Databricks-Certified-Professional-Data-Engineer Exam Dumps - Databricks Certification Questions and Answers

The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string “REDACTED” if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string “REDACTED” in each row. Verified References: [Databricks Certified Data Engineer Professional], under “Lakehouse” section; Databricks Documentation, under “CASE expression” section.

Job clusters are optimized for automated production workloads. They start when a job is triggered and terminate automatically once the task completes. This ensures cost control while maintaining performance and reliability for batch ETL. Autoscaling allows Databricks to add or remove workers dynamically based on workload size, ensuring large data volumes are processed efficiently.

All-purpose clusters are intended for development or ad-hoc workloads, not scheduled ETL.

Reference Source: Databricks Compute and Job Cluster Configuration Documentation – “Autoscaling and Job Clusters.”

Exact extract: “Job clusters are created for a job run and terminate when the job completes.”

This is the correct answer because it characterizes the general programming model used by Spark Structured Streaming, which is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model, where users can express their streaming computation using the same Dataset/DataFrame API as they would use for static data. The Spark SQL engine will take care of running the streaming query incrementally and continuously and updating the final result as streaming data continues to arrive. Verified References: [Databricks Certified Data Engineer Professional], under “Structured Streaming” section;  Databricks Documentation , under “Overview” section.

To create an external or unmanaged Delta Lake table, you need to use the EXTERNAL keyword in the CREATE TABLE statement. This indicates that the table is not managed by the catalog and the data files are not deleted when the table is dropped. You also need to provide a LOCATION clause to specify the path where the data files are stored. For example:

CREATE EXTERNAL TABLE events ( date DATE, eventId STRING, eventType STRING, data STRING) USING DELTA LOCATION ‘/mnt/delta/events’;

This creates an external Delta Lake table named events that references the data files in the ‘/mnt/delta/events’ path. If you drop this table, the data files will remain intact and you can recreate the table with the same statement.

This is the correct answer because it is the REST API call that can be used to review the notebooks configured to run as tasks in a multi-task job. The REST API is an interface that allows programmatically interacting with Databricks resources, such as clusters, jobs, notebooks, or tables. The REST API uses HTTP methods, such as GET, POST, PUT, or DELETE, to perform operations on these resources. The /jobs/get endpoint is a GET method that returns information about a job given its job ID. The information includes the job settings, such as the name, schedule, timeout, retries, email notifications, and tasks. The tasks are the units of work that a job executes. A task can be a notebook task, which runs a notebook with specified parameters; a jar task, which runs a JAR uploaded to DBFS with specified main class and arguments; or a python task, which runs a Python file uploaded to DBFS with specified parameters. A multi-task job is a job that has more than one task configured to run in a specific order or in parallel. By using the /jobs/get endpoint, one can review the notebooks configured to run as tasks in a multi-task job. Verified References: [Databricks Certified Data Engineer Professional], under “Databricks Jobs” section; Databricks Documentation, under “Get” section; Databricks Documentation, under “JobSettings” section.

The system.billing.usage table in the Unity Catalog System schema provides detailed usage metrics for each workload in the workspace. The field identity_metadata.run_as identifies the user or service principal under which the job or query executed. Summing usage_quantity provides total DBU (Databricks Unit) consumption per user. According to Databricks documentation, this table is the authoritative source for monitoring workspace cost drivers, showing compute SKU, user, and DBU consumption over time. Grouping by identity_metadata.run_as and summing usage_quantity produces the correct aggregation to determine top users. Other queries use non-existent or incorrect fields (created_by, run_name, or multiplied usage quantities), which do not reflect actual billing metrics.

https://docs.databricks.com/en/lakehouse/data-objects.html

This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string “redacted” will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks. Verified References: [Databricks Certified Data Engineer Professional], under “Security and Governance” section; Databricks Documentation, under “Secrets” section.