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

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is running a groupBy aggregation on a massive user activity log grouped by user_id. A few users have millions of records, causing task skew and long runtimes. Which technique will fix the skew in this aggregation?

A) Filter out the skewed users before the aggregation.
B) Use salting by adding a random prefix to skewed keys before aggregation, then aggregate again after removing the prefix.
C) Increase the Spark driver memory and retry.
D) Use reduceByKey instead of groupBy to avoid shuffles.

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

3. A platform team lead is responsible for automating the individual teams attribution towards SQL Warehouse usage. The requirement is to identify the SQL warehouse usage at the individual user's level and generate a daily report to be shared with an executive team that includes leaders from all business units. How should the platform lead generate an automated report that can be shared daily?

A) Restrict users from running any SQL query unless they provide all the query details so that the attribution can be calculated and shared with the executive team.
B) Use the system tables to capture the audit and billing usage data and create a dashboard with daily refresh schedules and shared with the executive team.
C) Let the users run the SQL query and then directly report the usage to the executives. The ownership of the SQL warehouse usage will be with the individual teams.
D) Use the system tables to capture the audit and billing usage data and share the queries with the executive team. This enables the executives to execute the query and see the latest results any time.

4. The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A) Schedule a job to execute the pipeline once an hour on a dedicated interactive cluster.
B) Configure a job that executes every time new data lands in a given directory.
C) Schedule a Structured Streaming job with a trigger interval of 60 minutes.
D) Schedule a job to execute the pipeline once an hour on a new job cluster.

5. To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A) Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
B) Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
C) Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.
D) Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
E) Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.

Solutions: