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Databricks is an American-based technology company that develops software that manages big data via Apache Spark. Their software helps different organizations to process and analyze large data sets efficiently. Therefore, there is a need to certify professionals to handle these technologies. The Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) Certification Exam is designed to validate the knowledge and proficiency of data engineers in designing and building effective and scalable data engineering solutions on the Databricks platform.

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Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) certification exam is designed for professionals who want to demonstrate their expertise in using Databricks to manage big data and create data pipelines. Databricks Certified Professional Data Engineer Exam certification exam is ideal for data engineers, data architects, data scientists, and other professionals who work with big data and want to validate their skills in using Databricks to build data pipelines.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
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.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, 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.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: C

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.
Reference
Apache Spark Configuration
Writing to Parquet Files in Spark

NEW QUESTION # 18
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

A. The connection to the external table will fail; the string "redacted" will be printed.
B. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
C. The connection to the external table will succeed; the string value of password will be printed in plain text.
D. The connection to the external table will succeed; the string "redacted" will be printed.
E. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.

Answer: D

Explanation:
Explanation
This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string "redacted" will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks. Verified References: [Databricks Certified Data Engineer Professional], under "Security & Governance" section; Databricks Documentation, under
"Secrets" section.

NEW QUESTION # 19
Review the following error traceback:

Which statement describes the error being raised?

A. There is no column in the table named heartrateheartrateheartrate
B. There is a type error because a DataFrame object cannot be multiplied.
C. There is a type error because a column object cannot be multiplied.
D. The code executed was PvSoark but was executed in a Scala notebook.
E. There is a syntax error because the heartrate column is not correctly identified as a column.

Answer: A

Explanation:
The error being raised is an AnalysisException, which is a type of exception that occurs when Spark SQL cannot analyze or execute a query due to some logical or semantic error1. In this case, the error message indicates that the query cannot resolve the column name 'heartrateheartrateheartrate' given the input columns 'heartrate' and 'age'. This means that there is no column in the table named 'heartrateheartrateheartrate', and the query is invalid. A possible cause of this error is a typo or a copy-paste mistake in the query. To fix this error, the query should use a valid column name that exists in the table, such as 'heartrate'. Reference: AnalysisException

NEW QUESTION # 20
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. SQL Endpoints
B. DELTA LIVE TABLES
C. STRUCTURED STREAMING with MULTI HOP
D. AUTO LOADER
E. JOBS and TASKS

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 # 21
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A. Skew caused by more data being assigned to a subset of spark-partitions.
B. Credential validation errors while pulling data from an external system.
C. Spill resulting from attached volume storage being too small.
D. Task queueing resulting from improper thread pool assignment.
E. Network latency due to some cluster nodes being in different regions from the source data

Answer: A

Explanation:
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills. Verified References:
[Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Skew" section.

NEW QUESTION # 22
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