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

SectionWeightObjectives
Topic 1: Data Processing & Transformations31%- Query optimization and performance
  • 1. Optimization strategies
  • 2. Understanding query plans
- Data transformation techniques
  • 1. DataFrame operations and transformations
  • 2. Complex data processing logic
  • 3. Aggregations, joins, and window functions
Topic 2: 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. Using PySpark and Spark SQL
  • 2. Data exploration and validation
Topic 3: 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 4: Productionizing Data Pipelines18%- 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 5: Databricks Intelligence Platform10%- Platform architecture and core concepts
  • 1. Compute options: clusters, SQL warehouses, serverless
  • 2. Workspace navigation and management
  • 3. Data layout and optimization: partitioning, file sizing, caching

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q47-Q52):

NEW QUESTION # 47
Which of the following is a benefit of the Databricks Lakehouse Platform embracing open source technologies?

Answer: D

Explanation:
One of the benefits of the Databricks Lakehouse Platform embracing open source technologies is that it avoids vendor lock-in. This means that customers can use the same open source tools and frameworks across different cloud providers, and migrate their data and workloads without being tied to a specific vendor. The Databricks Lakehouse Platform is built on open source projects such as Apache Spark, Delta Lake, MLflow, and Redash, which are widely used and trusted by millions of developers. By supporting these open source technologies, the Databricks Lakehouse Platform enables customers to leverage the innovation and community of the open source ecosystem, and avoid the risk of being locked into proprietary or closed solutions. The other options are either not related to open source technologies (A, B, C, D), or not benefits of the Databricks Lakehouse Platform (A, B). References: Databricks Documentation - Built on open source, Databricks Documentation - What is the Lakehouse Platform?, Databricks Blog - Introducing the Databricks Lakehouse Platform.


NEW QUESTION # 48
What is the structure of an Asset Bundle?

Answer: C

Explanation:
An Asset Bundle in Databricks is structured as a YAML configuration file that defines the project's artifacts, resources, and configurations. This format allows reproducibility, automation, and portability of projects across workspaces, making it the standard approach for production pipelines.


NEW QUESTION # 49
An organization has data stored across multiple external systems, including MySQL, Amazon Redshift, and Google BigQuery. The data engineer wants to perform analytics without ingesting directly into Databricks, ensuring unified governance and minimizing data duplication. Which feature of Databricks enables querying these external data sources while maintaining centralized governance?

Answer: B

Explanation:
Lakehouse Federation allows Databricks to query external data sources like MySQL, Redshift, and BigQuery without moving or duplicating data, while maintaining centralized governance through Unity Catalog.


NEW QUESTION # 50
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: E

Explanation:
Explanation
https://www.w3schools.com/python/python_functions.asp


NEW QUESTION # 51
A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.
Which of the following describes why Auto Loader inferred all of the columns to be of the string type?

Answer: A

Explanation:
Explanation
JSON data is a text-based format that uses strings to represent all values. When Auto Loader infers the schema of JSON data, it assumes that all values are strings. This is because Auto Loader cannot determine the type of a value based on its string representation. https://docs.databricks.com/en/ingestion/auto-loader/schema.html Forexample, the following JSON string represents a value that is logically a boolean: JSON "true" Use code with caution. Learn more However, Auto Loader would infer that the type of this value is string. This is because Auto Loader cannot determine that the value is a boolean based on its string representation. In order to get Auto Loader to infer the correct types for columns, the data engineer can provide type inference or schema hints. Type inference hints can be used to specify the types of specific columns. Schema hints can be used to provide the entire schema of the data.
Therefore, the correct answer is B. JSON data is a text-based format.


NEW QUESTION # 52
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