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

SectionWeightObjectives
Databricks Intelligence Platform10%- Platform architecture and core concepts
  • 1. Workspace navigation and management
  • 2. Data layout and optimization: partitioning, file sizing, caching
  • 3. Compute options: clusters, SQL warehouses, serverless
Productionizing Data Pipelines18%- Deployment and CI/CD
  • 1. Version control integration
  • 2. Databricks Asset Bundles
- Monitoring and troubleshooting
  • 1. Pipeline reliability and recovery
  • 2. Logging and error handling
- Workflow orchestration
  • 1. Lakeflow Jobs creation and management
  • 2. Scheduling, triggers, and dependencies
Development and Ingestion30%- Data ingestion patterns and methods
  • 1. Batch and streaming ingestion
  • 2. Connecting to external data sources
  • 3. Delta Lake basics and usage
- Notebook development fundamentals
  • 1. Using PySpark and Spark SQL
  • 2. Data exploration and validation
Data Processing & Transformations31%- Data transformation techniques
  • 1. DataFrame operations and transformations
  • 2. Aggregations, joins, and window functions
  • 3. Complex data processing logic
- Query optimization and performance
  • 1. Understanding query plans
  • 2. Optimization strategies
Data Governance & Quality11%- Data quality and reliability
  • 1. Schema enforcement and evolution
  • 2. Data validation and quality checks
- Unity Catalog implementation
  • 1. Data governance model
  • 2. Permissions and access control

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q168-Q173):

NEW QUESTION # 168
A data engineer is joining a very large events_df DataFrame containing billions of rows with a small country_lookup_df DataFrame containing fewer than 10,000 rows. The DataFrames are joined on the country_code column. The join is slow because of data shuffling.
How should the data engineer improve performance?

Answer: B


NEW QUESTION # 169
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

Answer: B


NEW QUESTION # 170
A Databricks workflow fails at the last stage due to an error in a notebook. This workflow runs daily. The data engineer fixes the mistake and wants to rerun the pipeline. This workflow is very costly and time-intensive to run. Which action should the data engineer do in order to minimise downtime and cost?

Answer: B

Explanation:
Repair run in Databricks allows rerunning only the failed tasks in a workflow instead of re-running the entire pipeline, minimizing downtime and reducing costs.


NEW QUESTION # 171
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

Answer: A

Explanation:
In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is "sqlite" because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, "sqlite" would be appropriate here. References:
* Create a table using JDBC
* JDBC connection string
* SQLite JDBC driver


NEW QUESTION # 172
An Auto Loader stream fails when a new column appears in incoming JSON files. The team wants the stream to add the new column automatically, restarting as needed, rather than failing permanently.
Which cloudFiles.schemaEvolutionMode setting should be used?

Answer: B

Explanation:
addNewColumns is the default mode when a schema location is provided: the stream fails on encountering an unknown column, updates the stored schema, and on restart processes data with the evolved schema. rescue keeps the schema fixed and places unexpected fields in the rescued data column, while none and failOnNewColumns do not evolve the schema.


NEW QUESTION # 173
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