100% Pass 2026 Reliable Databricks Databricks-Certified-Data-Engineer-Associate: Databricks Certified Data Engineer Associate Exam Test Lab Questions

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

Section	Weight	Objectives
Topic 1: Development and Ingestion	30%	- Notebook development fundamentals 1. Data exploration and validation 2. Using PySpark and Spark SQL - Data ingestion patterns and methods 1. Delta Lake basics and usage 2. Batch and streaming ingestion 3. Connecting to external data sources
Topic 2: 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 3: Productionizing Data Pipelines	18%	- Deployment and CI/CD 1. Databricks Asset Bundles 2. Version control integration - 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
Topic 4: 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
Topic 5: Data Processing & Transformations	31%	- Data transformation techniques 1. DataFrame operations and transformations 2. Complex data processing logic 3. Aggregations, joins, and window functions - Query optimization and performance 1. Optimization strategies 2. Understanding query plans

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q123-Q128):

NEW QUESTION # 123
A data engineer runs a statement every day to copy the previous day's sales into the table transactions. Each day's sales are in their own file in the location "/transactions/raw".
Today, the data engineer runs the following command to complete this task:

After running the command today, the data engineer notices that the number of records in table transactions has not changed.
Which of the following describes why the statement might not have copied any new records into the table?

A. The previous day's file has already been copied into the table.
B. The PARQUET file format does not support COPY INTO.
C. The names of the files to be copied were not included with the FILES keyword.
D. The COPY INTO statement requires the table to be refreshed to view the copied rows.
E. The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.

Answer: A

Explanation:
Explanation
https://docs.databricks.com/en/ingestion/copy-into/index.html The COPY INTO SQL command lets you load data from a file location into a Delta table. This is a re-triable and idempotent operation; files in the source location that have already been loaded are skipped. if there are no new records, the only consistent choice is C no new files were loaded because already loaded files were skipped.

NEW QUESTION # 124
In which of the following file formats is data from Delta Lake tables primarily stored?

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

Answer: C

Explanation:
Delta Lake is an open source project that provides ACID transactions, time travel, and other features on top of Apache Parquet, a columnar file format that is widely used for big data analytics. Delta Lake uses versioned Parquet files to store your data in your cloud storage, along with JSON files as transaction logs and checkpoint files to track the changes and ensure data integrity. Delta Lake is compatible with any Apache Hive compatible file format, such as CSV, JSON, or AVRO, but it primarily stores data as Parquet files for better performance and compression. Reference: How to Create Delta Lake tables, 5 reasons to choose Delta Lake format (on Databricks), Parquet vs Delta format in Azure Data Lake Gen 2 store, What is Delta Lake? - Azure Databricks, Lakehouse and Delta tables - Microsoft Fabric

NEW QUESTION # 125
What Databricks feature can be used to check the data sources and tables used in a workspace?

A. Do not use the lineage feature as it only tracks activity from the last 3 months and will not provide full details on dependencies.
B. Use the lineage feature to visualize a graph that highlights where the table is used only in reports.
C. Use the lineage feature to visualize a graph that highlights where the table is used only in notebooks,
D. Use the lineage feature to visualize a graph that shows all dependencies, including where the table is used in notebooks, other tables, and reports.

Answer: D

NEW QUESTION # 126
What is the structure of an Asset Bundle?

A. A compressed archive (ZIP) that solely contains workspace assets without any accompanying metadata.
B. A Docker image containing runtime environments and the source code of the assets
C. A YAML configuration file that specifies the artifacts, resources, and configurations for the project.
D. A single plain text file enumerating the names of assets to be migrated to a new workspace.

Answer: C

NEW QUESTION # 127
A company has a strict 15-minute service-level agreement for updating its currency-exchange dashboard. Source data arrives in small increments every few minutes. The team needs a Lakeflow Jobs trigger strategy that keeps end-to-end latency within the SLA while minimizing compute cost and DBU consumption.
Which strategy is recommended?

A. Use a continuous trigger.
B. Use a scheduled trigger every 1 minute.
C. Use a file-arrival trigger whenever a single JSON file arrives.
D. Use a scheduled trigger every 12 minutes, with the streaming task configured to use Trigger.AvailableNow.

Answer: D

NEW QUESTION # 128
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