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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modelling | 6% | - Design Medallion Architecture - Optimize table design and partitioning - Implement dimensional and relational models |
| Topic 2: Data Governance | 7% | - Manage data assets and metadata - Use Unity Catalog for governance - Enforce data policies and standards |
| Topic 3: Data Transformation, Cleansing, and Quality | 10% | - Enforce data quality standards - Apply data cleansing and validation rules - Implement schema evolution and management |
| Topic 4: Developing Code for Data Processing using Python and SQL | 22% | - Write efficient and maintainable code - Implement complex data processing logic - Use Databricks-specific libraries and APIs |
| Topic 5: Debugging and Deploying | 10% | - Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs |
| Topic 6: Data Ingestion & Acquisition | 7% | - Handle incremental and batch data loads - Use Auto Loader and structured streaming - Ingest data from diverse sources |
| Topic 7: Ensuring Data Security and Compliance | 10% | - Secure data at rest and in transit - Ensure data privacy and compliance - Implement access control and permissions |
| Topic 8: Data Sharing and Federation | 5% | - Use Delta Sharing for secure data sharing - Manage cross-platform data access - Implement Lakehouse Federation |
| Topic 9: Cost & Performance Optimisation | 13% | - Optimize compute and storage resources - Improve query and pipeline performance - Apply cost management best practices |
| Topic 10: Monitoring and Alerting | 10% | - Track data lineage and metrics - Monitor pipeline performance and health - Set up alerts and notifications |
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NEW QUESTION # 108
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.
Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?
Answer: C
Explanation:
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view.
Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter.
NEW QUESTION # 109
A Delta Lake table was created with the below query:
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Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?
Answer: A
Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly.
NEW QUESTION # 110
A data engineer is setting up a pipeline to ingest data from a message bus system that occasionally delivers duplicate messages. The duplicate messages can be a week apart. The target is a Databricks Delta Lake table where each record should appear exactly once. Which Databricks ingestion pattern should be implemented to handle potential duplicates where events can arrive outside of the configured watermark?
Answer: D
Explanation:
Using MERGE INTO with a unique key enforces idempotent writes at the Delta Lake table level.
This approach reliably handles duplicates even when events arrive far outside any streaming watermark, ensuring that each logical record is written exactly once regardless of arrival time.
NEW QUESTION # 111
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
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Which statement describes this implementation?
Answer: A
NEW QUESTION # 112
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?
Answer: E
Explanation:
This is the correct answer because it ensures that this requirement is met. The requirement is that all tables in the Lakehouse should be configured as external Delta Lake tables. An external table is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created by using the location keyword to specify the path to an existing directory in a cloud storage system, such as DBFS or S3. By creating external tables, the data engineering team can avoid losing data if they drop or overwrite the table, as well as leverage existing data without moving or copying it.
NEW QUESTION # 113
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