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

SectionObjectives
Data Ingestion and ELT Development- ETL patterns and transformations
- Handling structured and semi-structured data
- Data ingestion using Spark SQL and PySpark
Productionizing Data Pipelines- Pipeline deployment and operationalization
- Databricks Workflows / Jobs orchestration
- Scheduling and monitoring jobs
Databricks Lakehouse Platform Fundamentals- Clusters, notebooks, and basic Databricks environment usage
- Workspace, architecture, and core platform concepts
Data Governance and Quality- Data quality concepts and management
- Data access control and governance
- Unity Catalog basics
Data Processing and Transformations- Apache Spark SQL operations (joins, aggregations, filtering)
- User-defined functions (UDFs)
- PySpark DataFrame transformations
- Delta Lake fundamentals (tables, transactions, optimization)

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q294-Q299):

NEW QUESTION # 294
A Databricks single-task workflow fails due to an error in a notebook. The data engineer fixes the mistake in the notebook.
What should the data engineer do to rerun the workflow?

Answer: D

Explanation:
Rerunning a failed workflow after fixing the notebook requires repairing the run so the task is re- executed with the updated code, without recreating the entire workflow or changing cluster configuration.


NEW QUESTION # 295
Which of the following SQL keywords can be used to convert a table from a long format to a wide format?

Answer: B

Explanation:
The SQL keyword that can be used to convert a table from a long format to a wide format is PIVOT. The PIVOT clause is used to rotate the rows of a table into columns of a new table1. The PIVOT clause can aggregate the values of a column based on the distinct values of another column, and use those values as the column names of the new table1. The PIVOT clause can be useful for transforming data from a long format, where each row represents an observation with multiple attributes, to a wide format, where each row represents an observation with a single attribute and multiple values2. For example, the PIVOT clause can be used to convert a table that contains the sales of different products by different regions into a table that contains the sales of each product by each region as separate columns1.
The other options are not suitable for converting a table from a long format to a wide format. CONVERT is a function that can be used to change the data type of an expression3. WHERE is a clause that can be used to filter the rows of a table based on a condition4. TRANSFORM is a keyword that can be used to apply a user- defined function to a group of rows in a table5. SUM is a function that can be used to calculate the total of a numeric column.
:
1: PIVOT | Databricks on AWS
2: Reshaping Data - Long vs Wide Format | Databricks on AWS
3: CONVERT | Databricks on AWS
4: WHERE | Databricks on AWS
5: TRANSFORM | Databricks on AWS
6: [SUM | Databricks on AWS]


NEW QUESTION # 296
A data engineer has been provided a PySpark DataFrame named df with columns product and revenue. The data engineer needs to compute complex aggregations to determine each product's total revenue, average revenue, and transaction count.
Which code snippet should the data engineer use?

Answer: D


NEW QUESTION # 297
What are the transformations typically included in building the Bronze layer ?

Answer: A

Explanation:
In the Medallion Architecture promoted by Databricks, the Bronze layer represents the raw ingestion layer where data is stored in its original form with minimal transformation. The primary goal is to ensure data fidelity and traceability , not to apply business logic or heavy transformations. Typical operations in the Bronze layer include schema inference (or enforcement), ingestion of raw data, and the addition of metadata columns such as ingestion timestamp, load date/time, and process identifiers. These metadata fields help track lineage, enable debugging, and support incremental processing downstream. Extensive data cleansing (A), aggregations (B), and business transformations (C) are intentionally deferred to the Silver and Gold layers , where data is refined and structured for analytics. By keeping Bronze data close to the source and append-only, organizations maintain a reliable audit trail and can reprocess data if needed. Therefore, adding operational metadata like load timestamps and process IDs is the correct characteristic of Bronze layer transformations.
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NEW QUESTION # 298
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Development mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

Answer: C


NEW QUESTION # 299
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