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

SectionWeightObjectives
Topic 1: 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
Topic 2: Data Governance & Quality11%- Data quality and reliability
  • 1. Schema enforcement and evolution
  • 2. Data validation and quality checks
- Unity Catalog implementation
  • 1. Data governance model
  • 2. Permissions and access control
Topic 3: 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
Topic 4: Productionizing Data Pipelines18%- Monitoring and troubleshooting
  • 1. Pipeline reliability and recovery
  • 2. Logging and error handling
- Workflow orchestration
  • 1. Scheduling, triggers, and dependencies
  • 2. Lakeflow Jobs creation and management
- Deployment and CI/CD
  • 1. Databricks Asset Bundles
  • 2. Version control integration
Topic 5: Data Processing & Transformations31%- Data transformation techniques
  • 1. Complex data processing logic
  • 2. Aggregations, joins, and window functions
  • 3. DataFrame operations and transformations
- Query optimization and performance
  • 1. Understanding query plans
  • 2. Optimization strategies

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q25-Q30):

NEW QUESTION # 25
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

Answer: B


NEW QUESTION # 26
A global retail company sells products across multiple categories (e.g., Electronics, Clothing) and regions (e.g., North, South, East, West). The sales team has provided the data engineer with a PySpark dataframe named sales_df as below and the team wants the data engineer to analyze the sales data to help them make strategic decisions.

Calculate the total sales amount for each product category and store the results in a new dataframe called category_sales.
What will generate the expected result of category_sales?

Answer: A

Explanation:
The correct approach is to group by the product category and then aggregate the sum of sales_amount, aliasing it as total_sales_amount. This produces the expected totals: 500 for Electronics and 900 for Clothing.


NEW QUESTION # 27
A Data Engineer is building a simple data pipeline using Delta Live Tables (DLT) in Databricksto ingest customer dat a. The raw customer data is stored in a cloud storage location in JSON format. The task is to create a DLT pipeline that reads the rawJSON data and writes it into a Delta table for further processing.
Which code snippet will correctly ingest the raw JSON data and create a Delta table using DLT?

Answer: D


NEW QUESTION # 28
A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:
DROP TABLE IF EXISTS my_table;
After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.
Which of the following describes why all of these files were deleted?

Answer: C

Explanation:
The reason why all of the data files and metadata files were deleted from the file system after dropping the table is that the table was managed. A managed table is a table that is created and managed by Spark SQL. It stores both the data and the metadata in the default location specified by the spark.sql.warehouse.dir configuration property. When a managed table is dropped, both the data and the metadata are deleted from the file system.
Option B is not correct, as the size of the table's data does not affect the behavior of dropping the table. Whether the table's data is smaller or larger than 10 GB, the data files and metadata files will be deleted if the table is managed, and will be preserved if the table is external.
Option C is not correct, for the same reason as option B.
Option D is not correct, as an external table is a table that is created and managed by the user. It stores the data in a user-specified location, and only stores the metadata in the Spark SQL catalog. When an external table is dropped, only the metadata is deleted from the catalog, but the data files are preserved in the file system.
Option E is not correct, as a table must have a location to store the data. If the location is not specified by the user, it will use the default location for managed tables. Therefore, a table without a location is a managed table, and dropping it will delete both the data and the metadata.
:
Managing Tables
[Databricks Data Engineer Professional Exam Guide]


NEW QUESTION # 29
A data engineer has created a new database using the following command:
CREATE DATABASE IF NOT EXISTS customer360;
In which of the following locations will the customer360 database be located?

Answer: A

Explanation:
Explanation
dbfs:/user/hive/warehouse - which is the default location


NEW QUESTION # 30
......

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