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

Section	Weight	Objectives
Topic 1: Productionizing Data Pipelines	18%	- 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 - Monitoring and troubleshooting 1. Logging and error handling 2. Pipeline reliability and recovery
Topic 2: Data Governance & Quality	11%	- Unity Catalog implementation 1. Permissions and access control 2. Data governance model - Data quality and reliability 1. Schema enforcement and evolution 2. Data validation and quality checks
Topic 3: Development and Ingestion	30%	- 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. Using PySpark and Spark SQL 2. Data exploration and validation
Topic 4: Databricks Intelligence Platform	10%	- 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
Topic 5: Data Processing & Transformations	31%	- Data transformation techniques 1. Complex data processing logic 2. Aggregations, joins, and window functions 3. DataFrame operations and transformations - Query optimization and performance 1. Optimization strategies 2. Understanding query plans

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q150-Q155):

NEW QUESTION # 150
A data engineer is developing an ETL process based on Spark SQL. The execution fails. The data engineer checks the Spark Ul and can see the ERRORS as follows:

Which two corrective actions should the data engineer perform to resolve this issue?
Choose 2 answers - (Q) Narrow the filters in order to collect less data in the query

A. Cache the dataset in order to boost the query performance
B. Upsize the driver node and deactivate autoshuffle partitions
C. Upsize the worker nodes and activate autoshuffle partitions
D. Fix the shuffle partitions to 50 to ensure the allocation

Answer: B,C

NEW QUESTION # 151
A data engineer is transforming a Bronze table containing API-response data into a Silver table. The Bronze table has a user_profile column of type STRING that contains JSON dat a. An example value is:
{"user_id":"12345","name":"John Smith","age":32,"email":"john@example.com"} The Silver table must make this data easily queryable for analytics without requiring JSON parsing in every downstream query.
Which approach standardizes this column for the Silver table?

A. SELECT
get_json_object(user_profile, 'user_id') AS user_id,
get_json_object(user_profile, 'name') AS name,
get_json_object(user_profile, 'age') AS age,
get_json_object(user_profile, 'email') AS email
FROM bronze_table;
B. SELECT
user_profile.user_id AS user_id,
user_profile.name AS name,
CAST(user_profile.age AS INT) AS age,
user_profile.email AS email
FROM bronze_table;
C. SELECT
from_json(
user_profile,
'user_id STRING, name STRING, age STRING, email STRING'
) AS parsed_profile
FROM bronze_table;
D. SELECT
get_json_object(user_profile, '$.user_id') AS user_id,
get_json_object(user_profile, '$.name') AS name,
CAST(get_json_object(user_profile, '$.age') AS INT) AS age,
get_json_object(user_profile, '$.email') AS email
FROM bronze_table;

Answer: D

Explanation:
Option C extracts each JSON field into a separate Silver-table column and casts age to the appropriate integer type. get_json_object accepts a JSON string and a JSONPath expression rooted at $; paths such as $.user_id and $.age therefore identify the required top-level fields correctly. This produces analytics-ready columns and prevents downstream queries from repeatedly parsing the original JSON string. Option A returns one nested struct and incorrectly defines age as STRING, so further projection or casting would still be required. Option B attempts field notation directly on a STRING column, which is not valid until the JSON has been parsed into a structured type. Option D omits the required $ JSONPath root and leaves age uncast. Therefore, option C most completely standardizes the source data for the Silver layer.

NEW QUESTION # 152
A data engineering team uses Declarative Automation Bundles (formerly Databricks Asset Bundles) to deploy the same codebase across dev, test, and prod environments. The team wants environment-specific behavior to be applied only through bundle configuration, without modifying notebooks, job definitions, or deployment logic during promotion. Which approach applies environment-specific configuration while meeting this requirement?

A. Deploy the same bundle once and edit job settings in the workspace UI for each environment after deployment.
B. Define bundle variables and reference them from bundle resources, then override variable values per environment using bundle targets.
C. Parameterize environment-specific values inside notebooks and set those parameters when each job run is triggered.
D. Use separate Git branches per environment so each branch can contain environment-specific configuration values.

Answer: B

Explanation:
Bundle variables hold reusable configuration values, and bundle targets override those values for dev, test, and prod at deployment time. This preserves one codebase and avoids modifying notebooks or workspace resources during promotion.

NEW QUESTION # 153
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?

A. Parquet files have a well-defined schema
B. CREATE TABLE AS SELECT statements cannot be used on files
C. Parquet files have the ability to be optimized
D. Parquet files will become Delta tables
E. Parquet files can be partitioned

Answer: A

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 CREATE TABLE AS SELECT, Parquet Files, CSV Files, Parquet vs. CSV

NEW QUESTION # 154
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?

A. Maintain workflow_config.json and deploy it using Databricks CLI
B. Maintain workflow_config.json and deploy it using Terraform
C. Manually create and manage the workflow in UI
D. Databricks Asset Bundles (DAB) + GitHub Integration

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 # 155
......

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