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

Section	Objectives
Data Modeling and Storage	- Schema evolution and data partitioning strategies - Design scalable data lakehouse architectures - Delta Lake table design and optimization
Production Pipelines and Orchestration	- Build and manage workflows using Databricks Jobs - Pipeline reliability and fault tolerance - Automate ETL pipelines and scheduling
Data Ingestion and Transformation	- Transform and clean datasets using Spark SQL and DataFrame APIs - Ingest data using Apache Spark and Databricks - Handle batch and streaming data pipelines
Security, Governance, Monitoring, and Optimization	- Cost optimization and performance tuning - Monitor and optimize Spark workloads - Implement Unity Catalog governance and access control

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q85-Q90):

NEW QUESTION # 85
Which of the following statements are correct on how Delta Lake implements a lake house?

A. Delta lake uses a proprietary format to write data, optimized for cloud storage
B. Using Apache Hadoop on cloud object storage
C. Delta lake stores data and meta data in computes memory
D. Delta lake always stores meta data in memory vs storage
E. Delta lake uses open source, open format, optimized cloud storage and scalable meta data

Answer: E

Explanation:
Explanation
Delta lake is
* Open source
* Builds up on standard data format
* Optimized for cloud object storage
* Built for scalable metadata handling
Delta lake is not
* Proprietary technology
* Storage format
* Storage medium
* Database service or data warehouse

NEW QUESTION # 86
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?

A. Manual
B. Integration
C. functional
D. Unit

Answer: D

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.
Reference:
Software Testing Fundamentals: Unit Testing

NEW QUESTION # 87
An external object storage container has been mounted to the location/mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of thefinancegroup, which statement describes how thetx_sales table will be created?

A. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
B. A managed table will be created in the DBFS root storage container.
C. An external table will be created in the storage container mounted to /mnt/finance eda bucket.
D. An managed table will be created in the storage container mounted to /mnt/finance eda bucket.
E. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.

Answer: D

Explanation:
https://docs.databricks.com/en/lakehouse/data-objects.html

NEW QUESTION # 88
Which of the following developer operations in the CI/CD can only be implemented through a GIT provider when using Databricks Repos.

A. Trigger Databricks Repos pull API to update the latest version
B. Create and edit code
C. Commit and push code
D. Pull request and review process
E. Create a new branch

Answer: D

Explanation:
Explanation
The answer is Pull request and review process, please note: the question is asking for steps that are being implemented in GIT provider not Databricks Repos.
See below diagram to understand the role of 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

Bottom of Form
Top of Form

NEW QUESTION # 89
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
B. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
C. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
D. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
E. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.

Answer: B

Explanation:
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

NEW QUESTION # 90
......

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