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

Section	Weight	Objectives
Topic 1: Data Governance & Quality	11%	- Data quality and reliability 1. Data validation and quality checks 2. Schema enforcement and evolution - Unity Catalog implementation 1. Data governance model 2. Permissions and access control
Topic 2: Development and Ingestion	30%	- Notebook development fundamentals 1. Data exploration and validation 2. Using PySpark and Spark SQL - Data ingestion patterns and methods 1. Batch and streaming ingestion 2. Connecting to external data sources 3. Delta Lake basics and usage
Topic 3: Data Processing & Transformations	31%	- Query optimization and performance 1. Understanding query plans 2. Optimization strategies - Data transformation techniques 1. DataFrame operations and transformations 2. Aggregations, joins, and window functions 3. Complex data processing logic
Topic 4: Productionizing Data Pipelines	18%	- Deployment and CI/CD 1. Version control integration 2. Databricks Asset Bundles - Workflow orchestration 1. Lakeflow Jobs creation and management 2. Scheduling, triggers, and dependencies - Monitoring and troubleshooting 1. Logging and error handling 2. Pipeline reliability and recovery
Topic 5: Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Data layout and optimization: partitioning, file sizing, caching 2. Compute options: clusters, SQL warehouses, serverless 3. Workspace navigation and management

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q220-Q225):

NEW QUESTION # 220
A data engineering team must version-control notebooks, review changes through pull requests, and deploy the same code to dev, staging, and production workspaces.
Which Databricks capability supports this workflow?

A. Storing notebook source files in DBFS /FileStore and copying them manually
B. Enabling notebook revision history and restoring snapshots as needed
C. Databricks Git folders (Repos) integrated with a remote Git provider
D. Exporting notebooks as DBC archives and emailing them between workspaces

Answer: C

Explanation:
Databricks Git folders provide native integration with providers such as GitHub, GitLab, Azure DevOps, and Bitbucket, supporting branching, commits, pulls, and pull-request workflows.
Combined with Databricks Asset Bundles or job Git sources, the same repository can be deployed across environments.

NEW QUESTION # 221
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.
C. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
D. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
E. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.

Answer: B

NEW QUESTION # 222
Which tool is used by Auto Loader to process data incrementally?

A. Databricks SQL
B. Spark Structured Streaming
C. Checkpointing
D. Unity Catalog

Answer: B

Explanation:
Auto Loader in Databricks utilizes Spark Structured Streaming for processing data incrementally. This allows Auto Loader to efficiently ingest streaming or batch data at scale and to recognize new data as it arrives in cloud storage. Spark Structured Streaming provides the underlying engine that supports various incremental data loading capabilities like schema inference and file notification mode, which are crucial for the dynamic nature of data lakes.

NEW QUESTION # 223
A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?

A. They can clone the existing task to a new Job and then edit it to run the new notebook.
B. They can create a new task in the existing Job and then add the original task as a dependency of the new task.
C. They can create a new task in the existing Job and then add it as a dependency of the original task.
D. They can create a new job from scratch and add both tasks to run concurrently.
E. They can clone the existing task in the existing Job and update it to run the new notebook.

Answer: C

Explanation:
To set up the new task to run a new notebook prior to the original task in a single-task Job, the data engineer can use the following approach: In the existing Job, create a new task that corresponds to the new notebook that needs to be run. Set up the new task with the appropriate configuration, specifying the notebook to be executed and any necessary parameters or dependencies. Once the new task is created, designate it as a dependency of the original task in the Job configuration. This ensures that the new task is executed before the original task.

NEW QUESTION # 224
Which of the following is stored in the Databricks customer's cloud account?

A. Notebooks
B. Databricks web application
C. Data
D. Repos
E. Cluster management metadata

Answer: C

Explanation:
The only option that is stored in the Databricks customer's cloud account is data. Data is stored in the customer's cloud storage service, such as AWS S3 or Azure Data Lake Storage. The customer has full control and ownership of their data and can access it directly from their cloud account.
Option A is not correct, as the Databricks web application is hosted and managed by Databricks on their own cloud infrastructure. The customer does not need to install or maintain the web application, but only needs to access it through a web browser.
Option B is not correct, as the cluster management metadata is stored and managed by Databricks on their own cloud infrastructure. The cluster management metadata includes information such as cluster configuration, status, logs, and metrics. The customer can view and manage their clusters through the Databricks web application, but does not have direct access to the cluster management metadata.
Option C is not correct, as the repos are stored and managed by Databricks on their own cloud infrastructure.
Repos are version-controlled repositories that store code and data files for Databricks projects. The customer can create and manage their repos through the Databricks web application, but does not have direct access to the repos.
Option E is not correct, as the notebooks are stored and managed by Databricks on their own cloud infrastructure. Notebooks are interactive documents that contain code, text, and visualizations for Databricks workflows. The customer can create and manage their notebooks through the Databricks web application, but does not have direct access to the notebooks.
Databricks Architecture
Databricks Data Sources
Databricks Repos
[Databricks Notebooks]
[Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 225
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