Free Databricks Databricks-Certified-Data-Engineer-Associate Practice Exam with Questions & Answers | Set: 5

According to the medallion lakehouse architecture, gold tables are the final layer of data that powers analytics, machine learning, and production applications. They are often highly refined and aggregated, containing data that has been transformed into knowledge, rather than just information. Silver tables, on the other hand, are the intermediate layer of data that represents a validated, enriched version of the raw data from the bronze layer. They provide an enterprise view of all its key business entities, concepts and transactions, but they may not have all the aggregations and calculations that are required for specific use cases. Therefore, gold tables are more likely to contain aggregations than silver tables. References:

What is the medallion lakehouse architecture?

What is a Medallion Architecture?

A webhook alert destination is a way to send notifications to external applications or services via HTTP requests. A data engineer can use a webhook alert destination to notify their entire team via a messaging webhook, such as Slack or Microsoft Teams, whenever the number of NULL values in the input data reaches 100. To set up a webhook alert destination, the data engineer needs to do the following steps:

In the Databricks SQL workspace, navigate to the Settings gear icon and select SQL Admin Console.

Click Alert Destinations and click Add New Alert Destination.

Select Webhook and enter the webhook URL and the optional custom template for the notification message.

Click Create to save the webhook alert destination.

In the Databricks SQL editor, create or open the query that returns the number of input records containing unexpected NULL values.

Click the Create Alert icon above the editor window and configure the alert criteria, such as the value column, the condition, and the threshold.

In the Notification section, select the webhook alert destination that was created earlier and click Create Alert. References: What are Databricks SQL alerts?, Monitor alerts, Monitoring Your Business with Alerts, Using Automation Runbook Webhooks To Alert on Databricks Status Updates.

The engineer should inspect the job’s maximum concurrent-runs configuration and its queueing setting. max_concurrent_runs controls how many active runs of the same job can execute simultaneously. When that limit has already been reached, Databricks can skip the newly triggered run if queueing is disabled. When queueing is enabled, the additional run waits until capacity becomes available instead of being skipped. Spot-instance availability might delay or prevent compute startup, but it would not normally produce a job-level concurrency message. SQL warehouse permissions affect SQL task execution after a run begins, while timeout_seconds determines how long a task may run before timing out. Therefore, option C directly addresses the reported run state. This candidate was also compared against the existing Word question bank.

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The engineer should use Repair run . A repair reruns unsuccessful tasks as part of the original job run while preserving the successful status of tasks that do not need to be executed again. In this scenario, Tasks 1 and 2 remain successful, Task 3 is rerun after its problem is corrected, and Task 4 can execute after its dependency succeeds. Run now starts a separate job run and normally executes the workflow from its beginning. Cloning a task creates another task definition and does not resume the failed run. Reset job is not the appropriate Lakeflow Jobs recovery operation. Databricks also notes that repaired tasks should use idempotent write logic where possible because a failed task might have produced partial output before failing. Therefore, option C correctly resumes the workflow without unnecessarily repeating the successful upstream tasks.

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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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Option D retains only orders whose customer identifier matches a row in customers_df. Because customers_df contains only active customers, an inner join acts as a membership filter: orders belonging to active customers match and remain, while orders belonging to inactive customers have no corresponding row and are removed. A left join with orders_df on the left would preserve every order, including orders from inactive customers, unless an additional NULL filter were applied. A left join with customers_df on the left changes the driving dataset and can exclude the required order structure or produce different cardinality. A cross join generates the Cartesian product of both DataFrames and is unrelated to key-based filtering. Therefore, the inner join in option D precisely implements the stated active-customer filtering requirement.

Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as cloning, committing, pushing, pulling, and branch management. Databricks Notebooks versioning is a legacy feature that allows users to link notebooks to GitHub repositories and perform basic Git operations. However, Databricks Notebooks versioning does not support the use of multiple branches for development work, which is an advantage of using Databricks Repos. With Databricks Repos, users can create and manage branches for different features, experiments, or bug fixes, and merge, rebase, or resolve conflicts between them. Databricks recommends using a separate branch for each notebook and following data science and engineering code development best practices using Git for version control, collaboration, and CI/CD. References: Git integration with Databricks Repos - Azure Databricks | Microsoft Learn, Git version control for notebooks (legacy) | Databricks on AWS, Databricks Repos Is Now Generally Available - New ‘Files’ Feature in …, Databricks Repos - What it is and how we can use it | Adatis.

A row-filter function must return a Boolean value indicating whether each row is visible to the current user. The function in option A returns true for members of the account-level all_regions group, allowing them to see every row. For other users, it returns the result of region = ' EU ' , limiting visibility to European records. is_account_group_member checks direct or indirect membership in an account-level group. The ALTER TABLE ... SET ROW FILTER ... ON (region) statement then associates the function with the table and passes the region column to it for every query. A column mask transforms displayed column values rather than removing rows. The object is a table, not a view, and USING COLUMNS is not the correct row-filter attachment syntax. Therefore, option A is correct.

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