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

Section	Weight	Objectives
Data Processing & Transformations	31%	- Query optimization and performance 1. Understanding query plans 2. Optimization strategies - Data transformation techniques 1. Complex data processing logic 2. Aggregations, joins, and window functions 3. DataFrame operations and transformations
Databricks Intelligence Platform	10%	- 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
Productionizing Data Pipelines	18%	- Deployment and CI/CD 1. Version control integration 2. Databricks Asset Bundles - Workflow orchestration 1. Lakeflow Jobs creation and management 2. Scheduling, triggers, and dependencies - Monitoring and troubleshooting 1. Pipeline reliability and recovery 2. Logging and error handling
Development and Ingestion	30%	- Notebook development fundamentals 1. Using PySpark and Spark SQL 2. Data exploration and validation - Data ingestion patterns and methods 1. Delta Lake basics and usage 2. Batch and streaming ingestion 3. Connecting to external data sources
Data Governance & Quality	11%	- 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

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Databricks-Certified-Data-Engineer-Associate Sample Questions, Free Sample Databricks-Certified-Data-Engineer-Associate Questions

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
In which of the following file formats is data from Delta Lake tables primarily stored?

A. JSON
B. A proprietary, optimized format specific to Databricks
C. Parquet
D. Delta
E. CSV

Answer: C

NEW QUESTION # 90
A Data Engineer is building a simple data pipeline using Delta Live Tables (DLT) in Databricks to ingest customer data. 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 raw JSON 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: C

Explanation:
The correct approach is to use @dlt.table to define a Delta Live Table and read the raw JSON data with spark.read.json("s3://..."). This properly ingests the JSON source data and materializes it as a managed Delta table in DLT.

NEW QUESTION # 91
A governance team is evaluating whether to use Unity Catalog attribute-based access control (ABAC) policies or manually applied row filters and column masks to protect sensitive data across their catalog. Why should the team use ABAC policies instead of manually applied row filters and column masks?

A. ABAC policies use governed tags to dynamically match tables and columns, so a single policy defined on a catalog automatically applies to all current and future tables within that catalog without per-table configuration.
B. ABAC policies allow row filters and column masks to be written in Python and Scala directly, whereas manually applied filters and masks can only use SQL user-defined functions.
C. ABAC policies can be applied to views and materialized views in addition to tables, whereas manually applied row filters and column masks can only be applied to tables.
D. ABAC policies support applying multiple distinct row filters to the same table for a single user at query time, whereas manually applied row filters are limited to one filter per table.

Answer: A

Explanation:
ABAC policies are defined centrally at the catalog or schema level and use governed tags to identify applicable data. They automatically protect matching current and future objects without configuring filters or masks on each table individually.

NEW QUESTION # 92
Which of the following describes the relationship between Gold tables and Silver tables?

A. Gold tables are more likely to contain valuable data than Silver tables.
B. Gold tables are more likely to contain truthful data than Silver tables.
C. Gold tables are more likely to contain a less refined view of data than Silver tables.
D. Gold tables are more likely to contain aggregations than Silver tables.
E. Gold tables are more likely to contain more data than Silver tables.

Answer: D

NEW QUESTION # 93
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?

A. dbfs:/user/hive/database/customer360
B. dbfs:/user/hive/customer360
C. More information is needed to determine the correct response
D. dbfs:/user/hive/warehouse

Answer: D

Explanation:
dbfs:/user/hive/warehouse Thereby showing "dbfs:/user/hive/warehouse/customer360.db The location of the customer360 database depends on the value of the spark.sql.warehouse.dir configuration property, which specifies the default location for managed databases and tables. If the property is not set, the default value is dbfs:/user/hive/warehouse. Therefore, the customer360 database will be located in dbfs:/user/hive/warehouse/customer360.db. However, if the property is set to a different value, such as dbfs:/user/hive/database, then the customer360 database will be located in dbfs:/user/hive/database/customer360.db. Thus, more information is needed to determine the correct response.
Option A is not correct, as dbfs:/user/hive/database/customer360 is not the default location for managed databases and tables, unless the spark.sql.warehouse.dir property is explicitly set to dbfs:/user/hive/database.
Option B is not correct, as dbfs:/user/hive/warehouse is the default location for the root directory of managed databases and tables, not for a specific database. The database name should be appended with .db to the directory path, such as dbfs:/user/hive/warehouse/customer360.db.
Option C is not correct, as dbfs:/user/hive/customer360 is not a valid location for a managed database, as it does not follow the directory structure specified by the spark.sql.warehouse.dir property.
Reference:
Databases and Tables
[Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 94
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