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Databricks Certified Data Engineer Professional Sample Questions:

1. A developer has successfully configured their credentials for Databricks Repos and cloned a remote Git repository. They do not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace. Which approach allows this user to share their code updates without the risk of overwriting the work of their teammates?

A) Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.
B) Use Repos to merge all differences and make a pull request back to the remote repository.
C) Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository
D) Use repos to merge all difference and make a pull request back to the remote repository.
E) Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.

2. A data engineer is creating a data ingestion pipeline to understand where customers are taking their rented bicycles during use. The engineer noticed that, over time, data being transmitted from the bicycle sensors fail to include key details like latitude and longitude. Downstream analysts need both the clean records and the quarantined records available for separate processing.
The data engineer already has this code:
import dlt
from pyspark.sql.functions import expr
rules = {
"valid_lat": "(lat IS NOT NULL)",
"valid_long": "(long IS NOT NULL)"
}
quarantine_rules = "NOT({})".format(" AND ".join(rules.values()))
@dlt.view
def raw_trips_data():
return spark.readStream.table("ride_and_go.telemetry.trips")
How should the data engineer meet the requirements to capture good and bad data?

A) @dlt.table
@dlt.expect_all_or_drop(rules)
def trips_data_quarantine():
return spark.readStream.table("raw_trips_data")
B) @dlt.view
@dlt.expect_or_drop("lat_long_present", "(lat IS NOT NULL AND long IS NOT NULL)") def trips_data_quarantine():
return spark.readStream.table("ride_and_go.telemetry.trips")
C) @dlt.table(name="trips_data_quarantine")
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.filter(expr(quarantine_rules))
)
D) @dlt.table(partition_cols=["is_quarantined", ])
@dlt.expect_all(rules)
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.withColumn("is_quarantined", expr(quarantine_rules))
)

3. A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table
Which of the following describes how results are generated each time the dashboard is updated?

A) The total count of records is calculated from the Delta transaction logs
B) The total count of records is calculated from the parquet file metadata
C) The total count of rows will be returned from cached results unless REFRESH is run
D) The total count of rows is calculated by scanning all data files
E) The total count of records is calculated from the Hive metastore

4. A task orchestrator has been configured to run two hourly tasks. First, an outside system writes Parquet data to a directory mounted at /mnt/raw_orders/. After this data is written, a Databricks job containing the following code is executed:

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order, and that the time field indicates when the record was queued in the source system.
If the upstream system is known to occasionally enqueue duplicate entries for a single order hours apart, which statement is correct?

A) The orders table will not contain duplicates, but records arriving more than 2 hours late will be ignored and missing from the table.
B) The orders table will contain only the most recent 2 hours of records and no duplicates will be present.
C) All records will be held in the state store for 2 hours before being deduplicated and committed to the orders table.
D) Duplicate records enqueued more than 2 hours apart may be retained and the orders table may contain duplicate records with the same customer_id and order_id.
E) Duplicate records arriving more than 2 hours apart will be dropped, but duplicates that arrive in the same batch may both be written to the orders table.

5. A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A) The email and ltv columns will be returned with the values in user itv.
B) Three columns will be returned, but one column will be named "redacted" and contain only null values.
C) Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.
D) Only the email and itv columns will be returned; the email column will contain all null values.
E) The email, age. and ltv columns will be returned with the values in user ltv.

Solutions: