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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 |
| Data Modeling | ~10% | - Apply dimensional modeling techniques - Design scalable Delta Lake schemas and clustering |
| Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| Streaming Workloads and Change Data Capture | ~11% | - Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines |
| Developing Code for Data Processing using Python and SQL | ~22% | - Manage dependencies, libraries, and UDFs - Implement scalable Python/SQL code and project structures - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader |
| CI/CD, Testing, and Deployment | ~6% | - Implement testing and deployment pipelines - Deploy with Declarative Automation Bundles, CLI, and REST API |
| Data Transformation, Cleansing, and Quality | ~12% | - Enforce data quality and quarantine bad data - Apply advanced Spark transformations |
| Security and Governance | ~10% | - Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs |
| Monitoring, Logging, and Troubleshooting | ~8% | - Use Spark UI, Query Profiler, and system tables - Diagnose common pipeline and job failures |
Databricks Certified Data Engineer Professional Sample Questions:
1. Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?
A) workspace
B) libraries
C) jobs
D) fs
E) configure
2. A platform team is creating a standardized template for Databricks Asset Bundles to support CI/CD. The template must specify defaults for artifacts, workspace root paths, and a run identity, while allowing a "dev" target to be the default and override specific paths. How should the team use databricks.yml to satisfy these requirements?
A) Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.
B) Use bundle, artifacts, workspace, run_as, and targets at the top level; set one target with default:true and override workspace paths or artifacts under that target.
C) Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.
D) Use project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.
3. A data engineer is configuring a Databricks Asset Bundle to deploy a job with granular permissions.
The requirements are:
- Grant the data-engineers group CAN_MANAGE access to the job.
- Ensure the auditors' group can view the job but not modify/run it.
- Avoid granting unintended permissions to other users/groups.
How should the data engineer deploy the job while meeting the requirements?
A) resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
- group_name: admin-team
level: IS_OWNER
B) resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
C) resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
permissions:
- group_name: auditors
level: CAN_VIEW
D) permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
4. What statement is true regarding the retention of job run history?
A) It is retained for 60 days, after which logs are archived
B) It is retained for 60 days, during which you can export notebook run results to HTML
C) It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
D) It is retained until you export or delete job run logs
E) It is retained for 90 days or until the run-id is re-used through custom run configuration
5. What is the first line of a Databricks Python notebook when viewed in a text editor?
A) %python
B) // Databricks notebook source
C) # Databricks notebook source
D) -- Databricks notebook source
E) # MAGIC %python
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: B | Question # 5 Answer: C |
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