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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Modeling | - Design and optimize data models
|
| Topic 2: Ensuring Data Security and Compliance | - Ensuring Compliance
|
| Topic 3: Data Sharing and Federation | - Share and federate data
|
| Topic 4: Debugging and Deploying | - Deploying CI/CD
|
| Topic 5: Monitoring and Alerting | - Alerting
|
| Topic 6: Developing Code for Data Processing using Python and SQL | - Using Python and Tools for Development
|
| Topic 7: Data Governance | - Govern enterprise data
|
| Topic 8: Cost & Performance Optimization | - Optimize cost and performance
|
| Topic 9: Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
| Topic 10: Data Transformation, Cleansing, and Quality | - Transform and validate data
|
Databricks Certified Data Engineer Professional Sample Questions:
The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?
- A. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
- B. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
- C. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
- D. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
Correct Answer: C 🗳️
Explanation: Only visible for Prep4cram members. You can sign-up / login (it's free).
A data engineer is masking a column containing email addresses. The goal is to produce output strings of identical length for all rows, while generating different outputs for different email values.
Which SQL function should be used to achieve this?
- A. mask(email, '?')
- B. sha1(email)
- C. sha2(email, 0)
- D. hash(email)
Correct Answer: D 🗳️
Explanation: Only visible for Prep4cram members. You can sign-up / login (it's free).
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: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
permissions:
- group_name: auditors
level: CAN_VIEW - C. resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- 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: [...]
Correct Answer: C 🗳️
Explanation: Only visible for Prep4cram members. You can sign-up / login (it's free).
A data engineer is optimizing a MERGE operation on an 800GB UC-managed table that experiences frequent updates and deletions. Which two actions should the engineer prioritize to improve MERGE performance? (Choose two.)
- A. Partition the table by date.
- B. Apply liquid clustering using the merge join keys.
- C. Enable deletion vectors on the table if not already enabled.
- D. Use ZORDER on high-cardinality columns.
- E. Overwrite the table instead of Merge.
Correct Answer: B,C 🗳️
Explanation: Only visible for Prep4cram members. You can sign-up / login (it's free).
A data engineer is working in an interactive notebook with many transformations before outputting the result from display(df.collect() ). The notebook includes wide transformations and a cross join.
The data engineer is getting the following error: "The spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached." Which action should the data engineer take?
- A. Rewrite their code to avoid putting memory pressure on the driver node.
- B. Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.
- C. Run the notebook on a single node cluster to keep driver from falling.
- D. Look at the compute metrics UI to see if the executors have higher than 90% memory utilization.
Correct Answer: A 🗳️
Explanation: Only visible for Prep4cram members. You can sign-up / login (it's free).
