Which languages are supported by Serverless compute clusters? (Choose two.)
Answer(s): A,B
Serverless compute clusters in Databricks support SQL (via Serverless SQL Warehouses) and Python (for interactive and job-based execution). Other languages like R, Scala, and Java require regular clusters.
A Data Engineer is designing Bronze layer in Databricks Medallion Architecture. The raw data is collected from multiple sources (Clickstream in JSON, Transactions in CSV).The task is to design the Bronze layer of the Medallion Architecture to ingest and store this raw data for further processing.Which operation applies to the Bronze layer?
Answer(s): A
In the Bronze layer of the Medallion Architecture, raw data from multiple sources is ingested without transformations while preserving its original schema. The data is then stored in Delta format, serving as the single source of truth for downstream Silver and Gold layers where cleaning, standardization, and business logic are applied.
What is the primary function of the Silver layer in the Databricks medallion architecture?
Answer(s): C
The Silver layer in the Databricks Medallion Architecture is responsible for validating, cleaning, and deduplicating raw Bronze data. This prepares data in a structured and reliable form, making it ready for downstream enrichment and analytics in the Gold layer.
A data engineer needs to combine sales data from an on-premises PostgreSQL database with customer data in Azure Synapse for a comprehensive report. The goal is to avoid data duplication and ensure up-to-date information.How should the data engineer achieve this using Databricks?
Answer(s): B
Lakehouse Federation allows Databricks to directly query external data sources like PostgreSQL and Azure Synapse without duplicating data. This ensures the report always uses up-to-date information while avoiding the overhead and cost of data movement.
A data engineering team wants to validate a new ingestion pipeline locally while ensuring large aggregations run in serverless compute in their Databricks workspace. They plan to use Databricks Connect and have the option to attach to either a shared cluster or serverless.Which workspace requirement should be confirmed first to avoid connection failures?
Databricks Connect requires Unity Catalog to be enabled when connecting to serverless compute, and the Databricks Connect version must explicitly support serverless for the target Databricks Runtime to ensure compatibility and prevent connection failures.
A data engineer needs to conduct Exploratory Analysis on data residing in a database that is within the company's custom-defined network in the cloud. The data engineer is using SQL for this task.Which type of SQL Warehouse will enable the data engineer to process large numbers of queries quickly and cost-effectively?
A serverless SQL Warehouse automatically scales to handle large numbers of queries, optimizes performance without manual configuration, and charges only for actual usage, making it the most cost-effective and efficient option for exploratory SQL analysis.
A data engineer must deliver a trustworthy customer 360 dataset in Databricks for data scientists and BI teams. The engineer plans to join deduplicated customer records with cleaned transaction data, enforce schema and data quality checks, and create a conformed "customer_transactions" view. Later, highly aggregated, domain-specific tables (for weekly spend and executive summaries) will be produced for dashboards.Where should the engineer build the conformed "customer_transactions" dataset, and where should the aggregated, report-ready tables reside?
Answer(s): D
The conformed customer_transactions dataset belongs in Silver because it represents cleaned, deduplicated, and validated data that is suitable for broad analytical use. Aggregated, domain-specific, and report-ready tables are best placed in Gold, as this layer is designed for highly curated datasets optimized for BI, dashboards, and executive reporting.
A data engineer requires rapid iteration on pipelines while maintaining reliable rollbacks after bad ingests, ensuring audit trails for regulatory compliance, and providing consistent access to a single source of truth for both AI and BI workloads.Which strategy should the data engineer apply to meet the needs?
Delta Lake provides ACID transactions and time travel for safe pipeline iteration and reliable rollbacks, while Unity Catalog ensures governed, auditable access with lineage and a single source of truth shared consistently across AI and BI workloads.
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