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| Section | Weight | Objectives |
|---|---|---|
| Debugging and Deploying | 10% | - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices - Troubleshooting pipelines and errors |
| Data Modelling | 6% | - Schema design and management - Medallion Architecture implementation - Delta Lake table design |
| Data Ingestion & Acquisition | 7% | - Schema inference and evolution - Connecting to diverse data sources - Auto Loader and streaming ingestion |
| Data Transformation, Cleansing, and Quality | 10% | - Handling missing or inconsistent data - Data validation and quality checks - Standardization and normalization |
| Developing Code for Data Processing using Python and SQL | 22% | - Integration with Databricks APIs and tools - Batch and incremental processing logic - Data transformation and aggregation |
| Monitoring and Alerting | 10% | - Pipeline observability and logging - Setting up alerts and notifications - Performance and health monitoring |
| Ensuring Data Security and Compliance | 10% | - Access control and permissions - Compliance standards implementation - Data encryption and masking |
| Cost & Performance Optimisation | 13% | - Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling - Query optimization and caching |
| Data Governance | 7% | - Data lineage and metadata tracking - Policy enforcement - Unity Catalog management |
| Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
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NEW QUESTION # 183
A platform team lead is responsible for automating SQL Warehouse usage attribution across business units. They need to identify warehouse usage at the individual user level and share a daily usage report with an executive team that includes business leaders from multiple departments.
How should the platform lead generate an automated report that can be shared daily?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks provides system tables under the system.billing and system.access schemas for auditing and cost attribution across SQL Warehouses. The best practice is to build a dashboard in Databricks SQL using these system tables and configure it with a daily refresh schedule. This ensures automated, up-to-date reporting of compute consumption, user activity, and cost distribution. Sharing this dashboard with executives provides transparency without requiring them to run queries manually. This approach aligns with Databricks' operational guidance on workspace observability and cost governance, making B the correct answer.
NEW QUESTION # 184
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the fieldpk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?
Answer: D
Explanation:
Explanation
This is the correct answer because it meets the requirements of maintaining a full record of all values that have ever been valid in the source system and recreating the current table state with only the most recent value for each record. The code ingests all log information into a bronze table, which preserves the raw CDC data as it is. Then, it uses merge into to perform an upsert operation on a silver table, which means it will insert new records or update or delete existing records based on the change type and the pk_id columns. This way, the silver table will alwaysreflect the current state of the source table, while the bronze table will keep the history of all changes. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Upsert into a table using merge" section.
NEW QUESTION # 185
John Smith is a newly joined team member in the Marketing team who currently has access read access to sales tables but does not have access to delete rows from the table, which of the following commands help you accomplish this?
Answer: B
Explanation:
Explanation
The answer is GRANT MODIFY ON TABLE table_name TO john.smith@marketing.com , please note INSERT, UPDATE, and DELETE are combined into one role called MODIFY.
Below are the list of privileges that can be granted to a user or a group, SELECT: gives read access to an object.
CREATE: gives the ability to create an object (for example, a table in a schema).
MODIFY: gives the ability to add, delete, and modify data to or from an object.
USAGE: does not give any abilities, but is an additional requirement to perform any action on a schema object.
READ_METADATA: gives the ability to view an object and its metadata.
CREATE_NAMED_FUNCTION: gives the ability to create a named UDF in an existing catalog or schema.
MODIFY_CLASSPATH: gives the ability to add files to the Spark classpath.
ALL PRIVILEGES: gives all privileges (is translated into all the above privileges
NEW QUESTION # 186
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?
Answer: A
NEW QUESTION # 187
A table is registered with the following code:
Bothusersandordersare Delta Lake tables. Which statement describes the results of queryingrecent_orders?
Answer: E
NEW QUESTION # 188
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