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

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineering team is implementing an append-only data pipeline using Delta Lake, and wants to ensure that data is never modified or deleted once written. Which Delta Lake feature should the data engineer enable to prevent modifications to existing data?

A) Delta VACUUM
B) Delta APPEND_ONLY
C) Delta Time Travel
D) Delta OPTIMIZE

2. A data engineering team is collaborating on a Databricks project where each team member needs to develop and test code independently before merging changes into the main branch.
They want to avoid accidental overwrites or branch switching issues while ensuring that all work is version- controlled and can be integrated into their CI/CD pipeline.
How should the data engineer achieve collaboration?

A) Each team member creates their own Databricks Git folder, mapped to the same remote Git repository, and works in their own development branch within their personal folder.
B) Team members edit notebooks directly in the workspace's shared folder and periodically copy changes into a Git folder for version control.
C) Team members use the Databricks CLI to clone the Git repository and perform Git operations from a cluster's web terminal.
D) All team members work in the same Databricks Git folder and perform Git operations (pull, push, commit, branch switching) directly in that shared folder.

3. To identify the top users consuming compute resources, a data engineering team needs to monitor usage within their Databricks workspace for better resource utilization and cost control.
The team decided to use Databricks system tables, available under the System catalog in Unity Catalog, to gain detailed visibility into workspace activity. Which SQL query should the team run from the System catalog to achieve this?

A) SELECT sku_name,
usage_metadata.run_name AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
B) SELECT identity_metadata.run_as AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email
ORDER BY total_dbus DESC
LIMIT 10
C) SELECT sku_name,
identity_metadata.created_by AS user_email,
COUNT(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
D) SELECT sku_name,
identity_metadata.created_by AS user_email,
SUM(usage_quantity * usage_unit) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10

4. A data engineer created a daily batch ingestion pipeline using a cluster with the latest DBR version to store banking transaction data, and persisted it in a MANAGED DELTA table called prod.gold.all_banking_transactions_daily. The data engineer is constantly receiving complaints from business users who query this table ad hoc through a SQL Serverless Warehouse about poor query performance. Upon analysis, the data engineer identified that these users frequently use high- cardinality columns as filters. The engineer now seeks to implement a data layout optimization technique that is incremental, easy to maintain, and can evolve over time. Which command should the data engineer implement?

A) Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.
B) Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.
C) Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.
D) Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.

5. A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.
Original query:

Proposed query:

Which step must also be completed to put the proposed query into production?

A) Register the data in the "/item_agg" directory to the Hive metastore
B) Run REFRESH TABLE delta, /item_agg'
C) Remove .option (mergeSchema', true') from the streaming write
D) Increase the shuffle partitions to account for additional aggregates
E) Specify a new checkpointlocation

Solutions: