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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q294-Q299):

NEW QUESTION # 294
A data engineering team ingests customer transaction data from three enterprise sources: a SQL database, Amazon S3, and an Apache Kafka stream. The data must maintain lineage and support compliance audits that require access to historical snapshots.
Which Lakeflow Connect configuration satisfies both the governance and audit requirements?

Answer: D


NEW QUESTION # 295
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: A

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


NEW QUESTION # 296
A data engineer needs access to a table new_table, 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 of the following approaches can be used to identify the owner of new_table?

Answer: A

Explanation:
he approach that can be used to identify the owner of new_table is to review the Owner field in the table's page in Data Explorer. Data Explorer is a web-based interface that allows users to browse, create, and manage data objects such as tables, views, and functions in Databricks1. The table's page in Data Explorer provides various information about the table, such as its schema, partitions, statistics, history, and permissions2. The Owner field shows the name and email address of the user who created or owns the table3. The data engineer can use this information to contact the table owner and request for permission to access the table.
The other options are not correct or reliable for identifying the owner of new_table. Reviewing the Permissions tab in the table's page in Data Explorer can show the users and groups who have access to the table, but not necessarily the owner4. Reviewing the Owner field in the table's page in the cloud storage solution can be misleading, as the owner of the data files may not be the same as the owner of the table5. There is a way to identify the owner of the table, as explained above, so option E is false.
Reference:
1: Data Explorer | Databricks on AWS
2: Table details | Databricks on AWS
3: Set owner when creating a view in databricks sql - Databricks - 9978
4: Table access control | Databricks on AWS
5: External tables | Databricks on AWS


NEW QUESTION # 297
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?

Answer: C

Explanation:
https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/


NEW QUESTION # 298
Which of the following describes the relationship between Gold tables and Silver tables?

Answer: B

Explanation:
Explanation
In some data processing pipelines, especially those following a typical "Bronze-Silver-Gold" data lakehouse architecture, Silver tables are often considered a more refined version of the raw or Bronze data. Silver tables may include data cleansing, schema enforcement, and some initial transformations. Gold tables, on the other hand, typically represent a stage where data is further enriched, aggregated, and processed to provide valuable insights for analytical purposes. This could indeed involve more aggregations compared to Silver tables.


NEW QUESTION # 299
......

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