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

Section	Weight	Objectives
Topic 1: Streaming Workloads and Change Data Capture	~11%	- Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines
Topic 2: Data Transformation, Cleansing, and Quality	~12%	- Apply advanced Spark transformations - Enforce data quality and quarantine bad data
Topic 3: Security and Governance	~10%	- Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs
Topic 4: Data Sharing and Federation	~8%	- Configure Delta Sharing and Lakehouse Federation
Topic 5: Data Modeling	~10%	- Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques
Topic 6: Developing Code for Data Processing using Python and SQL	~22%	- Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Implement scalable Python/SQL code and project structures - Manage dependencies, libraries, and UDFs
Topic 7: Cost and Performance Optimization	~13%	- Leverage system tables and observability tools - Optimize queries, clusters, and storage
Topic 8: CI/CD, Testing, and Deployment	~6%	- Implement testing and deployment pipelines - Deploy with Declarative Automation Bundles, CLI, and REST API
Topic 9: Monitoring, Logging, and Troubleshooting	~8%	- Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables

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Databricks Certified Data Engineer Professional Sample Questions (Q143-Q148):

NEW QUESTION # 143
A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?

A. Edit the global cluster policy to allow the restricted instance type, then re-run the entire job.
B. Manually create a new cluster for Task Y, update the job configuration, and trigger a full re-run.
C. Delete the failed run, disable the cluster policy, and re-execute all tasks.
D. Use "Repair run", override the cluster configuration for Task Y to use a permitted instance type, and let Databricks re-run only Task Y.

Answer: D

Explanation:
Repair run allows re-running only the failed task without affecting successfully completed tasks.
Overriding the cluster configuration for the specific task resolves the policy violation quickly while keeping the rest of the job intact and minimizing disruption.

NEW QUESTION # 144
The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
B. Schedule a job to execute the pipeline once an hour on a new job cluster.
C. Schedule a job to execute the pipeline once an hour on a dedicated interactive cluster.
D. Configure a job that executes every time new data lands in a given directory.

Answer: B

Explanation:
Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost-effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the cost, while also fulfilling the requirement for hourly data updates for the business reporting team's dashboards.

NEW QUESTION # 145
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

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

Answer: A

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 # 146
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 User A created the jobs, their identity will be associated with both the job creation events and the job run events.
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 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.
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 the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.

Answer: C

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 # 147
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

A. Total VMs: 1
400 GB per Executor
160 Cores / Executor
B. Total VMs: 8
50 GB per Executor
20 Cores / Executor
C. Total VMs: 2
200 GB per Executor
80 Cores / Executor
D. Total VMs: 4
100 GB per Executor
40 Cores/Executor

Answer: A

Explanation:
https://docs.databricks.com/en/clusters/cluster-config-best-practices.html

NEW QUESTION # 148
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