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Databricks Certified Data Engineer Professional Sample Questions:

1. A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.
When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?

A) The five Minute Load Average remains consistent/flat
B) Bytes Received never exceeds 80 million bytes per second
C) Total Disk Space remains constant
D) Overall cluster CPU utilization is around 25%
E) Network I/O never spikes

2. A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities. Which type of Databricks cluster should the data engineer configure?

A) A lightweight single-node cluster with low worker node count to reduce costs.
B) A high-concurrency cluster designed for interactive SQL workloads.
C) A job cluster configured to autoscale across multiple workers during the pipeline run.
D) An all-purpose cluster always kept running to ensure low-latency job startup times.

3. A data engineer is creating a daily reporting job. There are two reporting notebooks--one for weekdays and one for weekends. An "if/else condition" task is configured as
{{job.start_time.is_weekday}} == true to route the job to either the weekday or weekend notebook tasks. The same job would be used across multiple time zones. Which action should a senior data engineer take upon reviewing the job to merge or reject the pull request?

A) Merge, as the job configuration looks good.
B) Reject, as they should use {{job.trigger_time.is_weekday}} instead.
C) Reject, as the {{job.start_time.is_weekday}} is not a valid value reference.
D) Reject, as the {{job.start_time.is_weekday}} is for the UTC timezone.

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) Add a broadcast hint: /*+ BROADCAST(r) */ for ip_ranges.
D) Force a sort-merge join with /*+ MERGE(r) */.

5. A data engineering team is collaborating on a Databricks project where each team member needs to develop and test code independently before merging changes into the main branch.
They want to avoid accidental overwrites or branch switching issues while ensuring that all work is version- controlled and can be integrated into their CI/CD pipeline.
How should the data engineer achieve collaboration?

A) Team members use the Databricks CLI to clone the Git repository and perform Git operations from a cluster's web terminal.
B) All team members work in the same Databricks Git folder and perform Git operations (pull, push, commit, branch switching) directly in that shared folder.
C) Team members edit notebooks directly in the workspace's shared folder and periodically copy changes into a Git folder for version control.
D) Each team member creates their own Databricks Git folder, mapped to the same remote Git repository, and works in their own development branch within their personal folder.

Solutions: