Get real Databricks-Certified-Professional-Data-Engineer exam questions for better preparation

A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

• A. Set the pipelines, reset, allowed property to false on bpm_stats
• B. Set the pipelines, reset, allowed property to false on raw_iot
• C. Set the SkipChangeCommits flag to true raw_lot
• D. Set the skipChangeCommits flag to true on bpm_stats

A data engineer inherits a Delta table with historical partitions by country that are badly skewed. Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve.
Which two actions should the data engineer take? (Choose 2)

• A. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.
• B. Periodically run OPTIMIZE table_name.
• C. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.
• D. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
• E. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.

Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

• A. * Total VMs: 4
  * 100 GB per Executor
  * 40 Cores/Executor
• B. * Total VMs: 8
  * 50 GB per Executor
  * 20 Cores / Executor
• C. * Total VMs:2
  * 200 GB per Executor
  * 80 Cores / Executor
• D. * Total VMs; 1
  * 400 GB per Executor
  * 160 Cores / Executor

The data science team has requested assistance in accelerating queries on free form text from user reviews.
The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer s suggestion is correct?

• A. Text data cannot be stored with Delta Lake.
• B. Delta Lake statistics are only collected on the first 4 columns in a table.
• C. Delta Lake statistics are not optimized for free text fields with high cardinality.
• D. The Delta log creates a term matrix for free text fields to support selective filtering.
• E. ZORDER ON review will need to be run to see performance gains.

A junior developer complains that the code in their notebook isn ' t producing the correct results in the development environment. A shared screenshot reveals that while they ' re using a notebook versioned with Databricks Repos, they ' re using a personal branch that contains old logic. The desired branch named dev-
2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

• A. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
• B. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-
  2.3.9
• C. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
• D. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
• E. Merge all changes back to the main branch in the remote Git repository and clone the repo again