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Databricks Certified Professional Data Engineer Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day.
At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?

A. Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.
B. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.
C. Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.
D. The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.
E. Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.

Answer: A

Explanation:
Explanation
This is the correct answer because it can meet the requirement of processing records in less than 10 seconds without modifying the checkpoint directory or dropping records. The trigger once option is a special type of trigger that runs the streaming query only once and terminates after processing all available data. This option can be useful for scenarios where you want to run streaming queries on demand or periodically, rather than continuously. By using the trigger once option and configuring a Databricks job to execute the query every 10 seconds, you can ensure that all backlogged records are processed with each batch and avoid inconsistent execution times. Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Trigger Once" section.

NEW QUESTION # 90
You are asked to debug a databricks job that is taking too long to run on Sunday's, what are the steps you are going to take to identify the step that is taking longer to run?

A. Once a job is launched, you cannot access the job's notebook activity.
B. Enable debug mode in the Jobs to see the output activity of a job, output should be available to view.
C. Under Workflow UI and jobs select job you want to monitor and select the run, notebook activity can be viewed.
D. A notebook activity of job run is only visible when using all-purpose cluster.
E. Use the compute's spark UI to monitor the job activity.

Answer: C

Explanation:
Explanation
The answer is, Under Workflow UI and jobs select job you want to monitor and select the run, notebook activity can be viewed.
You have the ability to view current active runs or completed runs, once you click the run you can see the A picture containing graphical user interface Description automatically generated

Click on the run to view the notebook output
Graphical user interface, text, application, email Description automatically generated

NEW QUESTION # 91
The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
B. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.
C. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
D. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
E. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.

Answer: B

Explanation:
The code uses the DELETE FROM command to delete records from the users table that match a condition based on a join with another table called delete_requests, which contains all users that have requested deletion.
The DELETE FROM command deletes records from a Delta Lake table by creating a new version of the table that does not contain the deleted records. However, this does not guarantee that the records to be deleted are no longer accessible, because Delta Lake supports time travel, which allows querying previous versions of the table using a timestamp or version number. Therefore, files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files from physical storage.
Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delete from a table" section; Databricks Documentation, under "Remove files no longer referenced by a Delta table" section.

NEW QUESTION # 92
You are currently asked to work on building a data pipeline, you have noticed that you are currently working on a very large scale ETL many data dependencies, which of the following tools can be used to address this problem?

A. JOBS and TASKS
B. DELTA LIVE TABLES
C. STRUCTURED STREAMING with MULTI HOP
D. SQL Endpoints
E. AUTO LOADER

Answer: B

Explanation:
Explanation
The answer is, DELTA LIVE TABLES
DLT simplifies data dependencies by building DAG-based joins between live tables. Here is a view of how the dag looks with data dependencies without additional meta data,
1.create or replace live view customers
2.select * from customers;
3.
4.create or replace live view sales_orders_raw
5.select * from sales_orders;
6.
7.create or replace live view sales_orders_cleaned
8.as
9.select sales.* from
10.live.sales_orders_raw s
11. join live.customers c
12.on c.customer_id = s.customer_id
13.where c.city = 'LA';
14.
15.create or replace live table sales_orders_in_la
16.selects from sales_orders_cleaned;
Above code creates below dag

Documentation on DELTA LIVE TABLES,
https://databricks.com/product/delta-live-tables
https://databricks.com/blog/2022/04/05/announcing-generally-availability-of-databricks-delta-live-tables-dlt.htm DELTA LIVE TABLES, addresses below challenges when building ETL processes
1.Complexities of large scale ETL
a.Hard to build and maintain dependencies
b.Difficult to switch between batch and stream
2.Data quality and governance
a.Difficult to monitor and enforce data quality
b.Impossible to trace data lineage
3.Difficult pipeline operations
a.Poor observability at granular data level
b.Error handling and recovery is laborious

NEW QUESTION # 93
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of
512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

A. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
B. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
C. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
D. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
E. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), and then write to parquet.

Answer: C

Explanation:
The key to efficiently converting a large JSON dataset to Parquet files of a specific size without shuffling data lies in controlling the size of the output files directly.
* Setting spark.sql.files.maxPartitionBytes to 512 MB configures Spark to process data in chunks of
512 MB. This setting directly influences the size of the part-files in the output, aligning with the target file size.
* Narrow transformations (which do not involve shuffling data across partitions) can then be applied to this data.
* Writing the data out to Parquet will result in files that are approximately the size specified by spark.sql.files.maxPartitionBytes, in this case, 512 MB.
* The other options involve unnecessary shuffles or repartitions (B, C, D) or an incorrect setting for this specific requirement (E).
References:
* Apache Spark Documentation: Configuration - spark.sql.files.maxPartitionBytes
* Databricks Documentation on Data Sources: Databricks Data Sources Guide

NEW QUESTION # 94
......

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Up-to-Date Online Databricks Databricks-Certified-Professional-Data-Engineer Practice Test Engine

Databricks-Certified-Professional-Data-Engineer Free Test Questions & Valid Databricks-Certified-Professional-Data-Engineer Exam Review

Databricks Certified Professional Data Engineer Exam Sample Questions (Q89-Q94):

Databricks Databricks-Certified-Professional-Data-Engineer exam prep, pass Databricks-Certified-Professional-Data-Engineer exam

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