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

Section	Weight	Objectives
Topic 1: Development and Ingestion	30%	- Data ingestion patterns and methods 1. Batch and streaming ingestion 2. Delta Lake basics and usage 3. Connecting to external data sources - Notebook development fundamentals 1. Using PySpark and Spark SQL 2. Data exploration and validation
Topic 2: Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Data layout and optimization: partitioning, file sizing, caching 2. Compute options: clusters, SQL warehouses, serverless 3. Workspace navigation and management
Topic 3: Data Governance & Quality	11%	- Unity Catalog implementation 1. Data governance model 2. Permissions and access control - Data quality and reliability 1. Schema enforcement and evolution 2. Data validation and quality checks
Topic 4: Productionizing Data Pipelines	18%	- Deployment and CI/CD 1. Version control integration 2. Databricks Asset Bundles - 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
Topic 5: Data Processing & Transformations	31%	- Data transformation techniques 1. Complex data processing logic 2. DataFrame operations and transformations 3. Aggregations, joins, and window functions - Query optimization and performance 1. Understanding query plans 2. Optimization strategies

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q163-Q168):

NEW QUESTION # 163
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. FROM CSV
B. None of these lines of code are needed to successfully complete the task
C. USING DELTA
D. USING CSV
E. FROM "path/to/csv"

Answer: D

NEW QUESTION # 164
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

Answer: B

Explanation:
The best practice is to use "Complete" as output mode instead of "append" when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.

NEW QUESTION # 165
A pipeline uses COPY INTO to ingest CSV files from cloud object storage into a Unity Catalog Delta table. Some files are occasionally re-uploaded with corrections using the same filename.
The engineer needs the corrected data to be ingested as soon as it becomes available.
What should the engineer do?

A. Reload all files from the source path during every run.
B. Load corrections as new files and apply updates to the target table.
C. Overwrite the target Delta table during every run.
D. Recreate the target table before every run.

Answer: B

Explanation:
COPY INTO is idempotent by default and tracks files that have already been loaded into the target table. A corrected file uploaded under the same name and path can therefore be treated as previously processed and skipped. The reliable pattern is to publish the correction under a new unique filename so that ingestion detects it as a new file. The pipeline can then use a business key with MERGE, UPDATE, or equivalent transformation logic to apply the corrected values to the target Delta table. Overwriting or recreating the complete table risks unnecessary data loss, increases processing cost, and disrupts downstream consumers. Reloading every historical source file is also inefficient and can introduce duplicates. Therefore, option B supports incremental ingestion while applying corrections safely and efficiently.

NEW QUESTION # 166
A company has a strict 15-minute Service Level Agreement (SLA) for updating its currency exchange dashboard. The source data arrives in small increments every few minutes. The data engineering team needs a triggering strategy for their Lakeflow Job that keeps end-to-end latency within the SLA while minimizing compute cost and DBU consumption. Which triggering strategy for the Lakeflow Job is the recommended way to meet this 15-minute SLA?

A. Use a Continuous trigger to ensure data is processed as soon as it arrives.
B. Use a File Arrival trigger configured to fire every time a single JSON file lands in the bucket.
C. Use a Scheduled trigger set to run every 12 minutes, with the streaming task configured to use Trigger.AvailableNow.
D. Use a Scheduled trigger set to run every 1 minute to ensure the SLA is never at risk.

Answer: C

Explanation:
A scheduled Trigger.AvailableNow workload is the most cost-efficient pattern when minute-level latency is acceptable. Running every 12 minutes provides a buffer within the 15-minute SLA while allowing compute to stop after processing all available data. Continuous or per-file execution would consume more compute.

NEW QUESTION # 167
In a healthcare provider organization using Delta Lake to store electronic health records (EHRs), a data analyst needs to analyze a snapshot of the patient_records table from two weeks ago before some recent data corrections were applied. What approach should the Data Engineer take to allow the analyst to query that specific prior version?

A. Use the VACUUMcommand to remove all versions of the table older than two weeks, then the analyst can query the remaining version.
B. Truncate the table to remove all data, then reload the data from two weeks ago into the truncated table for the analyst to query.
C. Identify the version number corresponding to two weeks ago from the Delta transaction log, share that version number with the analyst to query using VERSION AS OF syntax, or export that version to a new Delta table for the analyst to query.
D. Restore the table to the version from two weeks ago using the RESTOREcommand, and have the analyst query the restored table.

Answer: C

NEW QUESTION # 168
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