DATABRICKS-CERTIFIED-DATA-ENGINEER-PROFESSIONAL ACTUAL DUMP, RELIABLE DATABRICKS-CERTIFIED-DATA-ENGINEER-PROFESSIONAL DUMPS PPT

Databricks-Certified-Data-Engineer-Professional Actual Dump, Reliable Databricks-Certified-Data-Engineer-Professional Dumps Ppt

Databricks-Certified-Data-Engineer-Professional Actual Dump, Reliable Databricks-Certified-Data-Engineer-Professional Dumps Ppt

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
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. lag("event_time", "10 minutes").alias("time")
  • B. to_interval("event_time", "5 minutes").alias("time")
  • C. "event_time"
  • D. window("event_time", "5 minutes").alias("time")
  • E. window("event_time", "10 minutes").alias("time")

Answer: D

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 # 17
Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?

  • A. Stage's detail screen and Query's detail screen
  • B. Query's detail screen and Job's detail screen
  • C. Stage's detail screen and Executor's log files
  • D. Driver's and Executor's log files
  • E. Executor's detail screen and Executor's log files

Answer: C

Explanation:
In the Spark UI, the Stage's detail screen provides key metrics about each stage of a job, including the amount of data that has been spilled to disk. If you see a high number in the "Spill (Memory)" or "Spill (Disk)" columns, it's an indication that a partition is spilling to disk.
The Executor's log files can also provide valuable information about spill. If a task is spilling a lot of data, you'll see messages in the logs like "Spilling UnsafeExternalSorter to disk" or "Task memory spill". These messages indicate that the task ran out of memory and had to spill data to disk.


NEW QUESTION # 18
Which Python variable contains a list of directories to be searched when trying to locate required modules?

  • A. sys.path
  • B. os-path
  • C. pypi.path
  • D. pylib.source
  • E. importlib.resource path

Answer: A

Explanation:
sys. path is a built-in variable within the sys module. It contains a list of directories that the interpreter will search in for the required module.


NEW QUESTION # 19
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic. What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

  • A. Can run
  • B. Can edit
  • C. Can manage
  • D. Can Read

Answer: D

Explanation:
Granting a user 'Can Read' permissions on a notebook within Databricks allows them to view the notebook's content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook's code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.


NEW QUESTION # 20
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion.
However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

  • A. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
  • B. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
  • C. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
  • D. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
  • E. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.

Answer: A

Explanation:
By default partitionning by a column will create a separate folder for each subset data linked to the partition.


NEW QUESTION # 21
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