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

Section	Weight	Objectives
Monitoring and Troubleshooting	16%	- Performance Optimization - Monitoring - Troubleshooting
Data Processing	28%	- Data Transformation - ETL Pipelines - Structured Streaming - Spark SQL
Databricks Lakehouse Platform	24%	- Lakehouse Architecture - Delta Lake - Data Management - Unity Catalog
Data Modeling and Storage	20%	- Storage Optimization - Data Modeling - File Formats
Data Quality and Governance	12%	- Data Lineage - Governance - Data Quality

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q78-Q83):

NEW QUESTION # 78
A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?

A. Define and import unit test functions from a separate Databricks notebook
B. Run unit tests against non-production data that closely mirrors production
C. Define and unit test functions using Files in Repos
D. Define units test and functions within the same notebook

Answer: B

Explanation:
The best practice for running unit tests on functions that interact with data is to use a dataset that closely mirrors the production data. This approach allows data engineers to validate the logic of their functions without the risk of affecting the actual production data. It's important to have a representative sample of production data to catch edge cases and ensure the functions will work correctly when used in a production environment.

NEW QUESTION # 79
A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
How should the data engineer fulfill these requirements?

A. Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"
B. Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
"cloudFiles.schemaEvolutionMode"= "addNewColumns"
C. Use Auto Loader in batch mode with a daily job to overwrite the Delta table.
D. Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")

Answer: B

Explanation:
Lakeflow Spark Declarative Pipelines combined with Auto Loader provide fully managed incremental file ingestion with exactly-once guarantees and minimal operational overhead.
Enabling schema inference and evolution allows new columns in incoming JSON files to be incorporated automatically, satisfying the requirements for streaming ingestion, schema evolution, and reduced manual infrastructure management.

NEW QUESTION # 80
A view is registered with the following code:

Both users and orders are Delta Lake tables.
Which statement describes the results of querying recent_orders?

A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
B. All logic will execute when the view is defined and store the result of joining tables to the DBFS; this stored data will be returned when the view is queried.
C. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
D. Results will be computed and cached when the view is defined; these cached results will incrementally update as new records are inserted into source tables.

Answer: A

NEW QUESTION # 81
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable:
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Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
B. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
C. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
D. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
E. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.

Answer: A

Explanation:
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation using mergeInto.

NEW QUESTION # 82
A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling. Which approach should identify the specific bottlenecks using native Databricks tools?

A. Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.
B. Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.
C. Analyze the Top Operators panel in the Query Profile to identify high-cost operations like BroadcastNestedLoopJoin
D. Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.

Answer: C

Explanation:
The Query Profile's Top Operators panel surfaces the most expensive operators in the query execution, making it possible to directly identify bottlenecks such as inefficient join strategies, poor data skipping, or excessive shuffling. This native visualization highlights where time and resources are spent, enabling precise root-cause analysis for slow-running queries.

NEW QUESTION # 83
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