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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. Which statement regarding stream-static joins and static Delta tables is correct?

A) Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
B) Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
C) Stream-static joins cannot use static Delta tables because of consistency issues.
D) The checkpoint directory will be used to track state information for the unique keys present in the join.
E) The checkpoint directory will be used to track updates to the static Delta table.

2. A data engineer is building a Lakeflow Declarative Pipelines pipeline to process healthcare claims data. A metadata JSON file defines data quality rules for multiple tables, including:
{
"claims": [
{"name": "valid_patient_id", "constraint": "patient_id IS NOT NULL"},
{"name": "non_negative_amount", "constraint": "claim_amount >= 0"}
]
}
The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.
How should the data engineer achieve this?

A) Load the JSON metadata, loop through its entries, and apply expectations using dlt.expect_all.
B) Use a SQL CONSTRAINT block referencing the JSON file path.
C) Reference each expectation with @dlt.expect decorators in the table declaration.
D) Invoke an external API to validate records against the metadata rules.

3. Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

A) - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor
B) - Total VMs: 16
- 25 GB per Executor
- 10 Cores / Executor
C) - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor
D) - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor
E) - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor

4. The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic. What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A) Can edit
B) Can Read
C) Can run
D) Can manage

5. A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON.
The code snippet is:
df = (spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("badRecordsPath", "/tmp/somewhere/badRecordsPath")
.schema("a int, b int")
.load("/Volumes/catalog/schema/raw_data/"))
What is the cause of the missing data?

A) The badRecordsPath location is accumulating many small files.
B) The engineer forgot to set the option "cloudFiles.quarantineMode" = "rescue".
C) At some point, the upstream data provider switched everything to multi-line JSON.
D) The source data is valid JSON but does not conform to the defined schema in some way.

Solutions: