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Databricks Certified Data Engineer Professional Exam Sample Questions (Q110-Q115):

NEW QUESTION # 110
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. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), 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 512, ingest the data, execute the narrow transformations, and then write to parquet.
D. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
E. 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.

Answer: E

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).

NEW QUESTION # 111
A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A. Executor's log file
B. Cluster Event Log
C. Ganglia
D. Workspace audit logs
E. Driver's log file

Answer: B

NEW QUESTION # 112
A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.
How can the data engineer fix this?

A. Move the table definition into a separate function, and make calls to this function using different input parameters inside the for loop.
B. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
C. Wrap the for loop inside another table definition, using generalized names and properties to replace with those from the inner table definition.
D. Convert the list of configuration values to a dictionary of table settings, using table names as keys.

Answer: A

Explanation:
In the provided refactored code, the for loop dynamically attempts to define multiple tables, but the use of a loop within the DLT (@dlt.table) decorator does not work properly because it results in a single function reference being overwritten for each iteration. This leads to an incorrect DAG because all the table definitions end up pointing to the last iteration of the loop.

NEW QUESTION # 113
A data engineering team has a time-consuming data ingestion job with three data sources. Each notebook takes about one hour to load new data. One day, the job fails because a notebook update introduced a new required configuration parameter. The team must quickly fix the issue and load the latest data from the failing source. Which action should the team take?

A. Repair the run with the new parameter, and update the task by adding the missing task parameter.
B. Repair the run with the new parameter.
C. Update the task by adding the missing task parameter, and manually run the job.
D. Share the analysis with the failing notebook owner so that they can fix it quickly.

Answer: A

Explanation:
The repair run capability in Databricks Jobs allows re-execution of failed tasks without re-running successful ones. When a parameterized job fails due to missing or incorrect task configuration, engineers can perform a repair run to fix inputs or parameters and resume from the failed state.
This approach saves time, reduces cost, and ensures workflow continuity by avoiding unnecessary recomputation. Additionally, updating the task definition with the missing parameter prevents future runs from failing.
Running the job manually (B) loses run context; (C) alone does not prevent recurrence; (D) delays resolution. Thus, A follows the correct operational and recovery practice.

NEW QUESTION # 114
What is the first of a Databricks Python notebook when viewed in a text editor?

A. %python
B. # MAGIC %python
C. // Databricks notebook source
D. # Databricks notebook source
E. -- Databricks notebook source

Answer: D

Explanation:
https://docs.databricks.com/en/notebooks/notebook-export-import.html#import-a-file-and-convert- it-to-a-notebook

NEW QUESTION # 115
......

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