VALID DATABRICKS-CERTIFIED-DATA-ENGINEER-PROFESSIONAL EXAM DUMPS ENSURE YOU A HIGH DATABRICKS-CERTIFIED-DATA-ENGINEER-PROFESSIONAL PASSING RATE

Valid Databricks-Certified-Data-Engineer-Professional exam dumps ensure you a high Databricks-Certified-Data-Engineer-Professional passing rate

Valid Databricks-Certified-Data-Engineer-Professional exam dumps ensure you a high Databricks-Certified-Data-Engineer-Professional passing rate

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

  • A. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
  • B. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
  • C. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
  • D. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations

Answer: D

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade- off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job.


NEW QUESTION # 20
A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?

  • A. All records from all columns will be displayed with the values in user_ltv.
  • B. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
  • C. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
  • D. All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
  • E. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.

Answer: B

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Explanation:
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.


NEW QUESTION # 21
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:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

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

  • A. window("event_time", "10 minutes").alias("time")
  • B. "event_time"
  • C. to_interval("event_time", "5 minutes").alias("time")
  • D. lag("event_time", "10 minutes").alias("time")
  • E. window("event_time", "5 minutes").alias("time")

Answer: E

Explanation:
This is the correct answer because the window function is used to group streaming data by time intervals. The window function takes two arguments: a time column and a window duration. The window duration specifies how long each window is, and must be a multiple of 1 second. In this case, the window duration is "5 minutes", which means each window will cover a non-overlapping five- minute interval. The window function also returns a struct column with two fields: start and end, which represent the start and end time of each window. The alias function is used to rename the struct column as "time".


NEW QUESTION # 22
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

  • A. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
  • B. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.
  • C. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
  • D. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
  • E. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.

Answer: D

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/delta/vacuum


NEW QUESTION # 23
A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?

  • A. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
  • B. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table
  • C. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.
  • D. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table

Answer: D

Explanation:
To validate that all records from the source are included in the derived table, creating a view that performs a left outer join between the validation_copy table and the report table is effective. The view can highlight any discrepancies, such as null values in the report table's key columns, indicating missing records. This view can then be referenced in DLT (Delta Live Tables) expectations for the report table to ensure data integrity. This approach allows for a comprehensive comparison between the source and the derived table.


NEW QUESTION # 24
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