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

SectionWeightObjectives
Development and Ingestion30%- 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. Data exploration and validation
  • 2. Using PySpark and Spark SQL
Data Governance & Quality11%- Data quality and reliability
  • 1. Data validation and quality checks
  • 2. Schema enforcement and evolution
- Unity Catalog implementation
  • 1. Data governance model
  • 2. Permissions and access control
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
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
Data Processing & Transformations31%- Query optimization and performance
  • 1. Optimization strategies
  • 2. Understanding query plans
- Data transformation techniques
  • 1. Aggregations, joins, and window functions
  • 2. Complex data processing logic
  • 3. DataFrame operations and transformations

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q117-Q122):

NEW QUESTION # 117
Which of the following commands can be used to write data into a Delta table while avoiding the writing of duplicate records?

Answer: B

Explanation:
The MERGE command can be used to upsert data from a source table, view, or DataFrame into a target Delta table. It allows you to specify conditions for matching and updating existing records, and inserting new records when no match is found. This way, you can avoid writing duplicate records into a Delta table1. The other commands (DROP, IGNORE, APPEND, INSERT) do not have this functionality and may result in duplicate records or data loss234. Reference: 1: Upsert into a Delta Lake table using merge | Databricks on AWS 2: SQL DELETE | Databricks on AWS 3: SQL INSERT INTO | Databricks on AWS 4: SQL UPDATE | Databricks on AWS


NEW QUESTION # 118
A data engineer has left the organization. The data team needs to transfer ownership of the data engineer's Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team.
Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?

Answer: D

Explanation:
The workspace administrator is the only individual who can transfer ownership of the Delta tables in Data Explorer, assuming the original data engineer no longer has access. The workspace administrator has the highest level of permissions in the workspace and can manage all resources, users, and groups. The other options are either not possible or not sufficient to perform the ownership transfer. The Databricks account representative is not involved in the workspace management. The transfer is possible and not dependent on the original data engineer. The new lead data engineer may not have the necessary permissions to access or modify the Delta tables, unless granted by the workspace administrator or the original data engineer before leaving. References: Workspace access control, Manage Unity Catalog object ownership.


NEW QUESTION # 119
A data engineer is maintaining an ETL pipeline code with a GitHub repository linked to their Databricks account. The data engineer wants to deploy the ETL pipeline to production as a databricks workflow. Which approach should the data engineer use?

Answer: D

Explanation:
The best practice for deploying ETL pipelines to production is to use Databricks Asset Bundles (DAB) with GitHub integration. DAB provides a declarative YAML-based structure for defining workflows and resources, supports CI/CD, and integrates seamlessly with GitHub, enabling version-controlled, automated deployments to production.


NEW QUESTION # 120
A data analyst has developed a query that runs against Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?

Answer: A

Explanation:
The spark.sql operation allows the data engineering team to run a SQL query and return the result as a PySpark DataFrame. This way, the data engineering team can use the same query that the data analyst has developed and operate with the results in PySpark. For example, the data engineering team can use spark.sql("SELECT * FROM sales") to get a DataFrame of all the records from the sales Delta table, and then apply various tests or transformations using PySpark APIs. The other options are either not valid operations (A, D), not suitable for running a SQL query (B, E), or not returning a DataFrame (A). Reference: Databricks Documentation - Run SQL queries, Databricks Documentation - Spark SQL and DataFrames.


NEW QUESTION # 121
A data engineer has written a function in a Databricks Notebook to calculate the population of bacteria in a given medium.

Analysts use this function in the notebook and sometimes provide input arguments of the wrong data type, which can cause errors during execution.
Which Databricks feature will help the data engineer quickly identify if an incorrect data type has been provided as input?

Answer: C


NEW QUESTION # 122
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