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The Databricks Certified-Data-Engineer-Professional certification exam is an industrial-recognized Databricks Certified Data Engineer Professional (Certified-Data-Engineer-Professional) certificate that is designed to validate candidates' skills, experience, and knowledge quickly. The Databricks Certified Data Engineer Professional (Certified-Data-Engineer-Professional) certification exam has been inspiring Databricks professionals since its beginning. Now this Certified-Data-Engineer-Professional certification exam has become solid proof of certain skills set and knowledge.

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

SectionWeightObjectives
Security and Governance~10%- Manage Unity Catalog permissions and ACLs
- Implement row-level security, column masking, and compliance
Developing Code for Data Processing using Python and SQL~22%- Manage dependencies, libraries, and UDFs
- Implement scalable Python/SQL code and project structures
- Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader
Data Transformation, Cleansing, and Quality~12%- Enforce data quality and quarantine bad data
- Apply advanced Spark transformations
CI/CD, Testing, and Deployment~6%- Deploy with Declarative Automation Bundles, CLI, and REST API
- Implement testing and deployment pipelines
Data Sharing and Federation~8%- Configure Delta Sharing and Lakehouse Federation
Cost and Performance Optimization~13%- Optimize queries, clusters, and storage
- Leverage system tables and observability tools
Monitoring, Logging, and Troubleshooting~8%- Use Spark UI, Query Profiler, and system tables
- Diagnose common pipeline and job failures
Data Modeling~10%- Apply dimensional modeling techniques
- Design scalable Delta Lake schemas and clustering
Streaming Workloads and Change Data Capture~11%- Apply AUTO CDC APIs and exactly-once semantics
- Implement reliable streaming pipelines

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Databricks Certified Data Engineer Professional Sample Questions (Q95-Q100):

NEW QUESTION # 95
A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays.
Which limitation will the team face while diagnosing this problem?

Answer: B

Explanation:
When adding new fields to a Delta table's schema, these fields will not be retrospectively applied to historical records that were ingested before the schema change. Consequently, while the team can use the new metadata fields to investigate transient processing delays moving forward, they will be unable to apply this diagnostic approach to past data that lacks these fields.


NEW QUESTION # 96
Which distribution does Databricks support for installing custom Python code packages?

Answer: B

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/workflows/jobs/how-to/use-python-wheels-in-workflows


NEW QUESTION # 97
Which statement describes integration testing?

Answer: D

Explanation:
Integration testing is a type of software testing where components of the software are gradually integrated and then tested as a unified group.


NEW QUESTION # 98
A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing. Which role must be in place to perform these actions across the metastore?

Answer: A

Explanation:
Metastore Admins have the highest administrative privileges within a Unity Catalog metastore.
They can transfer ownership of any Unity Catalog object, including catalogs, schemas, tables, storage credentials, and external locations. Metastore Admins are also required to manage Delta Sharing configurations such as creating or transferring shares and recipients.
Account Admins, by contrast, only create metastores and cannot change ownership or manage Delta Sharing objects. Workspace Admins have privileges limited to workspace-level management, not cross-metastore access.


NEW QUESTION # 99
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

Answer: C

Explanation:
This is the correct answer because spark.sql.files.maxPartitionBytes is a configuration parameter that directly affects the size of a spark-partition upon ingestion of data into Spark. This parameter configures the maximum number of bytes to pack into a single partition when reading files from file- based sources such as Parquet, JSON and ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in size, unless there are too many small files or only one large file.


NEW QUESTION # 100
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