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Databricks Certified Data Engineer Professional Sample Questions:

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

2. 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) 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.
C) 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.
D) 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.

3. An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable:

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A) Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
B) Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
C) Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
D) Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
E) Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.

4. A company stores account transactions in a Delta Lake table. The company needs to apply frequent account-level correlations (e.g., UPDATE statements) but wants to avoid rewriting entire Parquet files for each change to reduce file churn and improve write performance. Which Delta Lake feature should they enable?

A) Enable deletion vectors on the Delta table
B) Partition the Delta table by account_id
C) Enable change data feed on the Delta table
D) Enable automatic file compaction on writes

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

Solutions: