EASY TO USE FORMATS OF TOPEXAMCOLLECTION DATABRICKS DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER PRACTICE EXAM MATERIAL

Easy to use Formats of TopExamCollection Databricks Databricks-Certified-Professional-Data-Engineer Practice Exam Material

Easy to use Formats of TopExamCollection Databricks Databricks-Certified-Professional-Data-Engineer Practice Exam Material

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q118-Q123):

NEW QUESTION # 118
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. Incremental state information should be maintained for 10 minutes for late- arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

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

  • A. slidingWindow("event_time", "10 minutes")
  • B. withWatermark("event_time", "10 minutes")
  • C. awaitArrival("event_time", "10 minutes")
  • D. await("event_time + '10 minutes'")
  • E. delayWrite("event_time", "10 minutes")

Answer: B

Explanation:
The correct answer is A. withWatermark("event_time", "10 minutes"). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The other options are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. References:
* Watermarking: https://docs.databricks.com/spark/latest/structured-streaming/watermarks.html
* Windowed aggregations: https://docs.databricks.com/spark/latest/structured-streaming/window- operations.html


NEW QUESTION # 119
A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

  • A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
  • B. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
  • C. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
  • D. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
  • E. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.

Answer: D


NEW QUESTION # 120
A team member is leaving the team and he/she is currently the owner of the few tables, instead of transfering the ownership to a user you have decided to transfer the ownership to a group so in the future anyone in the group can manage the permissions rather than a single individual, which of the following commands help you accomplish this?

  • A. GRANT OWNER On table_name to 'group'
  • B. TRANSFER OWNER table_name to 'group'
  • C. GRANT OWNER table_name to 'group'*
  • D. ALTER TABLE table_name OWNER to 'group'
  • E. ALTER OWNER ON table_name to 'group'

Answer: D

Explanation:
Explanation
The answer is ALTER TABLE table_name OWNER to 'group'
Assign owner to object


NEW QUESTION # 121
Which of the following commands will return records from an existing Delta table my_table where duplicates
have been removed?

  • A. 1. SELECT DISTINCT *
    2. FROM my_table;
  • B. 1. MERGE INTO my_table a
    2. USING new_records b ON a.id = b.id
    3. WHEN NOT MATCHED
    4. THEN INSERT *;
  • C. 1. SELECT *
    2. FROM my_table
    3. WHERE duplicate = False;
  • D. 1. DROP DUPLICATES
    2. FROM my_table;
  • E. 1. MERGE INTO my_table a
    2. USING new_records b;

Answer: A


NEW QUESTION # 122
Data engineering team has a job currently setup to run a task load data into a reporting table every day at 8: 00 AM takes about 20 mins, Operations teams are planning to use that data to run a second job, so they access latest complete set of data. What is the best to way to orchestrate this job setup?

  • A. Add Operation reporting task in the same job and set the Data Engineering task to de-pend on Operations reporting task
  • B. Add Operation reporting task in the same job and set the operations reporting task to depend on Data Engineering task
  • C. Setup a second job to run at 8:20 AM in the same workspace
  • D. Use Auto Loader to run every 20 mins to read the initial table and set the trigger to once and create a second job
  • E. Setup a Delta live to table based on the first table, set the job to run in continuous mode

Answer: B

Explanation:
Explanation
The answer is Add Operation reporting task in the same job and set the operations reporting task to depend on Data Engineering task.

Diagram Description automatically generated with medium confidence


NEW QUESTION # 123
......

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