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

Databricks Certified Data Engineer Professional Sample Questions:

1. The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

A) Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.
B) No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
C) Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
D) No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
E) No; the change data feed only tracks inserts and updates not deleted records.

2. A data engineer manages a production Lakeflow Declarative Pipeline that processes customer transaction data. The pipeline includes several data quality expectations such as transaction_amount > 0 and customer_id IS NOT NULL. These expectations are defined using the EXPECT clause in SQL.
The engineer aims to monitor the pipeline's data quality by analyzing the number of records that passed or failed each expectation during the latest pipeline update. The Lakeflow Declarative Pipelines event logs are stored in a Delta table named event_log_table.
For the most recent pipeline update, determine a programmatically appropriate approach to extract information like the name of each expectation, associated dataset, count of records that passed the expectation, and count of records that failed the expectation.
Which method retrieves the desired data quality metrics from the Lakeflow Declarative Pipelines event log?

A) Access the event_log_table, filter for events where event_type = 'expectation_result', and extract the expectation metrics from the details field.
B) Access the event_log_table, filter for events where event_type = 'flow_progress', and parse details.flow_progress.data_quality.expectations field to extract the required metrics.
C) Query the event_log_table for events with event_type = 'data_quality' and directly select the passed_records and failed_records fields.
D) Use the Lakeflow Declarative Pipelines UI to navigate to the specific pipeline, select the dataset, and view the Data Quality tab to manually retrieve the expectation metrics.

3. A query is taking too long to run. After investigating the Spark UI, the data engineer discovered a significant amount of disk spill. The compute instance being used has a core-to-memory ratio of
1:2. What are the two steps the data engineer should take to minimize spillage? (Choose two.)

A) Reduce spark.sql.files.maxPartitionBytes.
B) Choose a compute instance with a higher core-to-memory ratio.
C) Choose a compute instance with more network bandwidth.
D) Choose a compute instance with more disk space.
E) Increase spark.sql.files.maxPartitionBytes.

4. When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

A) Network I/O never spikes
B) Total Disk Space remains constant
C) Bytes Received never exceeds 80 million bytes per second
D) The five Minute Load Average remains consistent/flat
E) CPU Utilization is around 75%

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) 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.
B) 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.
C) 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.
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) 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.

Solutions: