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

SectionObjectives
Topic 1: Productionizing Data Pipelines- Scheduling and monitoring jobs
- Databricks Workflows / Jobs orchestration
- Pipeline deployment and operationalization
Topic 2: Databricks Lakehouse Platform Fundamentals- Workspace, architecture, and core platform concepts
- Clusters, notebooks, and basic Databricks environment usage
Topic 3: Data Processing and Transformations- Delta Lake fundamentals (tables, transactions, optimization)
- PySpark DataFrame transformations
- Apache Spark SQL operations (joins, aggregations, filtering)
- User-defined functions (UDFs)
Topic 4: Data Ingestion and ELT Development- Handling structured and semi-structured data
- ETL patterns and transformations
- Data ingestion using Spark SQL and PySpark
Topic 5: 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 (Q299-Q304):

NEW QUESTION # 299
An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query. For the first week following the project's release, the manager wants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project's release.
Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project's release?

Answer: E

Explanation:
In Databricks SQL, you can use scheduled query executions to update your dashboards or enable routine alerts. By default, your queries do not have a schedule. To set the schedule, you can use the dropdown pickers to specify the frequency, period, starting time, and time zone. You can also choose to end the schedule on a certain date by selecting the End date checkbox and picking a date from the calendar. This way, you can ensure that the query does not run beyond the first week of the project's release and does not incur any additional cost. Option A is incorrect, as setting a limit to the number of DBUs does not stop the query from running. Option B is incorrect, as there is no option to end the schedule after a certain number of refreshes. Option C is incorrect, as there is a way to ensure the query does not cost the organization money beyond the first week of the project's release. Option D is incorrect, as setting a limit to the number of individuals who can manage the query's refresh schedule does not affect the query's execution or cost. Reference: Schedule a query, Schedule a query - Azure Databricks - Databricks SQL


NEW QUESTION # 300
Which of the following describes the type of workloads that are always compatible with Auto Loader?

Answer: B

Explanation:
Auto Loader is a Structured Streaming source that incrementally and efficiently processes new data files as they arrive in cloud storage. It supports both Python and SQL in Delta Live Tables, which are ideal for building streaming data pipelines. Auto Loader can handle near real-time ingestion of millions of files per hour and provide exactly-once guarantees when writing data into Delta Lake. Auto Loader is not designed for dashboard, machine learning, serverless, or batch workloads, which have different requirements and characteristics. References: What is Auto Loader?, Delta Live Tables


NEW QUESTION # 301
A data engineer observes that an ETL job in Databricks has increased its execution time from 5 minutes (baseline) to 12 minutes over the past week. Using the Lakeflow Jobs run history view, the engineer needs to identify whether the slowdown is consistent or intermittent and determine the root cause. Which action should the engineer take to analyze execution time trends against the baseline?

Answer: D

Explanation:
Comparing historical run durations shows whether the slowdown is recurring or sporadic, while compute metrics such as CPU, memory, and worker utilization help identify resource bottlenecks associated with slower runs.


NEW QUESTION # 302
A data engineer notices that a Spark job performing a join between a large table and a small lookup table is slow. The lookup table is only a few megabytes. Which Spark optimization technique should be applied to improve the performance of the join operation?

Answer: A


NEW QUESTION # 303
Which of the following is hosted completely in the control plane of the classic Databricks architecture?

Answer: C

Explanation:
The Databricks web application is the user interface that allows you to create and manage workspaces, clusters, notebooks, jobs, and other resources. It is hosted completely in the control plane of the classic Databricks architecture, which includes the backend services that Databricks manages in your Databricks account. The other options are part of the compute plane, which is where your data is processed by compute resources such as clusters. The compute plane is in your own cloud account and network. Reference: Databricks architecture overview, Security and Trust Center


NEW QUESTION # 304
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