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Databricks Lakehouse Platform Accreditation – 80+ Verified Q&A – Delta Lake, Unity Catalog, Photon, Workflows – 2025/2026

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This document includes 80+ verified exam questions and answers for the Databricks Lakehouse Platform Accreditation exam, fully updated for the 2025/2026 academic year. It serves as a comprehensive review and self-study guide aligned with Databricks Academy standards, focusing on the latest tools and architecture used across enterprise-grade lakehouse environments. The material covers: Lakehouse vs. Warehouse vs. Data Lake: comparison of schema flexibility, performance, transactional support, governance, and cloud scalability Delta Lake capabilities: ACID transactions, schema enforcement and evolution, scalable metadata, time travel, batch and streaming unification, and CRUD operations Unity Catalog: data governance, object hierarchy (metastore → catalog → schema → table), storage credentials, external locations, data sharing via Delta Sharing Databricks SQL and Photon engine: high-speed query engine, benefits of serverless SQL compute, end-to-end BI integration, performance improvements over Apache Spark Security and access control: IAM roles, ACLs, Secrets API, network protection, centralized identity management Databricks Workflows: orchestration of ingestion, engineering, ML, and BI tasks using Delta Live Tables (DLT) and dbt Streaming and ML support: Auto Loader, Structured Streaming, MLflow, AutoML, and MLOps tooling for full data lifecycle management Perfect for: Data engineers, analysts, and architects seeking foundational accreditation Candidates preparing for Databricks certification paths Professionals transitioning from traditional data warehouse systems to lakehouse architecture Instructors and technical leads delivering Databricks-based training or internal upskilling programs Each question reflects realistic exam scenarios, with answers grounded in Databricks documentation and platform design principles. Keywords: Databricks Lakehouse Delta Lake Databricks SQL Unity Catalog Photon engine Auto Loader Structured Streaming Delta Live Tables Data governance Serverless compute Storage credentials External locations Metastore Catalog schema Access control SQL dashboard BI integration MLflow AutoML MLOps Workflows orchestration Declarative ETL Data sharing Open storage format

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Institution
Databricks Lakehouse
Course
Databricks Lakehouse

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Databricks Lakehouse Platform
Accreditation 2025/2026 Exam
Questions and Answers | A+ Score
Assured



What are the pros and cons of a data warehouse? - 🧠ANSWER ✔✔Pros:

use for BI, analytics, structured & clean data, predefined schema




Cons: no support for semi or unstructured data, inflexible schemas,

struggled with volume and velocity upticks, and long processing time

, What are the pros and cons of a data lake? - 🧠ANSWER ✔✔Pros: flexible

data storage, streaming support, cost efficient in the cloud, support for AI

and ML




Cons: no transactional support, poor data reliability, slow analysis

performance, data governance concerns, data warehouses still needed

How does the data lakehouse combine the powers of data warehouses and

lakes? - 🧠ANSWER ✔✔allows all machine learning, SQL, BI, and

streaming use cases. one security and governance approach for all data

assets on all clouds. an open and reliable data platform to efficiently handle

all data types




- transaction support

- schema enforcement and governance

- BI support

- decoupled storage from compute

- open storage format

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Institution
Databricks Lakehouse
Course
Databricks Lakehouse

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