Databricks Databricks Certified Data Engineer Associate Exam Databricks-Certified-Data-Engineer-Associate Exam Dumps: Updated Questions & Answers (August 2026)

The DELETE command in Delta Lake allows you to remove data that matches a predicate from a Delta table. This command will delete all the rows where the value in the column age is greater than 25 from the existing Delta table my_table and save the updated table. The other options are either incorrect or do not achieve the desired result. Option A will only select the rows that match the predicate, but not delete them. Option B will update the rows that match the predicate, but not delete them. Option D will update the rows that do not match the predicate, but not delete them. Option E will delete the rows that do not match the predicate, which is the opposite of what we want. References: Table deletes, updates, and merges — Delta Lake Documentation

A fan-in dependency pattern makes the dashboard task depend on all three parallel tasks. After the entry notebook completes, Databricks can execute the cleansing, feature-engineering, and model-update tasks concurrently because none depends on another. The dashboard refresh remains blocked until every configured upstream dependency has completed successfully. In Lakeflow Jobs, task dependencies form a directed acyclic graph, and Databricks runs as many eligible upstream tasks in parallel as possible. The default All succeeded Run if condition ensures that the downstream dashboard task runs only when all three dependencies succeed. Sequentially chaining the parallel tasks would unnecessarily increase the total execution time. Depending on only one task could refresh the dashboard before the other outputs are ready. Therefore, option B correctly implements the required fan-out and fan-in DAG.

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he approach that can be used to identify the owner of new_table is to review the Owner field in the table’s page in Data Explorer. Data Explorer is a web-based interface that allows users to browse, create, and manage data objects such as tables, views, and functions in Databricks1. The table’s page in Data Explorer provides various information about the table, such as its schema, partitions, statistics, history, and permissions2. The Owner field shows the name and email address of the user who created or owns the table3. The data engineer can use this information to contact the table owner and request for permission to access the table.

The other options are not correct or reliable for identifying the owner of new_table. Reviewing the Permissions tab in the table’s page in Data Explorer can show the users and groups who have access to the table, but not necessarily the owner4. Reviewing the Owner field in the table’s page in the cloud storage solution can be misleading, as the owner of the data files may not be the same as the owner of the table5. There is a way to identify the owner of the table, as explained above, so option E is false.

1: Data Explorer | Databricks on AWS

2: Table details | Databricks on AWS

3: Set owner when creating a view in databricks sql - Databricks - 9978

4: Table access control | Databricks on AWS

5: External tables | Databricks on AWS

 Databricks SQL allows users to schedule queries to run automatically at a specified frequency and time zone. This can help users to keep their dashboards or alerts updated with the latest data. To schedule a query, users need to do the following steps:

In the Query Editor, click Schedule > Add schedule to open a menu with schedule settings.

Choose when to run the query. Use the dropdown pickers to specify the frequency, period, starting time, and time zone. Optionally, select the Show cron syntax checkbox to edit the schedule in Quartz Cron Syntax.

Choose More options to show optional settings. Users can also choose a name for the schedule, and a SQL warehouse to power the query.

Click Create. The query will run automatically according to the schedule.

The other options are incorrect because they do not refer to the correct location or frequency to schedule the query. The query’s page in Databricks SQL is the place where users can edit, run, or schedule the query. The SQL endpoint’s page in Databricks SQL is the place where users can manage the SQL warehouses and SQL endpoints. The Jobs UI is the place where users can create, run, or schedule jobs that execute notebooks, JARs, or Python scripts. References: Schedule a query, What are Databricks SQL alerts?, Jobs.

Option D is directly associated with the failure because toPandas() collects the complete Spark DataFrame into a single pandas DataFrame in the driver process. With 50 million wide rows, the collected result can exceed the memory assigned to the Spark driver and produce a driver-side OutOfMemoryError. Databricks and PySpark documentation warn that toPandas() should be used only when the resulting dataset is expected to be small because all records are loaded into driver memory. spark.executor.memory controls the executor processes that perform distributed transformations, not the final local pandas object. spark.sql.shuffle.partitions controls the number of shuffle partitions, while spark.memory.fraction controls Spark execution and storage memory allocation. Therefore, the relevant configuration among the available choices is spark.driver.memory, making option D correct.

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Reading a Unity Catalog table requires privileges at all relevant levels of its three-part namespace. The group needs USE CATALOG on corp_marketing, USE SCHEMA on campaigns, and SELECT on email_stats. These privileges allow users to resolve the catalog and schema and read the table without granting permission to create or modify objects. Option A omits USE CATALOG, so table access is incomplete. Option B unnecessarily grants CREATE SCHEMA, violating the requirement that the group must not create objects. Option D omits USE CATALOG and grants MODIFY, which would permit data changes. Unity Catalog follows a hierarchical permissions model, but possession of SELECT alone is insufficient without the required usage privileges on the parent containers. Therefore, the least-privilege sequence in option C is correct.

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For interactive notebook development, Databricks distinguishes between all-purpose compute and job compute . All-purpose compute is designed for analysis, notebook development, and interactive workloads , while job compute is intended for automated scheduled or triggered jobs. Because the engineer is actively developing a proof of concept in a notebook and wants real-time feedback, an all-purpose cluster is the right compute type. Adding autoscaling makes it better suited to the observed usage spikes, because the cluster can expand when notebook execution demands more resources and scale down afterward, reducing waste compared with a large fixed-size cluster. That makes option B the best answer. Option A keeps the interactive development model, but the fixed large size is less efficient. Options C and D use job clusters, which are better aligned with production jobs rather than iterative notebook-based development. Databricks documentation consistently positions all-purpose compute for collaborative and interactive development, and autoscaling is a standard mechanism to handle variable workloads more efficiently.

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