Databricks-Certified-Professional-Data-Engineer Valid Exam Tips & Databricks-Certified-Professional-Data-Engineer Exam Tests

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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Processing with Spark25-30%- Spark DataFrames and Spark SQL
  • 1. Window functions
  • 2. Spark SQL queries and functions
  • 3. DataFrame operations and transformations
- Python and SQL for data engineering
  • 1. Built-in and user-defined functions
  • 2. Performance optimization techniques
  • 3. Spark APIs in Python
Topic 2: Data Warehouse and Lakehouse Architecture15-20%- Lakehouse architecture principles
  • 1. Data governance fundamentals
  • 2. Bronze, silver, gold data layers
  • 3. Differences between data lake, data warehouse, and lakehouse
Topic 3: Data Ingestion15-20%- Batch ingestion methods
  • 1. DBR autoloader
  • 2. Integration with external systems
  • 3. Spark APIs for ingestion
- Streaming ingestion
  • 1. Kafka integration
  • 2. Structured streaming fundamentals
Topic 4: Pipeline Development and Orchestration10-15%- Databricks workflows
  • 1. Task dependencies and orchestration
  • 2. Jobs and job scheduling
  • 3. Monitoring and alerting
Topic 5: Delta Lake20-25%- Delta Lake operations
  • 1. Schema evolution and enforcement
  • 2. Delta Live Tables
  • 3. Merge, update, delete operations
- Delta Lake fundamentals
  • 1. Optimize and Z-order
  • 2. ACID transactions
  • 3. Time travel and data versioning

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch nameddev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

Answer: A

Explanation:
This is the correct answer because it will allow the developer to update their local repository with the latest changes from the remote repository and switch to the desired branch. Pulling changes will not affect the current branch or create any conflicts, as it will only fetch the changes and not merge them. Selecting the dev-
2.3.9 branch from the dropdown will checkout that branch and display its contents in the notebook. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Tooling" section; Databricks Documentation, under "Pull changes from a remote repository" section.


NEW QUESTION # 82
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?

Answer: C

Explanation:
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function.
Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.
References:
* Software Testing Fundamentals: Unit Testing


NEW QUESTION # 83
A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should have five workers, one driver of type i3.xlarge, and should use the '14.3.x-scala2.12' runtime.
Which command should the data engineer use?

Answer: D

Explanation:
The Databricks CLI allows users to manage clusters using command-line commands. The correct command for creating a cluster follows a specific format.
Key Components in the Command:
* Command Type: databricks compute create is the correct syntax for creating a new compute resource (cluster).
* Runtime Version: '14.3.x-scala2.12' specifies the Databricks runtime to use.
* Workers: --num-workers 5 sets the number of worker nodes to 5.
* Node Type: --node-type-id i3.xlarge defines the hardware configuration.
* Cluster Name: --cluster-name DataEngineer_cluster assigns a recognizable name to the cluster.
Evaluation of Options:
* Option A (databricks clusters create ...)
* Incorrect: databricks clusters create is not a valid command in the Databricks CLI v0.205.
* The correct CLI command for cluster creation is databricks compute create.
* Option B (databricks clusters add ...)
* Incorrect: databricks clusters add is not a valid CLI command.
* Option C (databricks compute add ...)
* Incorrect: databricks compute add is not a valid CLI command.
* Option D (databricks compute create ...)
* Correct: databricks compute create is the correct command for creating a cluster.
Conclusion:
The correct command to create a cluster with five workers, an i3.xlarge node type, and Databricks runtime
14.3.x-scala2.12 is:
databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster Thus, the correct answer is D.
References:
Databricks CLI Documentation


NEW QUESTION # 84
A new data engineer has started at a company. The data engineer has recently been added to the company's
Databricks workspace as new.engineer@company.com. The data engineer needs to be able to query the table
sales in the database retail. The new data engineer already has been granted USAGE on the database retail.
Which of the following commands can be used to grant the appropriate permissions to the new data engineer?

Answer: D


NEW QUESTION # 85
When defining external tables using formats CSV, JSON, TEXT, BINARY any query on the exter-nal tables caches the data and location for performance reasons, so within a given spark session any new files that may have arrived will not be available after the initial query. How can we address this limitation?

Answer: A

Explanation:
Explanation
The answer is REFRESH TABLE table_name
REFRESH TABLE table_name will force Spark to refresh the availability of external files and any changes.
When spark queries an external table it caches the files associated with it, so that way if the table is queried again it can use the cached files so it does not have to retrieve them again from cloud object storage, but the drawback here is that if new files are available Spark does not know until the Refresh command is ran.


NEW QUESTION # 86
......

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