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Databricks Certified Professional Data Engineer Exam Sample Questions (Q97-Q102):

NEW QUESTION # 97
A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

  • A. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
  • B. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
  • C. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
  • D. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
  • E. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.

Answer: D

Explanation:
This is the correct answer because it explains which of the following adjustments will get a more accurate measure of how code is likely to perform in production. The adjustment is that calling display() forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results. When developing code in Databricks notebooks, one should be aware of how Spark handles transformations and actions. Transformations are operations that create a new DataFrame or Dataset from an existing one, such as filter, select, or join. Actions are operations that trigger a computation on a DataFrame or Dataset and return a result to the driver program or write it to storage, such as count, show, or save. Calling display() on a DataFrame or Dataset is also an action that triggers a computation and displays the result in a notebook cell. Spark uses lazy evaluation for transformations, which means that they are not executed until an action is called. Spark also uses caching to store intermediate results in memory or disk for faster access in subsequent actions. Therefore, calling display() forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results. To get a more accurate measure of how code is likely to perform in production, one should avoid calling display() too often or clear the cache before running each cell. Verified Reference: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Lazy evaluation" section; Databricks Documentation, under "Caching" section.


NEW QUESTION # 98
Which of the following SQL command can be used to insert or update or delete rows based on a condition to check if a row(s) exists?

  • A. UPDATE table_name
  • B. MERGE INTO table_name
  • C. INSERT INTO OVERWRITE table_name
  • D. INSERT IF EXISTS table_name
  • E. COPY INTO table_name

Answer: B

Explanation:
Explanation
here is the additional documentation for your review.
https://docs.databricks.com/spark/latest/spark-sql/language-manual/delta-merge-into.html
1.MERGE INTO target_table_name [target_alias]
2. USING source_table_reference [source_alias]
3. ON merge_condition
4. [ WHEN MATCHED [ AND condition ] THEN matched_action ] [...]
5. [ WHEN NOT MATCHED [ AND condition ] THEN not_matched_action ] [...]
6.
7.matched_action
8. { DELETE |
9. UPDATE SET * |
10. UPDATE SET { column1 = value1 } [, ...] }
11.
12.not_matched_action
13. { INSERT * |
14. INSERT (column1 [, ...] ) VALUES (value1 [, ...])


NEW QUESTION # 99
Which statement regarding stream-static joins and static Delta tables is correct?

  • A. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
  • B. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
  • C. The checkpoint directory will be used to track updates to the static Delta table.
  • D. The checkpoint directory will be used to track state information for the unique keys present in the join.
  • E. Stream-static joins cannot use static Delta tables because of consistency issues.

Answer: A

Explanation:
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch. Verified Reference: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Stream and static joins" section.


NEW QUESTION # 100
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. delayWrite("event_time", "10 minutes")
  • B. await("event_time + '10 minutes'")
  • C. slidingWindow("event_time", "10 minutes")
  • D. withWatermark("event_time", "10 minutes")
  • E. awaitArrival("event_time", "10 minutes")

Answer: D

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. Reference:
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 # 101
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property"contains_pii" = true.
The following SQL DDL statement is executed to create a new table:

Which command allows manual confirmation that these three requirements have been met?

  • A. DESCRIBE EXTENDED dev.pii test
  • B. DESCRIBE HISTORY dev.pii test
  • C. DESCRIBE DETAIL dev.pii test
  • D. SHOW TABLES dev
  • E. SHOW TBLPROPERTIES dev.pii test

Answer: A

Explanation:
This is the correct answer because it allows manual confirmation that these three requirements have been met.
The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property
"contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "DESCRIBE EXTENDED" section.


NEW QUESTION # 102
......

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