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

SectionWeightObjectives
Topic 1: Development and Ingestion30%- Notebook development fundamentals
  • 1. Using PySpark and Spark SQL
  • 2. Data exploration and validation
- Data ingestion patterns and methods
  • 1. Delta Lake basics and usage
  • 2. Batch and streaming ingestion
  • 3. Connecting to external data sources
Topic 2: Data Governance & Quality11%- Unity Catalog implementation
  • 1. Permissions and access control
  • 2. Data governance model
- Data quality and reliability
  • 1. Data validation and quality checks
  • 2. Schema enforcement and evolution
Topic 3: Productionizing Data Pipelines18%- Workflow orchestration
  • 1. Scheduling, triggers, and dependencies
  • 2. Lakeflow Jobs creation and management
- Monitoring and troubleshooting
  • 1. Pipeline reliability and recovery
  • 2. Logging and error handling
- Deployment and CI/CD
  • 1. Databricks Asset Bundles
  • 2. Version control integration
Topic 4: Databricks Intelligence Platform10%- 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
Topic 5: Data Processing & Transformations31%- Data transformation techniques
  • 1. Aggregations, joins, and window functions
  • 2. Complex data processing logic
  • 3. DataFrame operations and transformations
- Query optimization and performance
  • 1. Optimization strategies
  • 2. Understanding query plans

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q246-Q251):

NEW QUESTION # 246
A data engineering team needs to ingest historical CSV files from a cloud-storage location that already contains 50,000 existing files. The team also expects new files to arrive continuously. The team wants to use Auto Loader to incrementally process both the existing files and new arrivals efficiently.
Which Auto Loader mode should the team configure for this use case?

Answer: A


NEW QUESTION # 247
A notebook task named check_volume calculates a daily data volume and writes it to a Lakeflow Jobs task value named record_count. If the count is greater than 1 million, the job should run high_volume_pipeline; otherwise, it should run normal_pipeline.
Which control-flow feature should be used?

Answer: D


NEW QUESTION # 248
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:

If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?

Answer: D

Explanation:
https://spark.apache.org/docs/latest/api/python/reference/pyspark.ss/api/pyspark.sql.streaming.DataStreamWriter.trigger.html


NEW QUESTION # 249
A data engineer needs access to a table new_uable, but they do not have the correct permissions. They can ask the table owner for permission, but they do not know who the table owner is.
Which approach can be used to identify the owner of new_table?

Answer: C

Explanation:
To find the owner of a table in Databricks, one can utilize the Data Explorer feature. The Data Explorer provides detailed information about various data objects, including tables. By navigating to the specific table's page in Data Explorer, a data engineer can review the Owner field, which identifies the individual or role that owns the table. This information is crucial for obtaining the necessary permissions or for any administrative actions related to the table.
Reference:
Databricks documentation on Data Explorer: Using Data Explorer in Databricks


NEW QUESTION # 250
A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:

A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?

Answer: B

Explanation:
Explanation
Ref:https://www.databricks.com/discover/pages/data-quality-management
CREATE TABLE my_table (id INT COMMENT 'Unique Identification Number', name STRING COMMENT 'PII', age INT COMMENT 'PII') TBLPROPERTIES ('contains_pii'=True) COMMENT 'Contains PII';


NEW QUESTION # 251
......

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