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

Section	Objectives
Topic 1: Data Ingestion and Transformation	- Transform and clean datasets using Spark SQL and DataFrame APIs - Ingest data using Apache Spark and Databricks - Handle batch and streaming data pipelines
Topic 2: Security, Governance, Monitoring, and Optimization	- Implement Unity Catalog governance and access control - Cost optimization and performance tuning - Monitor and optimize Spark workloads
Topic 3: Production Pipelines and Orchestration	- Build and manage workflows using Databricks Jobs - Pipeline reliability and fault tolerance - Automate ETL pipelines and scheduling
Topic 4: Data Modeling and Storage	- Delta Lake table design and optimization - Design scalable data lakehouse architectures - Schema evolution and data partitioning strategies

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q68-Q73):

NEW QUESTION # 68
You are designing an analytical to store structured data from your e-commerce platform and un-structured data from website traffic and app store, how would you approach where you store this data?

A. Use traditional data warehouse for structured data and use data lakehouse for un-structured data.
B. Data lakehouse can only store unstructured data but cannot enforce a schema
C. Traditional data warehouses are good for storing structured data and enforcing schema
D. Data lakehouse can store structured and unstructured data and can enforce schema

Answer: D

Explanation:
Explanation
The answer is, Data lakehouse can store structured and unstructured data and can enforce schema What Is a Lakehouse? - The Databricks Blog Graphical user interface, text, application Description automatically generated

NEW QUESTION # 69
Which of the following SQL commands are used to append rows to an existing delta table?

A. APPEND INTO DELTA table_name
B. APPEND INTO table_name
C. UPDATE table_name
D. INSERT INTO table_name
E. COPY DELTA INTO table_name

Answer: D

Explanation:
Explanation
The answer is INSERT INTO table_name
Insert adds rows to an existing table, this is very similar to add rows a traditional Database or Da-tawarehouse.

NEW QUESTION # 70
A data engineer is using Lakeflow Declarative Pipelines Expectations feature to track the data quality of their incoming sensor dat a. Periodically, sensors send bad readings that are out of range, and they are currently flagging those rows with a warning and writing them to the silver table along with the good data. They've been given a new requirement - the bad rows need to be quarantined in a separate quarantine table and no longer included in the silver table.
This is the existing code for their silver table:
@dlt.table
@dlt.expect("valid_sensor_reading", "reading < 120")
def silver_sensor_readings():
return spark.readStream.table("bronze_sensor_readings")
What code will satisfy the requirements?

A. @dlt.table@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")def silver_sensor_readings():return spark.readStream.table("bronze_sensor_readings")@dlt.table@dlt.expect("invalid_sensor_reading", "reading < 120")def quarantine_sensor_readings():return spark.readStream.table("bronze_sensor_readings")
B. @dlt.table@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")def silver_sensor_readings():return spark.readStream.table("bronze_sensor_readings")@dlt.table@dlt.expect_or_drop("invalid_sensor_reading", "reading >= 120")def quarantine_sensor_readings():return spark.readStream.table("bronze_sensor_readings")
C. @dlt.table@dlt.expect_or_drop("valid_sensor_reading", "reading < 120")def silver_sensor_readings():return spark.readStream.table("bronze_sensor_readings")@dlt.table@dlt.expect("invalid_sensor_reading", "reading >= 120")def quarantine_sensor_readings():return spark.readStream.table("bronze_sensor_readings")
D. @dlt.table@dlt.expect("valid_sensor_reading", "reading < 120")def silver_sensor_readings():return spark.readStream.table("bronze_sensor_readings")@dlt.table@dlt.expect("invalid_sensor_reading", "reading >= 120")def quarantine_sensor_readings():return spark.readStream.table("bronze_sensor_readings")

Answer: D

Explanation:
Lakeflow Declarative Pipelines (DLT) supports data quality enforcement using @dlt.expect, @dlt.expect_or_drop, and @dlt.expect_all.
@dlt.expect applies a rule and records whether rows pass or fail the condition but does not drop failing rows. Instead, failing rows can be written to a quarantine table.
@dlt.expect_or_drop enforces that only rows passing the condition flow downstream, dropping bad records automatically.
In this case, the requirement is:
Good rows (reading < 120) go to the silver table.
Bad rows (reading >= 120) go to a quarantine table.
Bad rows should not be included in silver.
The correct implementation is Option A, where:
The silver table uses @dlt.expect to validate reading < 120. These rows flow normally.
The quarantine table applies an expectation for reading >= 120, ensuring bad records are captured separately.
Other options are incorrect:
Option B/D: These either use expect_or_drop incorrectly or apply wrong conditions, leading to dropped rows without quarantining properly.
Option C: Uses expect_or_drop for both tables, which would discard bad rows instead of persisting them into a quarantine table.
Thus, Option A meets the business requirement to split good and bad data streams while ensuring both are captured for auditing and processing.

NEW QUESTION # 71
A data engineer is diagnosing a slow-running Delta Lake query in Databricks SQL that involves complex joins and large datasets.
They must determine whether the underlying cause is poor data skipping, inefficient join strategies, or excessive data shuffling.
Which approach should be used to identify the specific bottlenecks with native Databricks tools?

A. Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.
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 such as BroadcastNestedLoopJoin.
D. Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.

Answer: C

Explanation:
Databricks Query Profile provides detailed execution metrics that help engineers identify performance bottlenecks in SQL workloads.
The Top Operators panel highlights expensive query operations, including joins, scans, aggregations, sorting, and shuffling.
Operators such as BroadcastNestedLoopJoin can indicate inefficient join execution, particularly when large datasets are involved.
Query Profile also provides metrics for execution time, memory consumption, input rows, and data scanned. Engineers can use these measurements to identify excessive shuffle operations, inefficient joins, and poor file pruning.
Option B provides general resource information but lacks operator-level analysis. Option C examines query plans without providing actual execution measurements. Option D changes query execution behavior and may produce misleading performance comparisons.
Therefore, the Top Operators panel is the most appropriate diagnostic tool.
Databricks on AWS

NEW QUESTION # 72
A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each micro-batch of data is processed in less than 3 seconds; at least 12 times per minute, a micro-batch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution. Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

A. Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.
B. Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.
C. Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to the maximum allowable threshold should minimize this cost.
D. Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
* Exact extract: "If no trigger is specified, the default processing-time trigger runs micro-batches as fast as possible."
* Exact extract: "Trigger once processes all available data once and then stops." References: Structured Streaming triggers; Databricks Jobs and job clusters.

NEW QUESTION # 73
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