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| Section | Weight | Objectives |
|---|
| Topic 1: Productionizing Data Pipelines | 18% | - Workflow orchestration
- 1. Scheduling, triggers, and dependencies
- 2. Lakeflow Jobs creation and management
- Monitoring and troubleshooting
- 1. Logging and error handling
- 2. Pipeline reliability and recovery
- Deployment and CI/CD
- 1. Version control integration
- 2. Databricks Asset Bundles
|
| Topic 2: Development and Ingestion | 30% | - Data ingestion patterns and methods
- 1. Batch and streaming ingestion
- 2. Delta Lake basics and usage
- 3. Connecting to external data sources
- Notebook development fundamentals
- 1. Using PySpark and Spark SQL
- 2. Data exploration and validation
|
| Topic 3: Data Governance & Quality | 11% | - Unity Catalog implementation
- 1. Data governance model
- 2. Permissions and access control
- Data quality and reliability
- 1. Data validation and quality checks
- 2. Schema enforcement and evolution
|
| Topic 4: Databricks Intelligence Platform | 10% | - Platform architecture and core concepts
- 1. Data layout and optimization: partitioning, file sizing, caching
- 2. Workspace navigation and management
- 3. Compute options: clusters, SQL warehouses, serverless
|
| Topic 5: Data Processing & Transformations | 31% | - Query optimization and performance
- 1. Optimization strategies
- 2. Understanding query plans
- Data transformation techniques
- 1. Complex data processing logic
- 2. Aggregations, joins, and window functions
- 3. DataFrame operations and transformations
|
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Databricks Certified Data Engineer Associate Exam Sample Questions (Q172-Q177):
NEW QUESTION # 172
A data engineer is debugging a Python notebook in Databricks that processes a dataset using PySpark. The notebook fails with an error during a DataFrame transformation. The engineer wants to inspect the state of variables, such as the input DataFrame and intermediate results, to identify where the error occurs. Which tool should the engineer use to debug the notebook and inspect the values of variables like DataFrames?
- A. Use the Python Notebook Interactive Debugger to set breakpoints and inspect variable values in real-time
- B. Use the Ganglia UI to monitor cluster resource usage and identify hardware issues
- C. Use the Spark UI to analyze the execution plan and identify stages where the job failed
- D. Use the Databricks CLI to download and analyze driver logs for detailed error messages
Answer: A
Explanation:
The Python Notebook Interactive Debugger in Databricks allows setting breakpoints and inspecting variable values, including DataFrames, in real time. This makes it the correct tool for debugging transformation errors in a PySpark notebook.
NEW QUESTION # 173
A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

- A. Option B
- B. Option A
- C. Option C
- D. Option D
- E. Option E
Answer: E
Explanation:
Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks
NEW QUESTION # 174
A data engineer wants to reduce costs and optimize cloud spending. The data engineer has decided to use Databricks Serverless for lowering cloud costs while maintaining existing SLAs.
What is the first step in migrating to Databricks Serverless?
- A. A frequently running and efficient Scala-based data transformation pipeline compatible with the latest Databricks runtime and Unity Catalog
- B. A frequently running and efficient Python-based data transformation pipeline compatible with the latest Databricks runtime and Unity Catalog
- C. Legacy Ingestion pipelines that include ingestion from sources API's, files, JDBC/ODBC connections
- D. Low frequency BI Dashboarding and Adhoc SQL Analytics
Answer: B
Explanation:
The first workloads to migrate to Databricks Serverless should be frequently running, efficient pipelines that are already compatible with the latest Databricks runtime and Unity Catalog. This ensures smooth migration while maintaining SLAs, and Python-based pipelines are the most common and fully supported starting point.
NEW QUESTION # 175
A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?
- A. The HISTORY command was run on the table
- B. The OPTIMIZE command was nun on the table
- C. The DELETE HISTORY command was run on the table
- D. The TIME TRAVEL command was run on the table
- E. The VACUUM command was run on the table
Answer: E
Explanation:
The VACUUM command is used to remove files that are no longer referenced by a Delta table and are older than the retention threshold1. The default retention period is 7 days2, but it can be changed by setting the delta.logRetentionDuration and delta.deletedFileRetentionDuration configurations3. If the VACUUM command was run on the table with a retention period shorter than 3 days, then the data files that were needed to restore the table to a 3-day-old version would have been deleted. The other commands do not delete data files from the table. The TIME TRAVEL command is used to query a historical version of the table4. The DELETE HISTORY command is not a valid command in Delta Lake. The OPTIMIZE command is used to improve the performance of the table by compacting small files into larger ones5. The HISTORY command is used to retrieve information about the operations performed on the table. References: 1: VACUUM | Databricks on AWS 2: Work with Delta Lake table history | Databricks on AWS 3: [Delta Lake configuration | Databricks on AWS] 4: Work with Delta Lake table history - Azure Databricks 5: [OPTIMIZE | Databricks on AWS] : [HISTORY | Databricks on AWS]
NEW QUESTION # 176
A data engineer is working in a Python notebook on Databricks to process data, but notices that the output is not as expected. The data engineer wants to investigate the issue by stepping through the code and checking the values of certain variables during execution. Which tool should the data engineer use to inspect the code execution and variables in real-time?
- A. Job Execution Dashboard
- B. Python Notebook Interactive Debugger
- C. SQL Analytics
- D. Cluster Logs
Answer: B
Explanation:
The Python Notebook Interactive Debugger in Databricks allows stepping through code execution, setting breakpoints, and inspecting variable values in real-time. This makes it the correct tool for debugging Python code inside a Databricks notebook.
NEW QUESTION # 177
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