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

Section	Weight	Objectives
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. Pipeline reliability and recovery 2. Logging and error handling
Data Governance & Quality	11%	- Data quality and reliability 1. Schema enforcement and evolution 2. Data validation and quality checks - Unity Catalog implementation 1. Permissions and access control 2. Data governance model
Databricks Intelligence Platform	10%	- 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
Development and Ingestion	30%	- Notebook development fundamentals 1. Using PySpark and Spark SQL 2. Data exploration and validation - Data ingestion patterns and methods 1. Batch and streaming ingestion 2. Connecting to external data sources 3. Delta Lake basics and usage
Data Processing & Transformations	31%	- Data transformation techniques 1. Aggregations, joins, and window functions 2. DataFrame operations and transformations 3. Complex data processing logic - Query optimization and performance 1. Understanding query plans 2. Optimization strategies

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q303-Q308):

NEW QUESTION # 303
A data engineer needs Task C to run ONLY if Task A succeeds AND Task B fails. Which dependency configuration implements this conditional logic?

A. Task C depends on Task A (All done) and Task B (All done) with a condition widget.
B. Task C depends on Task A (Success) with Task B listed as a negative dependency.
C. Create a Python task that checks dbutils.jobs.taskValues from Tasks A and B before running Task C.
D. Task C uses two dependency chains: Task A with All succeeded, Task B with at least one failed.

Answer: D

Explanation:
Use separate gate tasks: one runs only when Task A succeeds, and another runs only when Task B fails. Task C then depends on both gates with All succeeded, enforcing the exact A succeeded AND B failed condition. A single Run if setting applies collectively to all dependencies and cannot express mixed per-task outcomes.

NEW QUESTION # 304
A data engineer uploads a CSV file using the "Create or modify a table using file upload" option in Databricks. To avoid incorrect schema inference, they disable Automatically detect column types before creating a Unity Catalog-managed table. What is the outcome?

A. All columns are created with the STRING data type.
B. Table creation fails because column types are not specified.
C. Previously inferred column types are preserved.
D. Numeric columns are inferred as numeric types; others are STRING.

Answer: A

Explanation:
Disabling Automatically detect column types causes Databricks to create every uploaded CSV column as STRING, avoiding automatic numeric, date, or Boolean inference.

NEW QUESTION # 305
Which of the following benefits is provided by the array functions from Spark SQL?

A. An ability to work with data in a variety of types at once
B. An ability to work with an array of tables for procedural automation
C. An ability to work with complex, nested data ingested from JSON files
D. An ability to work with time-related data in specified intervals
E. An ability to work with data within certain partitions and windows

Answer: C

Explanation:
The array functions from Spark SQL are a subset of the collection functions that operate on array columns1. They provide an ability to work with complex, nested data ingested from JSON files or other sources2. For example, the explode function can be used to transform an array column into multiple rows, one for each element in the array3. The array_contains function can be used to check if a value is present in an array column4. The array_join function can be used to concatenate all elements of an array column with a delimiter. These functions can be useful for processing JSON data that may have nested arrays or objects. References: 1: Spark SQL, Built-in Functions - Apache Spark 2: Spark SQL Array Functions Complete List - Spark By Examples 3: Spark SQL Array Functions - Syntax and Examples - DWgeek.com 4: Spark SQL, Built-in Functions - Apache Spark : Spark SQL, Built-in Functions - Apache Spark : [Working with Nested Data Using Higher Order Functions in SQL on Databricks - The Databricks Blog]

NEW QUESTION # 306
A data engineer streams customer orders into a Kafka topic (orders_topic) and is currently writing the ingestion script of a DLT pipeline. The data engineer needs to ingest the data from Kafka brokers to DLT using Databricks What is the correct code for ingesting the data?

Answer: A

NEW QUESTION # 307
A data engineer needs to audit who made changes to a Delta table during the last seven days.
Which SQL statement returns the record of past operations, including timestamps, users, and operation types?

A. SHOW TBLPROPERTIES my_table
B. SELECT * FROM delta.\my_table/_delta_log/``
C. DESCRIBE DETAIL my_table
D. DESCRIBE HISTORY my_table

Answer: D

Explanation:
DESCRIBE HISTORY my_table returns the Delta table's operation history. Its result includes information such as the table version, operation timestamp, user identity when available, operation name, parameters, notebook or job details, and associated metrics. The engineer can filter the returned timestamps to analyze operations performed during the last seven days. SHOW TBLPROPERTIES displays configured table properties rather than transactional history. DESCRIBE DETAIL returns the current table metadata and storage characteristics, not a chronological audit of changes. Directly querying the _delta_log path is unnecessary, depends on file-level implementation details, and the displayed path is not the supported SQL statement for this audit requirement. Therefore, option C provides the supported and structured record of past Delta table operations.

NEW QUESTION # 308
......

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