How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?

• A. Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.
• B. Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.
• C. Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.
• D. Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.

A data architect is implementing Delta Sharing as part of their data governance strategy to enable secure data collaboration with external partners and internal business units. The architect must establish a permission framework that allows designated data stewards to create shares for their respective domains while maintaining security boundaries and audit compliance. Which specific permissions and roles must be assigned to enable users to create, configure, and manage Delta Shares while maintaining proper security governance and access controls?

• A. Only workspace admins can create and manage shares
• B. Users need to be metastore admins or have CREATE SHARE privilege for the metastore
• C. Users need the MANAGE SHARES permission on the workspace
• D. Any user with USE_CATALOG privilege can create shares

A company processes semi-structured JSON files from an external source using Auto Loader in a classic Databricks job. Occasionally, records arrive with null critical fields, invalid types, or unexpected nested schema variations. The engineer must ensure that malformed or non- conforming records are not dropped silently and are captured in a separate quarantine table. The pipeline should continue processing good records into the Bronze layer without failing the job, and the approach must support both batch and streaming ingestion.
The data engineer needs to build a robust ingestion pattern that automatically routes bad records to a quarantine Delta table, while still ingesting good records into the Bronze layer for further processing.
Which approach fulfills the quarantine mechanism in this ingestion architecture?

• A. Use Auto Loader with failFast mode to set to false, and enable schema evolution; invalid records will be silently ignored during ingestion.
• B. Use Auto Loader with LDP and implement an EXPECT () constraint with a record audit logic to route bad records.
• C. Use Lakeflow Spark Declarative Pipelines with a SQL pipeline; configure it to drop rows with nulls using where critical_fields is not null, and rely on audit logs for malformed data.
• D. Create a notebook job with inferSchema=True, write a streaming query with .foreachBatch() and catch exceptions using try/except to redirect failed batches to quarantine.

An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

• A. Shallow clone the table for the analytics team.
• B. Give the analytics team direct access to the production table.
• C. Create a new table for the analytics team using a CTAS statement.
• D. Deep clone the table for the analytics team.