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To become a Databricks Certified Professional Data Engineer, candidates must pass a rigorous exam that covers a wide range of topics related to data engineering. Databricks-Certified-Professional-Data-Engineer Exam is designed to test a candidate's ability to design, build, and maintain data pipelines using Databricks tools and technologies. It also tests their understanding of data modeling, data warehousing, and data integration.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q39-Q44):

NEW QUESTION # 39
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.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.
C. 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.
D. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
E. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.

Answer: E

Explanation:
For this scenario where a one-TB JSON dataset needs to be converted into Parquet format without employing Delta Lake's auto-sizing features, the goal is to avoid unnecessary data shuffles and yet ensure optimal file sizes for the output Parquet files. Here's a breakdown of why option A is most suitable:
* Setting maxPartitionBytes:The spark.sql.files.maxPartitionBytes configuration controls the size of blocks that Spark reads from the data source (in this case, the JSON files) but also influences the output size of files when data is written without repartition or coalesce operations. Setting this parameter to
512 MB directly addresses the requirement to manage the output file size effectively.
* Data Ingestion and Processing:
* Ingesting Data:Load the JSON dataset into a DataFrame.
* Applying Transformations:Perform any required narrow transformations that do not involve shuffling data (like filtering or adding new columns).
* Writing to Parquet:Directly write the transformed DataFrame to Parquet files. The setting for maxPartitionBytes ensures that each part-file is approximately 512 MB, meeting the requirement for part-file size without additional steps to repartition or coalesce the data.
* Performance Consideration:This approach is optimal because:
* It avoids the overhead of shuffling data, which can be significant, especially with large datasets.
* It directly ties the read/write operations to a configuration that matches the target output size, making it efficient in terms of both computation and I/O operations.
* Alternative Options Analysis:
* Option B and D:Involves repartitioning, which would trigger a shuffle of the data, contradicting the requirement to avoid shuffling for performance reasons.
* Option C:Uses coalesce, which is less intensive than repartition but can still lead to uneven partition sizes and does not directly control the output file size as effectively as setting maxPartitionBytes.
* Option E:Setting shuffle partitions to 512 doesn't directly control the output file size for writing to Parquet and could lead to smaller files depending on the dataset's partitioning post- transformations.
References
* Apache Spark Configuration
* Writing to Parquet Files in Spark

NEW QUESTION # 40
A facilities-monitoring team is building a near-real-time Power BI dashboard off the Delta table device_readings :
* device_id STRING - unique sensor ID
* event_ts TIMESTAMP - ingestion timestamp (UTC)
* temperature_c DOUBLE - temperature in °C
* notes STRING
For each sensor, the team needs one row per non-overlapping 5-minute interval, offset by 2 minutes (for example, intervals like 00:02-00:07 , 00:07-00:12 , and so on), showing the average temperature in that slice.
The result must include each interval's start and end timestamps so downstream tools can plot time-series bars correctly. Which query satisfies the requirement?

A. SELECT
device_id,
date_trunc( ' minute ' , event_ts - INTERVAL 2 MINUTES) + INTERVAL 2 MINUTES AS bucket_start, date_trunc( ' minute ' , event_ts - INTERVAL 2 MINUTES) + INTERVAL 7 MINUTES AS bucket_end, AVG(temperature_c) AS avg_temp_5m FROM device_readings GROUP BY device_id, date_trunc( ' minute ' , event_ts - INTERVAL 2 MINUTES) ORDER BY device_id, bucket_start;
B. WITH buckets AS (
SELECT
device_id,
window(event_ts, ' 5 minutes ' , ' 5 minutes ' , ' 2 minutes ' ) AS win, temperature_c FROM device_readings ) SELECT device_id, win.start AS bucket_start, win.end AS bucket_end, AVG(temperature_c) AS avg_temp_5m FROM buckets GROUP BY device_id, win ORDER BY device_id, bucket_start;
C. SELECT
device_id,
event_ts,
AVG(temperature_c) OVER (
PARTITION BY device_id
ORDER BY event_ts
RANGE BETWEEN INTERVAL 5 MINUTES PRECEDING AND CURRENT ROW
) AS avg_temp_5m
FROM device_readings
WINDOW w AS (window(event_ts, ' 5 minutes ' , ' 2 minutes ' ));
D. SELECT
device_id,
window.start AS bucket_start,
window.end AS bucket_end,
AVG(temperature_c) AS avg_temp_5m
FROM device_readings
GROUP BY device_id, window(event_ts, ' 5 minutes ' , ' 5 minutes ' , ' 2 minutes ' ) ORDER BY device_id, bucket_start;

Answer: B

Explanation:
Spark documents window(timeColumn, windowDuration, slideDuration=None, startTime=None) for time bucketing. The startTime argument is specifically the offset from the epoch used to align window boundaries, and the output is a window struct with start and end fields. That exactly matches the requirement for 5-minute non-overlapping intervals offset by 2 minutes. ( Apache Spark ) Option A correctly uses window(event_ts, ' 5 minutes ' , ' 5 minutes ' , ' 2 minutes ' ) , which creates tumbling
5-minute windows offset by 2 minutes and then exposes win.start and win.end . Option B is malformed in how it references the generated window column, option C creates minute-aligned groupings rather than true 5- minute tumbling windows, and option D computes a rolling window average instead of one row per non- overlapping time bucket. ( Apache Spark )

NEW QUESTION # 41
Which of the below commands can be used to drop a DELTA table?

A. DROP DELTA table_name
B. DROP TABLE table_name
C. DROP table_name
D. DROP TABLE table_name FORMAT DELTA

Answer: B

NEW QUESTION # 42
A data engineer is masking a column containing email addresses. The goal is to produce output strings of identical length for all rows, while generating different outputs for different email values.
Which SQL function should be used to achieve this?

A. sha1(email)
B. hash(email)
C. sha2(email, 0)
D. mask(email, '?')

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The hash() function in Databricks SQL returns a deterministic fixed-length integer (or hexadecimal string) derived from the input. When applied to sensitive identifiers like email addresses, it produces a unique value for each distinct input while ensuring uniform output size, making it suitable for anonymization where referential consistency is required.
Functions like mask() perform pattern-based substitutions that change string lengths, and sha1() or sha2() produce long hexadecimal strings of varying lengths (depending on hash size), which may not match requirements for fixed-length masking.
Therefore, the correct choice for fixed-length, deterministic pseudonymization of email addresses is hash(email), as it maintains analytical usability while anonymizing sensitive data.

NEW QUESTION # 43
Which of the following commands can be used to run one notebook from another notebook?

A. execute.utils.run("full notebook path")
B. notebook.utils.run("full notebook path")
C. only job clusters can run notebook
D. dbutils.notebook.run("full notebook path")
E. spark.notebook.run("full notebook path")

Answer: D

Explanation:
Explanation
The answer is dbutils.notebook.run(" full notebook path ")
Here is the full command with additional options.
run(path: String, timeout_seconds: int, arguments: Map): String
1.dbutils.notebook.run("ful-notebook-name", 60, {"argument": "data", "argument2": "data2", ...})

NEW QUESTION # 44
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