured to run as tasks in a multi-task job?

  • A. /jobs/list
  • B. /jobs/runs/list
  • C. /jobs/get
  • D. /jobs/runs/get-output
  • E. /jobs/runs/get

Answer: C

Explanation:
This is the correct answer because it is the REST API call that can be used to review the notebooks configured to run as tasks in a multi-task job. The REST API is an interface that allows programmatically interacting with Databricks resources, such as clusters, jobs, notebooks, or tables. The REST API uses HTTP methods, such as GET, POST, PUT, or DELETE, to perform operations on these resources. The /jobs/get endpoint is a GET method that returns information about a job given its job ID. The information includes the job settings, such as the name, schedule, timeout, retries, email notifications, and tasks. The tasks are the units of work that a job executes. A task can be a notebook task, which runs a notebook with specified parameters; a jar task, which runs a JAR uploaded to DBFS with specified main class and arguments; or a python task, which runs a Python file uploaded to DBFS with specified parameters. A multi-task job is a job that has more than one task configured to run in a specific order or in parallel. By using the /jobs/get endpoint, one can review the notebooks configured to run as tasks in a multi-task job. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Jobs" section; Databricks Documentation, under "Get" section; Databricks Documentation, under "JobSettings" section.


NEW QUESTION # 67
......

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