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Databricks Certified Data Engineer Professional Sample Questions:

1. A member of the data engineering team has submitted a short notebook that they wish to schedule as part of a larger data pipeline. Assume that the commands provided below produce the logically correct results when run as presented.

Which command should be removed from the notebook before scheduling it as a job?

A) Cmd 2
B) Cmd 6
C) Cmd 4
D) Cmd 5
E) Cmd 3

2. A data engineer is implementing liquid clustering on a Delta Lale table and needs to understand how it affects data management operations. The table will be updated frequently with new data.
The table is an external table and not managed by Unity Catalog. How does liquid clustering in Delta Lake handle new data that is inserted after the initial table creation?

A) New data is written to a staging area and clustered during scheduled maintenance.
B) New data is rejected if it doesn't match the clustering pattern.
C) New data is automatically clustered during write operations.
D) New data remains unclustered until the next OPTIMIZE operation.

3. A Data Engineer is building a fraud detection pipeline that calls out to Open AI, via a Python library, and needs to include an access token when using the API. Which Databricks CLI command should the Data Engineer use to create the secret?

A) databricks secrets put-secret SCOPE KEY; dbutils.secrets.get (SCOPE, KEY)
B) databricks secrets put-secret KEY SCOPE; dbutils.secrets.get (KEY, SCOPE)
C) databricks tokens put-token SCOPE KEY; dbutils.tokens.get (SCOPE, KEY)
D) databricks tokens put-token KEY SCOPE; dbutils.secrets.get (KEY, SCOPE)

4. A security analytics pipeline must enrich billions of raw connection logs with geolocation data.
The join hinges on finding which IPv4 range each event's address falls into.
Table 1: network_events ( 5 billion rows)
event_id ip_int
42 3232235777
Table 2: ip_ranges ( 2 million rows)
start_ip_int end_ip_int country
3232235520 3232236031 US
The query is currently very slow:
SELECT n.event_id, n.ip_int, r.country
FROM network_events n
JOIN ip_ranges r
ON n.ip_int BETWEEN r.start_ip_int AND r.end_ip_int;
Which change will most dramatically accelerate the query while preserving its logic?

A) Increase spark.sql.shuffle.partitions from 200 to 10000.
B) Add a range-join hint /*+ RANGE_JOIN(r, 65536) */.
C) Force a sort-merge join with /*+ MERGE(r) */.
D) Add a broadcast hint: /*+ BROADCAST(r) */ for ip_ranges.

5. An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. The user_id field represents a unique key for the data, which has the following schema:
user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT New records are all ingested into a table named account_history which maintains a full record of all data in the same schema as the source. The next table in the system is named account_current and is implemented as a Type 1 table representing the most recent value for each unique user_id.
Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the described account_current table as part of each hourly batch job?

A) Use Delta Lake version history to get the difference between the latest version of account history and one version prior, then write these records to account current.
B) Use Auto Loader to subscribe to new files in the account history directory; configure a Structured Streaminq trigger once job to batch update newly detected files into the account current table.
C) Filter records in account history using the last updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the most recent value for each username.
D) Overwrite the account current table with each batch using the results of a query against the account history table grouping by user id and filtering for the max value of last updated.
E) Filter records in account history using the last updated field and the most recent hour processed, as well as the max last iogin by user id write a merge statement to update or insert the most recent value for each user id.

Solutions: