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

Section	Weight	Objectives
Cost & Performance Optimisation	13%	- Apply cost management best practices - Optimize compute and storage resources - Improve query and pipeline performance
Data Sharing and Federation	5%	- Use Delta Sharing for secure data sharing - Implement Lakehouse Federation - Manage cross-platform data access
Developing Code for Data Processing using Python and SQL	22%	- Write efficient and maintainable code - Implement complex data processing logic - Use Databricks-specific libraries and APIs
Data Governance	7%	- Use Unity Catalog for governance - Manage data assets and metadata - Enforce data policies and standards
Data Transformation, Cleansing, and Quality	10%	- Implement schema evolution and management - Enforce data quality standards - Apply data cleansing and validation rules
Debugging and Deploying	10%	- Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs
Data Modelling	6%	- Design Medallion Architecture - Optimize table design and partitioning - Implement dimensional and relational models
Data Ingestion & Acquisition	7%	- Ingest data from diverse sources - Handle incremental and batch data loads - Use Auto Loader and structured streaming
Monitoring and Alerting	10%	- Track data lineage and metrics - Set up alerts and notifications - Monitor pipeline performance and health
Ensuring Data Security and Compliance	10%	- Ensure data privacy and compliance - Implement access control and permissions - Secure data at rest and in transit

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q48-Q53):

NEW QUESTION # 48
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
B. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
C. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.
D. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.

Answer: C

Explanation:
Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. These statistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.

NEW QUESTION # 49
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A. Create a separate history table for each pk_id resolve the current state of the table by running a Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from union all filtering the history tables for the most recent state.
B. Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.
C. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
D. Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
E. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.

Answer: D

Explanation:
CDF captures changes only from a Delta table and is only forward-looking once enabled. The CDC logs are writing to object storage. So you would need to ingestion those and merge into downstream tables.

NEW QUESTION # 50
A data engineer is performing a join operation to combine values from a static userlookup table with a streaming DataFrame streamingDF.
Which code block attempts to perform an invalid stream-static join?

A. userLookup.join(streamingDF, ["user_id"], how="right")
B. userLookup.join(streamingDF, ["userid"], how="inner")
C. streamingDF.join(userLookup, ["user_id"], how="left")
D. streamingDF.join(userLookup, ["user_id"], how="outer")
E. streamingDF.join(userLookup, ["userid"], how="inner")

Answer: D

Explanation:
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#support- matrix-for-joins-in-streaming-queries

NEW QUESTION # 51
A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for 15 days (instead of the default 7 days), in order to permanently comply with the organization's data retention policy. Which code snippet correctly sets this retention period for deleted files?

A. from delta.tables import *
deltaTable = DeltaTable.forPath(spark, "/mnt/data/my_table")
deltaTable.deletedFileRetentionDuration = "interval 15 days"
B. spark.sql("VACUUM my_table RETAIN 15 HOURS")
C. spark.conf.set("spark.databricks.delta.deletedFileRetentionDuration", "15 days")
D. spark.sql("ALTER TABLE my_table SET TBLPROPERTIES
('delta.deletedFileRetentionDuration' = 'interval 15 days')")

Answer: D

Explanation:
The deleted file retention period in Delta Lake is controlled by the table property delta.deletedFileRetentionDuration. Setting this property via ALTER TABLE ensures the retention policy is persistently enforced at the table level, extending deleted file retention to 15 days in compliance with organizational requirements.

NEW QUESTION # 52
A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability. Which piece of Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from information is critical to this decision?

A. Delta Lake only supports Type 0 tables; once records are inserted to a Delta Lake table, they cannot be modified.
B. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
C. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
D. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
E. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires setting multiple fields in a single update.

Answer: B

Explanation:
Delta Lake's time travel feature allows users to access previous versions of a table, providing a powerful tool for auditing and versioning. However, using time travel as a long-term versioning solution for auditing purposes can be less optimal in terms of cost and performance, especially as the volume of data and the number of versions grow. For maintaining a full history of valid street addresses as they appear in a customers table, using a Type 2 table (where each update creates a new record with versioning) might provide better scalability and performance by avoiding the overhead associated with accessing older versions of a large table. While Type 1 tables, where existing records are overwritten with new values, seem simpler and can leverage time travel for auditing, the critical piece of information is that time travel might not scale well in cost or latency for long-term versioning needs, making a Type 2 approach more viable for performance and scalability.

NEW QUESTION # 53
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