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Databricks Certified Professional Data Engineer exam is a certification program that validates the skills and knowledge of professionals working with big data technologies, particularly on the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam is designed to test the candidate's ability to design, build, and maintain data pipelines, implement machine learning workflows, and optimize performance on the Databricks platform. Databricks Certified Professional Data Engineer Exam certification is ideal for data engineers, data architects, and big data professionals who want to demonstrate their expertise in the field.

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Databricks Certified Professional Data Engineer certification is a valuable credential for professionals who want to advance their careers in data engineering. Databricks Certified Professional Data Engineer Exam certification demonstrates the candidates' proficiency in using Databricks to build efficient and scalable data processing systems. Databricks Certified Professional Data Engineer Exam certification also validates the candidates' ability to work with big data technologies and handle complex data workflows. Overall, the Databricks Certified Professional Data Engineer certification is an excellent way for professionals to showcase their expertise in data engineering and increase their value in the job market.

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

Topic
Details

Topic 1

Databricks Tooling: The Databricks Tooling topic encompasses the various features and functionalities of Delta Lake. This includes understanding the transaction log, Optimistic Concurrency Control, Delta clone, indexing optimizations, and strategies for partitioning data for optimal performance in the Databricks SQL service.

Topic 2

Data Processing: The topic covers understanding partition hints, partitioning data effectively, controlling part-file sizes, updating records, leveraging Structured Streaming and Delta Lake, implementing stream-static joins and deduplication. Additionally, it delves into utilizing Change Data Capture and addressing performance issues related to small files.

Topic 3

Testing & Deployment: It discusses adapting notebook dependencies to use Python file dependencies, leveraging Wheels for imports, repairing and rerunning failed jobs, creating jobs based on common use cases, designing systems to control cost and latency SLAs, configuring the Databricks CLI, and using the REST API to clone a job, trigger a run, and export the run output.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q161-Q166):

NEW QUESTION # 161
Which statement regarding stream-static joins and static Delta tables is correct?

A. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
B. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
C. The checkpoint directory will be used to track state information for the unique keys present in the join.
D. Stream-static joins cannot use static Delta tables because of consistency issues.
E. The checkpoint directory will be used to track updates to the static Delta table.

Answer: B

Explanation:
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch. Verified Reference: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Stream and static joins" section.

NEW QUESTION # 162
Which of the following is correct for the global temporary view?

A. global temporary views are created in a database called temp database
B. global temporary views can be accessed across many clusters
C. global temporary views can be still accessed even if the notebook is detached and at-tached
D. global temporary views can be still accessed even if the cluster is restarted
E. global temporary views cannot be accessed once the notebook is detached and attached

Answer: C

Explanation:
Explanation
The answer is global temporary views can be still accessed even if the notebook is detached and attached There are two types of temporary views that can be created Local and Global
* A local temporary view is only available with a spark session, so another notebook in the same cluster can not access it. if a notebook is detached and reattached local temporary view is lost.
* A global temporary view is available to all the notebooks in the cluster, even if the notebook is detached and reattached it can still be accessible but if a cluster is restarted the global temporary view is lost.

NEW QUESTION # 163
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
B. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
C. Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.
D. Define a view against the products_per_order table and define the dashboard against this view.

Answer: D

Explanation:
Given the requirement for daily refresh of data and the need to ensure quick response times for interactive queries while controlling costs, a nightly batch job to pre-compute and save the required summary metrics is the most suitable approach.
By pre-aggregating data during off-peak hours, the dashboard can serve queries quickly without requiring on-the-fly computation, which can be resource-intensive and slow, especially with many users.
This approach also limits the cost by avoiding continuous computation throughout the day and instead leverages a batch process that efficiently computes and stores the necessary data.
The other options (A, C, D) either do not address the cost and performance requirements effectively or are not suitable for the use case of less frequent data refresh and high interactivity.
Reference:
Databricks Documentation on Batch Processing: Databricks Batch Processing Data Lakehouse Patterns: Data Lakehouse Best Practices

NEW QUESTION # 164
A platform team is creating a standardized template for Databricks Asset Bundles to support CI/CD. The template must specify defaults for artifacts, workspace root paths, and a run identity, while allowing a "dev" target to be the default and override specific paths.
How should the team use databricks.yml to satisfy these requirements?

A. Use project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.
B. Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.
C. Use bundle, artifacts, workspace, run_as, and targets at the top level; set one target with default: true and override workspace paths or artifacts under that target.
D. Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.

Answer: C

Explanation:
In Databricks Asset Bundles, the databricks.yml file defines all top-level configuration keys, including bundle, artifacts, workspace, run_as, and targets. The targets section defines specific deployment contexts (for example, dev, test, prod). Setting default: true for a target marks it as the default environment. Overrides for workspace paths and artifact configurations can be defined inside each target while keeping defaults at the top level.
Reference Source: Databricks Asset Bundle Configuration Guide - "Structure of databricks.yml and target overrides."
=========

NEW QUESTION # 165
Operations team is using a centralized data quality monitoring system, a user can publish data quality metrics through a webhook, you were asked to develop a process to send messages using a webhook if there is atleast one duplicate record, which of the following approaches can be taken to integrate an alert with current data quality monitoring system

A. Setup an alert with custom Webhook destination
B. Use notebook and Jobs to use python to publish DQ metrics
C. Setup an alert with custom template
D. Setup an alert to send an email, use python to parse email, and publish a webhook message
E. Setup an alert with dynamic template

Answer: A

Explanation:
Explanation
Alerts supports multiple destinations, email is the default destination.
Alert destinations | Databricks on AWS
Graphical user interface, application Description automatically generated

NEW QUESTION # 166
......

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