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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformation, Cleansing, and Quality | 10% | - Apply data cleansing and validation rules - Implement schema evolution and management - Enforce data quality standards |
| Topic 2: Developing Code for Data Processing using Python and SQL | 22% | - Use Databricks-specific libraries and APIs - Write efficient and maintainable code - Implement complex data processing logic |
| Topic 3: Monitoring and Alerting | 10% | - Track data lineage and metrics - Set up alerts and notifications - Monitor pipeline performance and health |
| Topic 4: Debugging and Deploying | 10% | - Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs |
| Topic 5: Data Sharing and Federation | 5% | - Use Delta Sharing for secure data sharing - Manage cross-platform data access - Implement Lakehouse Federation |
| Topic 6: Cost & Performance Optimisation | 13% | - Optimize compute and storage resources - Apply cost management best practices - Improve query and pipeline performance |
| Topic 7: Data Modelling | 6% | - Implement dimensional and relational models - Design Medallion Architecture - Optimize table design and partitioning |
| Topic 8: Data Governance | 7% | - Manage data assets and metadata - Enforce data policies and standards - Use Unity Catalog for governance |
| Topic 9: Ensuring Data Security and Compliance | 10% | - Secure data at rest and in transit - Implement access control and permissions - Ensure data privacy and compliance |
| Topic 10: Data Ingestion & Acquisition | 7% | - Use Auto Loader and structured streaming - Ingest data from diverse sources - Handle incremental and batch data loads |
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NEW QUESTION # 112
A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing. Which role must be in place to perform these actions across the metastore?
Answer: D
Explanation:
Metastore Admins have the highest administrative privileges within a Unity Catalog metastore.
They can transfer ownership of any Unity Catalog object, including catalogs, schemas, tables, storage credentials, and external locations. Metastore Admins are also required to manage Delta Sharing configurations such as creating or transferring shares and recipients.
Account Admins, by contrast, only create metastores and cannot change ownership or manage Delta Sharing objects. Workspace Admins have privileges limited to workspace-level management, not cross-metastore access.
NEW QUESTION # 113
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?
Answer: C
Explanation:
In Databricks, using the Secrets module allows for secure management of sensitive information such as database credentials. Granting 'Read' permissions on a secret key that maps to database credentials for a specific team ensures that only members of that team can access Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from these credentials. This approach aligns with the principle of least privilege, granting users the minimum level of access required to perform their jobs, thus enhancing security.
NEW QUESTION # 114
A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.
In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.
The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.
How can the data engineer fix this?
Answer: B
Explanation:
The issue with the refactored code is that it tries to use string interpolation to dynamically create table names within the dlc.table decorator, which will not correctly interpret the table names.
Instead, by using a dictionary with table names as keys and their configurations as values, the data engineer can iterate over the dictionary items and use the keys (table names) to properly configure the table settings. This way, the decorator can correctly recognize each table name, and the corresponding configuration settings can be applied appropriately.
NEW QUESTION # 115
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?
Answer: D
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 # 116
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".
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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?
Answer: A
NEW QUESTION # 117
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