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Databricks Certified Data Engineer Professional Sample Questions:

1. Which of the following is true of Delta Lake and the Lakehouse?

A) Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
B) Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
C) Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
D) Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
E) Z-order can only be applied to numeric values stored in Delta Lake tables

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

3. A view is registered with the following code:

Both users and orders are Delta Lake tables.
Which statement describes the results of querying recent_orders?

A) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
B) All logic will execute when the view is defined and store the result of joining tables to the DBFS; this stored data will be returned when the view is queried.
C) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
D) Results will be computed and cached when the view is defined; these cached results will incrementally update as new records are inserted into source tables.

4. A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

A) Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
B) Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
C) Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
D) Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
E) Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.

5. The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A)

B)

C) preds.write.format("delta").save("/preds/churn_preds")
D)

E) preds.write.mode("append").saveAsTable("churn_preds")

Solutions: