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| Section | Objectives |
|---|---|
| Data Governance | - Unity Catalog Permissions
|
| Data Sharing and Federation | - Delta Sharing
|
| Data Transformation, Cleansing, and Quality | - Data Quality
|
| Data Modelling | - Scalable Data Models
|
| Developing Code for Data Processing using Python and SQL | - Building and Testing ETL Pipelines
|
| Cost & Performance Optimisation | - Delta Optimization
|
| Monitoring and Alerting | - Alerting
|
| Ensuring Data Security and Compliance | - Compliance
|
| Debugging and Deploying | - Debugging and Troubleshooting
|
| Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
Question 1
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:
For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:
Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?
A. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
B. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
C. Populate the dashboard by configuring a nightly batch job to save the required values as a table overwritten with each update.
D. Define a view against the products_per_order table and define the dashboard against this view.
E. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
Question 2
A platform engineer needs to report the resource consumption, categorized by SKU tier, across all workspaces. The engineer decides to use the system.billing.usage system table to create a query. Which SQL query will accurately return the daily usage by product?
A.
B.
C.
D. 
Question 3
A workspace admin has created a new catalog called finance_data and wants to delegate permission management to a finance team lead without giving them full admin rights. Which privilege should be granted to the finance team lead?
A. MANAGE privilege on the finance_data catalog.
B. Make the finance team lead a metastore admin.
C. GRANT OPTION privilege on the finance_data catalog.
D. ALL PRIVILEGES on the finance_data catalog.
Question 4
A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?
A. Perform a full outer join on a unique key and overwrite existing data.
B. Rely on Delta Lake schema enforcement to prevent duplicate records.
C. VACUUM the Delta table after each batch completes.
D. Perform an insert-only merge with a matching condition on a unique key.
E. Set the configuration delta.deduplicate = true.
Question 5
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.
Solutions:
| Question 1 Answer: C | Question 2 Answer: A | Question 3 Answer: A | Question 4 Answer: D | Question 5 Answer: B |
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