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

Section	Objectives
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 Processing and Transformations	- PySpark DataFrame transformations - Delta Lake fundamentals (tables, transactions, optimization) - User-defined functions (UDFs) - Apache Spark SQL operations (joins, aggregations, filtering)
Data Ingestion and ELT Development	- Data ingestion using Spark SQL and PySpark - ETL patterns and transformations - Handling structured and semi-structured data
Data Governance and Quality	- Data access control and governance - Data quality concepts and management - Unity Catalog basics

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q287-Q292):

NEW QUESTION # 287
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

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

A. org.apache.spark.sql.jdbc
B. org.apache.spark.sql.sqlite
C. DELTA
D. sqlite
E. autoloader

Answer: D

Explanation:
In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is "sqlite" because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, "sqlite" would be appropriate here. References:
* Create a table using JDBC
* JDBC connection string
* SQLite JDBC driver

NEW QUESTION # 288
A data engineer deploys a multi-task Databricks job that orchestrates three notebooks. One task intermittently fails with Exit Code 1 but succeeds on retry. The engineer needs to collect detailed logs for the failing attempts, including stdout/stderr and cluster lifecycle context, and share them with the platform team.
What steps the data engineer needs to follow using built-in tools?

A. Download worker logs directly from the Spark UI and ignore driver logs, as worker logs contain stdout
/stderr for all tasks and cluster events.
B. From the job run details page, export the job's logs or configure log delivery; then retrieve the compute driver logs and event logs from the compute details page to correlate stdout/stderr with cluster events.
C. Export the notebook run results to HTML; this bundle includes complete stdout, stderr, and cluster event history across all tasks.
D. Use the notebook interactive debugger to re-run the entire multi-task job, and capture step-through traces for the failing task.

Answer: B

Explanation:
The recommended way to troubleshoot and collect detailed job logs is through the Job Run Details page in Databricks. From there, engineers can export run logs or configure automatic log delivery to a storage destination. The driver and event logs available under compute details provide stdout, stderr, and cluster lifecycle context required for root-cause analysis.
Reference Source: Databricks Jobs Monitoring and Logging Documentation - "Access driver logs and configure log delivery."

NEW QUESTION # 289
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?

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

Answer: A

Explanation:
Explanation
To achieve this, the data engineer can set up an Alert in the Databricks workspace that triggers when the query results exceed the threshold of 100 NULL values. They can create a new webhook alert destination in the Alert's configuration settings and provide the necessary messaging webhook URL to receive notifications.
When the Alert is triggered, it will send a message to the configured webhook URL, which will then notify the entire team of the issue.

NEW QUESTION # 290
Which Databricks feature can be used to check the data sources and tables used in a workspace?

A. Use the lineage feature to visualize a graph that shows all dependencies, including where the table is used in notebooks, other tables, and reports.
B. Use the lineage feature to visualize a graph that highlights where the table is used only in notebooks.
C. Use the audit logs feature to review user activities, such as who queried or modified a table, instead of visualizing dependencies between data objects.
D. Use the lineage feature to visualize a graph that highlights where the table is used only in reports.

Answer: A

Explanation:
The lineage feature provides a visual graph of end-to-end data dependencies, showing data sources, tables, notebooks, and downstream consumers such as reports, enabling visibility into how data is produced and used across the workspace.

NEW QUESTION # 291
A data engineer must support self-serve BI dashboards for hundreds of business users who run ad hoc, high-concurrency SQL queries throughout the day against governed Unity Catalog tables.
The team needs a near-instant start, autoscaling without manual tuning, and the best performance for SQL while minimizing operational overhead. Which Databricks compute should the data engineer use?

A. Jobs compute clusters triggered by dashboards on a schedule.
B. SQL Warehouse (Classic) with a fixed cluster size.
C. SQL Warehouse (Serverless) with Photon enabled.
D. All-purpose classic cluster with manual autoscaling enabled.

Answer: C

Explanation:
Serverless SQL Warehouses provide instant startup, automatic scaling, and optimized performance with Photon for high-concurrency BI workloads, while minimizing operational overhead and management effort.

NEW QUESTION # 292
......

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