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Databricks Databricks-Certified-Professional-Data-Engineer Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Professional Data Engineer Exam
Exam Number:	Databricks-Certified-Professional-Data-Engineer
Real Exam Qty:	60
Exam Duration:	90 minutes
Exam Format:	Multiple Choice, Multiple Select
Exam Price:	$200 USD
Available Languages:	English
Certificate Validity Period:	2 years
Passing Score:	70%
Related Certifications:	Databricks Certified Associate Developer Databricks Certified Data Analyst Associate
Sample Questions:	Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions
Exam Way:	Online proctored exam (Pearson VUE)
Pre Condition:	Recommended: 6+ months of hands-on experience with Databricks and data engineering concepts; familiarity with Python or Scala and SQL is strongly recommended
Official Syllabus URL:	https://www.databricks.com/learn/certification/professional-data-engineer

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Databricks Certified Professional Data Engineer exam consists of a set of performance-based tasks that test the candidate's ability to apply their knowledge and skills to real-world scenarios. Databricks-Certified-Professional-Data-Engineer exam is conducted online and can be taken from anywhere in the world. Databricks-Certified-Professional-Data-Engineer Exam is timed and candidates have to complete the tasks within the given time frame. Databricks-Certified-Professional-Data-Engineer exam is designed in such a way that it assesses the candidate's ability to work with Databricks Unified Analytics Platform and solve complex data engineering problems.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
A data engineer wants to automate job monitoring and recovery in Databricks using the Jobs API. They need to list all jobs, identify a failed job, and rerun it.
Which sequence of API actions should the data engineer perform?

A. Use the jobs/list endpoint to list jobs, check job run statuses with jobs/runs/list, and rerun a failed job using jobs/run-now.
B. Use the jobs/get endpoint to retrieve job details, then use jobs/update to rerun failed jobs.
C. Use the jobs/cancel endpoint to remove failed jobs, then recreate them with jobs/create and run the new ones.
D. Use the jobs/list endpoint to list jobs, then use the jobs/create endpoint to create a new job, and run the new job using jobs/run-now.

Answer: A

Explanation:
The Databricks Jobs REST API provides several endpoints for automation. The correct monitoring and rerun flow uses three specific calls:
* GET /api/2.1/jobs/list - Lists all available jobs within the workspace.
* GET /api/2.1/jobs/runs/list - Returns all runs for a specific job, including their current state (e.g., TERMINATED: FAILED).
* POST /api/2.1/jobs/run-now - Immediately triggers a rerun of the specified job.
This sequence aligns with Databricks' prescribed automation model for job observability and recovery. Using jobs/update modifies metadata but does not rerun jobs, and jobs/create is only used for creating new jobs, not rerunning failed ones. Cancelling and recreating jobs introduces unnecessary duplication. Therefore, option A is the correct automated recovery workflow.

NEW QUESTION # 74
A data architect is designing a data model that works for both video-based machine learning work-loads and
highly audited batch ETL/ELT workloads.
Which of the following describes how using a data lakehouse can help the data architect meet the needs of
both workloads?

A. A data lakehouse stores unstructured data and is ACID-compliant
B. A data lakehouse requires very little data modeling
C. A data lakehouse combines compute and storage for simple governance
D. A data lakehouse fully exists in the cloud
E. A data lakehouse provides autoscaling for compute clusters

Answer: A

NEW QUESTION # 75
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) */.

Answer: B

Explanation:
Comprehensive and Detailed Explanation from Databricks Documentation:
The query joins billions of rows (network_events) with millions of rows (ip_ranges) using a range predicate (BETWEEN). Unlike equality joins (=), range joins are not efficiently handled by broadcast or sort-merge joins because:
* Broadcast Join (D): Effective for small tables but only for equality joins. Since this query uses a range condition, broadcast will not reduce the complexity of scanning billions of records across non-equality conditions.
* Sort-Merge Join (C): Works for ordered joins but is inefficient on range conditions. Sorting billions of records adds excessive overhead and will not resolve the bottleneck.
* Increasing Shuffle Partitions (A): Only spreads out shuffle work but does not address the fundamental inefficiency of range-based lookups at scale.
Range Joins in Spark (RANGE_JOIN hint):
Databricks provides range join optimizations specifically for conditions such as BETWEEN. By applying a RANGE_JOIN hint, Spark can build optimized data structures (such as interval indexes or partition pruning strategies) that map billions of input rows to ranges much faster. This avoids brute-force scans and unnecessary shuffle costs.
Thus, Option B is the correct solution because:
* It leverages range-join optimization, which is purpose-built for queries joining massive event logs to smaller lookup tables with IP ranges.
* This ensures Spark can evaluate billions of rows against millions of ranges with optimized matching logic, drastically improving query performance while preserving correctness.

NEW QUESTION # 76
A data engineer needs to ingest data from an Apache Iceberg table stored in AWS S3 that is registered in AWS Glue Data Catalog. The team wants to read the Iceberg table directly in a Databricks Notebook using Spark, without converting the table to Delta format or using UniForm, under Unity Catalog governance on standard (shared) access mode clusters where custom Spark configuration is not permitted.
Which approach should the data engineer use to enable querying of the Iceberg table from Databricks?

A. Enable UniForm on the existing Iceberg table by running ALTER TABLE ... SET TBLPROPERTIES ('delta.universalFormat.enabledFormats' = 'iceberg'), which allows Spark to read the Iceberg table natively through the Delta reader.
B. Use spark.read.format("iceberg") with the S3 path directly and no additional catalog configuration, because the Iceberg format reader resolves Glue-registered tables automatically from the S3 URI.
C. Create a Lakehouse Federation connection to the AWS Glue Data Catalog and a foreign catalog in Unity Catalog, then query the Iceberg table as a read-only foreign Iceberg table through the federated catalog.
D. Configure the Spark session with the Iceberg Glue catalog by setting spark.sql.catalog.my_catalog to the Iceberg Glue catalog implementation class and the corresponding catalog properties, then query using the configured catalog namespace.

Answer: C

Explanation:
Databricks supports reading externally managed Apache Iceberg tables through Unity Catalog catalog federation.
The engineer can establish a connection to AWS Glue Data Catalog and register a foreign catalog in Unity Catalog. This exposes supported Iceberg tables for read-only access without converting them into Delta Lake tables.
Unity Catalog manages authorization, while Databricks retrieves table metadata from the foreign catalog and reads the underlying files in cloud object storage.
Option A incorrectly attempts to apply Delta UniForm properties to an existing Iceberg table. Option B assumes that the Iceberg reader automatically discovers Glue metadata. Option C requires custom Spark catalog configuration, which the scenario prohibits.
Databricks requires a supported runtime, appropriate storage permissions, and a configured foreign-catalog storage root for Iceberg table access.

NEW QUESTION # 77
Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?

A. Regex
B. C++
C. Julia
D. Scala Datasets
E. pyspsark.ml.feature

Answer: A

Explanation:
Explanation
Regex, or regular expressions, are a powerful way of matching patterns in text. They can be used to identify key areas of text when parsing Spark Driver log4j output, such as the log level, the timestamp, the thread name, the class name, the method name, and the message. Regex can be applied in various languages and frameworks, such as Scala, Python, Java, Spark SQL, and Databricks notebooks. References:
https://docs.databricks.com/notebooks/notebooks-use.html#use-regular-expressions
https://docs.databricks.com/spark/latest/spark-sql/udf-scala.html#using-regular-expressions-in-udfs
https://docs.databricks.com/spark/latest/sparkr/functions/regexp_extract.html
https://docs.databricks.com/spark/latest/sparkr/functions/regexp_replace.html

NEW QUESTION # 78
......

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