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| Section | Objectives |
|---|---|
| Topic 1: Monitoring and Alerting | - Alerting
|
| Topic 2: Data Transformation, Cleansing, and Quality | - Data Quality
|
| Topic 3: Developing Code for Data Processing using Python and SQL | - Using Python and Tools for Development
|
| Topic 4: Ensuring Data Security and Compliance | - Data Security
|
| Topic 5: Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
| Topic 6: Debugging and Deploying | - Debugging and Troubleshooting
|
| Topic 7: Data Governance | - Unity Catalog Permissions
|
| Topic 8: Cost & Performance Optimisation | - Cost Optimization
|
| Topic 9: Data Sharing and Federation | - Lakehouse Federation
|
| Topic 10: Data Modelling | - Scalable Data Models
|
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?
Correct Answer: D 🗳️
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A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context.
Assuming the job is already deployed, they need to trigger its execution while ensuring the target- specific configuration is respected. Which command will trigger the job execution?
Correct Answer: C 🗳️
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Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets.
Which statement describes a limitation of Databricks Secrets?
Correct Answer: B 🗳️
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The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".
The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?



Correct Answer: C 🗳️
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?
Correct Answer: B 🗳️
Explanation: Only visible for Actual4test members. You can sign-up / login (it's free).
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