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

SectionWeightObjectives
Topic 1: 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
Topic 2: 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 3: Productionizing Data Pipelines18%- Deployment and CI/CD
  • 1. Databricks Asset Bundles
  • 2. Version control integration
- 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
Topic 4: 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 5: Development and Ingestion30%- Notebook development fundamentals
  • 1. Data exploration and validation
  • 2. Using PySpark and Spark SQL
- Data ingestion patterns and methods
  • 1. Connecting to external data sources
  • 2. Batch and streaming ingestion
  • 3. Delta Lake basics and usage

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
Identify the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATE for a constraint violation.
A data engineer has created an ETL pipeline using Delta Live table to manage their company travel reimbursement detail, they want to ensure that the if the location details has not been provided by the employee, the pipeline needs to be terminated.
How can the scenario be implemented?

Answer: B


NEW QUESTION # 33
Which of the following data lakehouse features results in improved data quality over a traditional data lake?

Answer: B

Explanation:
Explanation
One of the key features of a data lakehouse that results in improved data quality over a traditional data lake is its support for ACID (Atomicity, Consistency, Isolation, Durability) transactions. ACID transactions provide data integrity and consistency guarantees, ensuring that operations on the data are reliable and that data is not left in an inconsistent state due to failures or concurrent access. In a traditional data lake, such transactional guarantees are often lacking, making it challenging to maintain data quality, especially in scenarios involving multiple data writes, updates, or complex transformations. A data lakehouse, by offering ACID compliance, helps maintain data quality by providing strong consistency and reliability, which is crucial for data pipelines and analytics.


NEW QUESTION # 34
Which of the following is stored in the Databricks customer's cloud account?

Answer: C

Explanation:
The only option that is stored in the Databricks customer's cloud account is data. Data is stored in the customer's cloud storage service, such as AWS S3 or Azure Data Lake Storage. The customer has full control and ownership of their data and can access it directly from their cloud account.
Option A is not correct, as the Databricks web application is hosted and managed by Databricks on their own cloud infrastructure. The customer does not need to install or maintain the web application, but only needs to access it through a web browser.
Option B is not correct, as the cluster management metadata is stored and managed by Databricks on their own cloud infrastructure. The cluster management metadata includes information such as cluster configuration, status, logs, and metrics. The customer can view and manage their clusters through the Databricks web application, but does not have direct access to the cluster management metadata.
Option C is not correct, as the repos are stored and managed by Databricks on their own cloud infrastructure.
Repos are version-controlled repositories that store code and data files for Databricks projects. The customer can create and manage their repos through the Databricks web application, but does not have direct access to the repos.
Option E is not correct, as the notebooks are stored and managed by Databricks on their own cloud infrastructure. Notebooks are interactive documents that contain code, text, and visualizations for Databricks workflows. The customer can create and manage their notebooks through the Databricks web application, but does not have direct access to the notebooks.
:
Databricks Architecture
Databricks Data Sources
Databricks Repos
[Databricks Notebooks]
[Databricks Data Engineer Professional Exam Guide]


NEW QUESTION # 35
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

Answer: B

Explanation:
Explanation
https://docs.databricks.com/en/structured-streaming/delta-lake.html


NEW QUESTION # 36
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: B

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.
:
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 # 37
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