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

Section	Objectives
Topic 1: Data Modeling and Transformation	- Spark SQL transformations - Dimensional modeling concepts - Performance optimization techniques
Topic 2: Production Pipelines and Orchestration	- Databricks Workflows - Error handling and recovery strategies - Job scheduling and monitoring
Topic 3: Databricks Lakehouse Platform Architecture	- Workspace and cluster architecture - Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold)
Topic 4: Delta Lake and Data Management	- Schema evolution and enforcement - Time travel and versioning - Delta Lake transactions and ACID properties
Topic 5: Data Ingestion and Processing	- Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader - ETL pipeline design patterns

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

NEW QUESTION # 68
A data engineer is designing a system leveraging Lakeflow Declarative Pipeline technology to process real-time truck telemetry data ingested from JSON files in S3 using Auto Loader. The data includes truck_id, timestamp, location, speed, and fuel_level. The system must support two use cases:
- Near-real-time monitoring of the latest location, speed, and
fuel_level per truck_id for the operations team.
- Daily aggregated reports of total distance traveled and average fuel
efficiency per truck_id for the management team.
Which approach should the data engineer use for streaming tables and materialized views in the Lakeflow Declarative Pipeline to meet these requirements?

A. Define a materialized view to ingest and store the raw telemetry data, and create a streaming table to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
B. Define a streaming table to ingest and store the raw telemetry data, and create a streaming table to compute the daily aggregated distance and fuel efficiency per truck_id reporting. Create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
C. Define a streaming table to ingest and store the raw telemetry data, and create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
D. Define a streaming table to ingest and store the raw telemetry data, and create a streaming table to incrementally compute the latest location, speed, and fuel_level per truck_id for real-time monitoring. Create a materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.

Answer: D

Explanation:
A streaming table is the right construct to ingest continuously arriving telemetry from Auto Loader.
Computing the latest per truck_id requires near-real-time incremental updates as new events arrive, which is best handled with a downstream streaming table. The daily aggregates are naturally suited to a materialized view, which maintains precomputed results for reporting and refreshes efficiently without requiring a continuously running streaming aggregation for a once- per-day consumption pattern.

NEW QUESTION # 69
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A. Task queueing resulting from improper thread pool assignment.
B. Skew caused by more data being assigned to a subset of spark-partitions.
C. Network latency due to some cluster nodes being in different regions from the source data
D. Credential validation errors while pulling data from an external system.
E. Spill resulting from attached volume storage being too small.

Answer: B

Explanation:
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills.

NEW QUESTION # 70
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

A. Run source env/bin/activate in a notebook setup script
B. Use &sh install in a notebook cell
C. Install libraries from PyPi using the cluster UI
D. Use &Pip install in a notebook cell

Answer: C

Explanation:
Installing a Python package scoped at the notebook level to all nodes in the currently active cluster in Databricks can be achieved by using the Libraries tab in the cluster UI. This interface allows you to install libraries across all nodes in the cluster. While the %pip command in a notebook cell would only affect the driver node, using the cluster UI ensures that the package is installed on all nodes.

NEW QUESTION # 71
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
B. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
C. Because User A created the jobs, their identity will be associated with both the job creation Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from events and the job run events.
D. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
E. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.

Answer: E

Explanation:
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity.
Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs.

NEW QUESTION # 72
When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

A. Total Disk Space remains constant
B. Bytes Received never exceeds 80 million bytes per second
C. Network I/O never spikes
D. CPU Utilization is around 75% Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
E. The five Minute Load Average remains consistent/flat

Answer: D

Explanation:
In the context of cluster performance and resource utilization, a CPU utilization rate of around
75% is generally considered a good indicator of efficient resource usage. This level of CPU utilization suggests that the cluster is being effectively used without being overburdened or underutilized. A consistent 75% CPU utilization indicates that the cluster's processing power is being effectively employed while leaving some headroom to handle spikes in workload or additional tasks without maxing out the CPU, which could lead to performance degradation. A five Minute Load Average that remains consistent/flat (Option A) might indicate underutilization or a bottleneck elsewhere.
Monitoring network I/O (Options B and C) is important, but these metrics alone don't provide a complete picture of resource utilization efficiency.
Total Disk Space (Option D) remaining constant is not necessarily an indicator of proper resource utilization, as it's more related to storage rather than computational efficiency.

NEW QUESTION # 73
......

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