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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q261-Q266):

NEW QUESTION # 261
A data engineer is designing a cost-optimized, event-driven pipeline. They configure a Lakeflow Job with a File Arrival trigger to watch an Amazon S3 bucket. The job runs a notebook that uses Auto Loader with trigger(availableNow=True) to ingest data into a Bronze table.
What is the technical relationship between the File Arrival trigger and Auto Loader in this integration pattern?

A. Auto Loader must be configured in File Notification mode to work with a Lakeflow Job File Arrival trigger.
B. Using a File Arrival trigger requires the engineer to disable Auto Loader checkpointing to prevent the job from processing the same file multiple times.
C. The File Arrival trigger automatically passes the specific file path of the new arrival to Auto Loader, allowing the engineer to omit the source-path configuration from the code.
D. The File Arrival trigger starts the job run, while Auto Loader uses its internal checkpoint to independently identify and process only the new files that have arrived since the last successful commit.

Answer: D

NEW QUESTION # 262
A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?

A. Both teams would be able to collaborate on projects in real-time
B. Both teams would reorganize to report to the same department
C. Both teams would autoscale their work as data size evolves
D. Both teams would respond more quickly to ad-hoc requests
E. Both teams would use the same source of truth for their work

Answer: E

Explanation:
Explanation
A data lakehouse is designed to unify the data engineering and data analysis architectures by integrating features of both data lakes and data warehouses. One of the key benefits of a data lakehouse is that it provides a common, centralized data repository (the "lake") that serves as a single source of truth for data storage and analysis. This allows both data engineering and data analysis teams to work with the same consistent data sets, reducing discrepancies and ensuring that the reports generated by both teams are based on the same underlying data.

NEW QUESTION # 263
A data engineer needs to combine sales data from an on-premises PostgreSQL database with customer data in Azure Synapse for a comprehensive report. The goal is to avoid data duplication and ensure up-to-date information. How should the data engineer achieve this using Databricks?

A. Export data from both sources to CSV files and upload them to Databricks
B. Use Lakehouse Federation to query both data sources directly
C. Manually synchronize data from both sources into a single database
D. Develop custom ETL pipelines to ingest data into Databricks

Answer: B

Explanation:
Lakehouse Federation allows Databricks to directly query external data sources like PostgreSQL and Azure Synapse without duplicating data. This ensures the report always uses up-to-date information while avoiding the overhead and cost of data movement.

NEW QUESTION # 264
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to a data analytics dashboard for a retail use case. The job has a Databricks SQL query that returns the number of store-level records where sales is equal to zero. The data engineer wants their entire team to be notified via a messaging webhook whenever this value is greater than 0.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of stores with $0 in sales is greater than zero?

A. They can set up an Alert without notifications.
B. They can set up an Alert with a custom template.
C. They can set up an Alert with a new webhook alert destination.
D. They can set up an Alert with one-time notifications.
E. They can set up an Alert with a new email alert destination.

Answer: C

Explanation:
A webhook alert destination is a notification destination that allows Databricks to send HTTP POST requests to a third-party endpoint when an alert is triggered. This enables the data engineer to integrate Databricks alerts with their preferred messaging or collaboration platform, such as Slack, Microsoft Teams, or PagerDuty. To set up a webhook alert destination, the data engineer needs to create and configure a webhook connector in their messaging platform, and then add the webhook URL to the Databricks notification destination. After that, the data engineer can create an alert for their Databricks SQL query, and select the webhook alert destination as the notification destination. The alert can be configured with a custom condition, such as when the number of stores with $0 in sales is greater than zero, and a custom message template, such as "Alert: {number_of_stores} stores have $0 in sales". The alert can also be configured with a recurrence interval, such as every hour, to check the query result periodically. When the alert condition is met, the data engineer and their team will receive a notification via the messaging webhook, with the custom message and a link to the Databricks SQL query. The other options are either not suitable for sending notifications via a messaging webhook (A, B, E), or not suitable for sending recurring notifications ©. References: Databricks Documentation - Manage notification destinations, Databricks Documentation - Create alerts for Databricks SQL queries, Databricks Documentation - Configure alert conditions and messages.

NEW QUESTION # 265
Which method should a Data Engineer apply to ensure Workflows are being triggered on schedule?

A. Scheduled Workflows can reduce resource consumption and expense since the cluster runs only long enough to execute the pipeline.
B. Scheduled Workflows require an always-running cluster, which is more expensive but reduces processing latency.
C. Scheduled Workflows process data as it arrives at configured sources.
D. Scheduled Workflows run continuously until manually stopped.

Answer: A

Explanation:
Scheduled Workflows in Databricks are designed to trigger jobs at specified intervals. These workflows can be configured to start a cluster when the job is scheduled, and the cluster will only run for the duration of the task. Once the workflow completes, the cluster can be terminated, which helps to reduce resource consumption and costs since you are only paying for the cluster while the workflow is running.

NEW QUESTION # 266
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