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Databricks Certified Professional Data Engineer Certification Exam is created to challenge data engineers with the significant knowledge of Databricks’ data engineering principles and techniques. To become Databricks certified, a candidate must pass the online certification exam designed for data engineers. Databricks-Certified-Professional-Data-Engineer Exam is scenario-based, comprises of 80 multiple-choice questions, and has a time limit of 120 minutes. The Certification exam tests the candidate's knowledge in topics such as data ingestion, data processing, data engineering, ETL, and data warehousing.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q41-Q46):

NEW QUESTION # 41
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
B. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
C. Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.
D. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.

Answer: B

Explanation:
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.
Reference:
Databricks documentation on using SQL views in Delta Lake: https://docs.databricks.com/delta/quick-start.html#sql-views

NEW QUESTION # 42
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. Task queueing resulting from improper thread pool assignment.
C. Spill resulting from attached volume storage being too small.
D. Credential validation errors while pulling data from an external system.
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 Reference: [Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Skew" section.

NEW QUESTION # 43
A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:
Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

A. Yes; Delta Lake supports infinite concurrent writers.
B. No; each of the streams needs to have its own checkpoint directory.
C. No; Delta Lake manages streaming checkpoints in the transaction log.
D. Yes; both of the streams can share a single checkpoint directory.
E. No; only one stream can write to a Delta Lake table.

Answer: B

Explanation:
This is the correct answer because checkpointing is a critical feature of Structured Streaming that provides fault tolerance and recovery in case of failures. Checkpointing stores the current state and progress of a streaming query in a reliable storage system, such as DBFS or S3. Each streaming query must have its own checkpoint directory that is unique and exclusive to that query. If two streaming queries share the same checkpoint directory, they will interfere with each other and cause unexpected errors or data loss. Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Checkpointing" section.

NEW QUESTION # 44
Which of the following describes how Databricks Repos can help facilitate CI/CD workflows on the Databricks Lakehouse Platform?

A. Databricks Repos can commit or push code changes to trigger a CI/CD process
B. Databricks Repos can store the single-source-of-truth Git repository
C. Databricks Repos can merge changes from a secondary Git branch into a main Git branch
D. Databricks Repos can facilitate the pull request, review, and approval process before merging branches
E. Databricks Repos can be used to design, develop, and trigger Git automation pipelines

Answer: A

Explanation:
Explanation
Answer is Databricks Repos can commit or push code changes to trigger a CI/CD process See below diagram to understand the role Databricks Repos and Git provider plays when building a CI/CD workdlow.
All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in Gray are done in a git provider like Github or Azure Devops.
Diagram Description automatically generated

NEW QUESTION # 45
Which of the following SQL commands are used to append rows to an existing delta table?

A. UPDATE table_name
B. APPEND INTO table_name
C. COPY DELTA INTO table_name
D. INSERT INTO table_name
E. APPEND INTO DELTA table_name

Answer: D

Explanation:
Explanation
The answer is INSERT INTO table_name
Insert adds rows to an existing table, this is very similar to add rows a traditional Database or Da-tawarehouse.

NEW QUESTION # 46
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