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

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

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Databricks Certified Data Engineer Professional Sample Questions (Q173-Q178):

NEW QUESTION # 173
A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON.
The code snippet is:
df = (spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("badRecordsPath", "/tmp/somewhere/badRecordsPath")
.schema("a int, b int")
.load("/Volumes/catalog/schema/raw_data/"))
What is the cause of the missing data?

A. The engineer forgot to set the option "cloudFiles.quarantineMode" = "rescue".
B. The source data is valid JSON but does not conform to the defined schema in some way.
C. The badRecordsPath location is accumulating many small files.
D. At some point, the upstream data provider switched everything to multi-line JSON.

Answer: B

Explanation:
Auto Loader quarantines records that cannot be parsed according to the specified schema, even if the JSON itself is well formed. If incoming records contain additional fields, missing fields, or incompatible data types compared to the declared schema, they are treated as invalid and routed to the bad records path.

NEW QUESTION # 174
A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data. Which configuration will provide the optimal performance while enabling these capabilities?

A. Use the open sharing protocol instead of Databricks-to-Databricks sharing for better performance.
B. Share tables WITH HISTORY, ensure tables don't have partitioning enabled, and enable CDF before sharing.
C. Share the entire schema WITHOUT HISTORY and rely on recipient-side caching for performance.
D. Share tables WITHOUT HISTORY and enable partitioning for better query performance.

Answer: B

Explanation:
The official Delta Sharing guidance specifies that in order for recipients to use time travel queries and streaming reads, providers must share Delta tables WITH HISTORY. Sharing history ensures the Delta log is included, which enables efficient access to table snapshots and incremental data streams.
Additionally, Change Data Feed (CDF) must be enabled prior to sharing if downstream consumers require streaming CDC queries. Without history, recipients cannot perform time travel or streaming queries. Open sharing supports static Delta tables but lacks streaming support.
Therefore, sharing tables WITH HISTORY and enabling CDF is the required configuration for both performance and functionality.

NEW QUESTION # 175
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over
20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
B. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
C. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
D. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
E. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.

Answer: D

Explanation:
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

NEW QUESTION # 176
Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?

A. They minimize memory usage by streaming each row individually through a lightweight Python wrapper, avoiding batch processing overhead.
B. They eliminate the JVM-Python boundary by bypassing serialization entirely, thereby avoiding data conversion overhead.
C. They allow row-level execution of functions in Python with native Spark optimization, removing the need for columnar execution.
D. They leverage Apache Arrow to enable vectorized operations between the JVM and Python runtimes, reducing serialization costs and improving computational efficiency.

Answer: D

Explanation:
Pandas UDFs use Apache Arrow to transfer data between the JVM and Python in a columnar, vectorized format. This significantly reduces serialization overhead and enables efficient batch processing, resulting in much better performance than traditional row-by-row PySpark UDFs on large datasets.

NEW QUESTION # 177
A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.
When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?

A. Network I/O never spikes
B. Overall cluster CPU utilization is around 25%
C. Bytes Received never exceeds 80 million bytes per second
D. Total Disk Space remains constant
E. The five Minute Load Average remains consistent/flat

Answer: B

Explanation:
This is the correct answer because it indicates a bottleneck caused by code executing on the driver. A bottleneck is a situation where the performance or capacity of a system is limited by a single component or resource. A bottleneck can cause slow execution, high latency, or low throughput. A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor. When evaluating the Ganglia Metrics for this cluster, one can look for indicators that show how the cluster resources are being utilized, such as CPU, memory, disk, or network. If the overall cluster CPU utilization is around 25%, it means that only one out of the four nodes (driver + 3 executors) is using its full CPU capacity, while the other three nodes are idle or underutilized. This suggests that the code executing on the driver is taking too long or consuming too much CPU resources, preventing the executors from receiving tasks or data to process. This can happen when the code has driver-side operations that are not parallelized or distributed, such as collecting large amounts of data to the driver, performing complex calculations on the driver, or using non-Spark libraries on the driver.

NEW QUESTION # 178
......

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