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

SectionObjectives
Topic 1: Data Ingestion and ELT Development- Handling structured and semi-structured data
- ETL patterns and transformations
- Data ingestion using Spark SQL and PySpark
Topic 2: Data Processing and Transformations- Delta Lake fundamentals (tables, transactions, optimization)
- Apache Spark SQL operations (joins, aggregations, filtering)
- User-defined functions (UDFs)
- PySpark DataFrame transformations
Topic 3: Data Governance and Quality- Data access control and governance
- Unity Catalog basics
- Data quality concepts and management
Topic 4: Productionizing Data Pipelines- Scheduling and monitoring jobs
- Databricks Workflows / Jobs orchestration
- Pipeline deployment and operationalization
Topic 5: Databricks Lakehouse Platform Fundamentals- Workspace, architecture, and core platform concepts
- Clusters, notebooks, and basic Databricks environment usage

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q151-Q156):

NEW QUESTION # 151
Which of the following data lakehouse features results in improved data quality over a traditional data lake?

Answer: D

Explanation:
A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. One of the key features of a data lakehouse is that it supports ACID-compliant transactions, which means that it ensures data integrity, consistency, and isolation across concurrent read and write operations3. This feature results in improved data quality over a traditional data lake, which does not support transactions and may suffer from data corruption, duplication, or inconsistency due to concurrent or streaming data ingestion and processing . References: 1: What is a Data Lakehouse? - Databricks 2: What is a Data Lakehouse? Definition, features & benefits. - Qlik 3: ACID Transactions - Databricks : [Data Lake vs Data Warehouse: Key Differences] : [Data Lakehouse: The Future of Data Engineering]


NEW QUESTION # 152
A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.
Which of the following Git operations does the data engineer need to run to accomplish this task?

Answer: A

Explanation:
Explanation
From the docs:
In Databricks Repos, you can use Git functionality to:
Clone, push to, and pull from a remote Git repository.
Create and manage branches for development work, including merging, rebasing, and resolving conflicts.
Create notebooks&mdash;including IPYNB notebooks&mdash;and edit them and other files.
Visually compare differences upon commit and resolve merge conflicts.
Source: https://docs.databricks.com/en/repos/index.html


NEW QUESTION # 153
A data engineer has three tables in a Delta Live Tables (DLT) pipeline. They have configured the pipeline to drop invalid records at each table. They notice that some data is being dropped due to quality concerns at some point in the DLT pipeline. They would like to determine at which table in their pipeline the data is being dropped.
Which of the following approaches can the data engineer take to identify the table that is dropping the records?

Answer: D

Explanation:
One of the features of DLT is that it provides data quality metrics for each dataset in the pipeline, such as the number of records that pass or fail expectations, the number of records that are dropped, and the number of records that are written to the target. These metrics can be accessed from the DLT pipeline page, where the data engineer can click on each table and view the data quality statistics for the latest update or any previous update. This way, they can identify which table is dropping the records and why. Reference:
Monitor Delta Live Tables pipelines
Manage data quality with Delta Live Tables


NEW QUESTION # 154
A data engineer is writing a script that is meant to ingest new data from cloud storage. In the event of the Schema change, the ingestion should fail. It should fail until the changes downstream source can be found and verified as intended changes.
Which command will meet the requirements?

Answer: B


NEW QUESTION # 155
A streaming pipeline reads IoT sensor data and writes aggregated results to a Delta table. The engineer wants to ensure that late-arriving records within a certain time window are still processed correctly. Which Structured Streaming feature should be used?

Answer: D


NEW QUESTION # 156
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