Databricks - Databricks-Certified-Data-Engineer-Associate - Latest Databricks Certified Data Engineer Associate Exam Latest Test Preparation

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

SectionWeightObjectives
Databricks Intelligence Platform10%- Platform architecture and core concepts
  • 1. Compute options: clusters, SQL warehouses, serverless
  • 2. Data layout and optimization: partitioning, file sizing, caching
  • 3. Workspace navigation and management
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
Productionizing Data Pipelines18%- Monitoring and troubleshooting
  • 1. Logging and error handling
  • 2. Pipeline reliability and recovery
- Workflow orchestration
  • 1. Lakeflow Jobs creation and management
  • 2. Scheduling, triggers, and dependencies
- Deployment and CI/CD
  • 1. Databricks Asset Bundles
  • 2. Version control integration
Data Processing & Transformations31%- Query optimization and performance
  • 1. Optimization strategies
  • 2. Understanding query plans
- Data transformation techniques
  • 1. DataFrame operations and transformations
  • 2. Aggregations, joins, and window functions
  • 3. Complex data processing logic
Development and Ingestion30%- Data ingestion patterns and methods
  • 1. Connecting to external data sources
  • 2. Batch and streaming ingestion
  • 3. Delta Lake basics and usage
- Notebook development fundamentals
  • 1. Data exploration and validation
  • 2. Using PySpark and Spark SQL

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q183-Q188):

NEW QUESTION # 183
A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

Answer: E

Explanation:
Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks


NEW QUESTION # 184
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: D

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


NEW QUESTION # 185
Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?

Answer: E

Explanation:
Option C is the correct answer because Parquet files have a well-defined schema that is embedded within the data itself. This means that the data types and column names of the Parquet files are automatically detected and preserved when creating an external table from them. This also enables the use of SQL and other structured query languages to access and analyze the data. CSV files, on the other hand, do not have a schema embedded in them, and require specifying the schema explicitly or inferring it from the data when creating an external table from them. This can lead to errors or inconsistencies in the data types and column names, and also increase the processing time and complexity.


NEW QUESTION # 186
Which of the following is stored in the Databricks customer's cloud account?

Answer: D


NEW QUESTION # 187
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 # 188
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