Online Access Free Databricks-Certified-Professional-Data-Engineer Practice Test

The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

• A.Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
• B.Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.
• C.Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
• D.Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.

The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

• A.Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
• B.Define a view against the products_per_order table and define the dashboard against this view.
• C.Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.
• D.Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.

A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays:
Which limitation will the team face while diagnosing this problem?

• A.New fields not be computed for historic records.
• B.Updating the table schema will invalidate the Delta transaction log metadata.
• C.Spark cannot capture the topic partition fields from the kafka source.
• D.Updating the table schema requires a default value provided for each file added.

Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets.
Which statement describes a limitation of Databricks Secrets?

• A.Secrets are stored in an administrators-only table within the Hive Metastore; database administrators have permission to query this table by default.
• B.Iterating through a stored secret and printing each character will display secret contents in plain text.
• C.The Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials.
• D.Account administrators can see all secrets in plain text by logging on to the Databricks Accounts console.
• E.Because the SHA256 hash is used to obfuscate stored secrets, reversing this hash will display the value in plain text.

A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

• A.Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
• B.Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
• C.Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
• D.Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
• E.Tasks B and C will be skipped; task A will not commit any changes because of stage failure.