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Databricks Certified Professional Data Engineer Exam Sample Questions (Q49-Q54):

NEW QUESTION # 49
Direct query on external files limited options, create external tables for CSV files with header and pipe delimited CSV files, fill in the blanks to complete the create table statement CREATE TABLE sales (id int, unitsSold int, price FLOAT, items STRING)
________
________
LOCATION "dbfs:/mnt/sales/*.csv"

A. USING CSV
TYPE ( "true","|")
B. FORMAT CSV
OPTIONS ( "true","|")
C. FORMAT CSV
TYPE ( header ="true", delimiter = "|")
D. USING CSV
OPTIONS ( header ="true", delimiter = "|")
(Correct)
E. FORMAT CSV
FORMAT TYPE ( header ="true", delimiter = "|")

Answer: D

Explanation:
Explanation
Answer is
USING CSV
OPTIONS ( header ="true", delimiter = "|")
Here is the syntax to create an external table with additional options
CREATE TABLE table_name (col_name1 col_typ1,..)
USING data_source
OPTIONS (key='value', key2=vla2)
LOCATION = "/location"

NEW QUESTION # 50
Which statement describes Delta Lake Auto Compaction?

A. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
B. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
C. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
D. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
E. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.

Answer: E

Explanation:
This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones.
Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB.
Auto Compaction only compacts files that have not been compacted previously. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Compaction for Delta Lake on Databricks" section.
"Auto compaction occurs after a write to a table has succeeded and runs synchronously on the cluster that has performed the write. Auto compaction only compacts files that haven't been compacted previously."
https://learn.microsoft.com/en-us/azure/databricks/delta/tune-file-size

NEW QUESTION # 51
Which of the following describes a benefit of a data lakehouse that is unavailable in a traditional data
warehouse?

A. A data lakehouse utilizes proprietary storage formats for data
B. A data lakehouse provides a relational system of data management
C. A data lakehouse couples storage and compute for complete control
D. A data lakehouse enables both batch and streaming analytics
E. A data lakehouse captures snapshots of data for version control purposes

Answer: D

NEW QUESTION # 52
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. The checkpoint directory will be used to track state information for the unique keys present in the join.
C. Stream-static joins cannot use static Delta tables because of consistency issues.
D. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
E. The checkpoint directory will be used to track updates to the static Delta table.

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 References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Stream and static joins" section.

NEW QUESTION # 53
The data engineering team noticed that one of the job fails randomly as a result of using spot in-stances, what feature in Jobs/Tasks can be used to address this issue so the job is more stable when using spot instances?

A. Add second task and add a check condition to rerun the first task if it fails
B. Use Databrick REST API to monitor and restart the job
C. Use Jobs runs, active runs UI section to monitor and restart the job
D. Add a retry policy to the task
E. Restart the job cluster, job automatically restarts

Answer: D

Explanation:
Explanation
The answer is, Add a retry policy to the task
Tasks in Jobs support Retry Policy, which can be used to retry a failed tasks, especially when using spot instance it is common to have failed executors or driver.

NEW QUESTION # 54
......

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