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Databricks Lakehouse Accreditation – 70+ Exam Questions with Verified Answers – Delta Lake, Unity Catalog, Workflows, SQL – 2025/2026

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This document contains over 70 expert-verified exam questions and answers for the Databricks Lakehouse Accreditation Badge Exam, updated for the 2025/2026 exam cycle. It is designed as a comprehensive preparation resource for professionals and students pursuing foundational certification in the Databricks Lakehouse Platform, focusing on architectural knowledge, feature capabilities, and practical integrations. Topics covered include: Lakehouse architecture fundamentals: differences between data lakes, warehouses, and the unified lakehouse solution; support for ACID transactions, schema enforcement, and version control with Delta Lake Delta Lake capabilities: scalable metadata handling, time travel, unified streaming and batch processing, and support for updates/deletes/merges Unity Catalog governance: fine-grained access control, cross-cloud data governance, three-level object referencing (Metastore → Catalog → Schema → Table), and secure identity management Databricks SQL and serverless compute: SQL warehouses, auto-scaling, query scheduling, cost efficiency, and BI tool integrations Data orchestration with Workflows: multi-cloud orchestration, notebook version control, and task support for machine learning, data engineering, and ingestion Streaming support: Auto Loader, Structured Streaming, Delta Live Tables, and MLflow for real-time monitoring Machine learning and automation: built-in model serving, AutoML, distributed training, and optimized frameworks Best suited for: Candidates preparing for the Databricks Lakehouse Accreditation Badge exam Data analysts and engineers seeking foundational knowledge of Databricks services Students and professionals working with SQL, Delta Lake, or data pipeline automation Instructors or technical trainers looking to create Databricks-based practice sets or mock assessments Each question is accompanied by a clear, accurate answer in alignment with Databricks documentation and platform functionality. The content reflects the structure and emphasis of the official exam. Keywords: Databricks Lakehouse Databricks SQL Delta Lake Unity Catalog Photon Structured Streaming Auto Loader Delta Live Tables SQL warehouses Lakehouse architecture Access control Serverless compute BI tool integration Data governance Metastore hierarchy Machine learning AutoML Data orchestration Workflows Streaming ETL Catalog schema Identity management Query optimization

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Subido en
29 de noviembre de 2025
Número de páginas
20
Escrito en
2025/2026
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Databricks Lakehouse Accreditation
Badge Exam Questions 2025/2026 Exam
All Answers and Illustrations Given



Which of the following is a common problem within a data lake architecture

that can be easily solved by using the Databricks Lakehouse Platform?

Select three responses.

- Ineffective partitioning

- Too many small files

- Lack of cloud service integrations

- Inability to use open-source data formats

,- Lack of ACID transaction support - 🧠ANSWER ✔✔Ineffective partitioning,

Too many small files, Lack of ACID transaction support.




Also: Lack of schema enforcement, lack of integration with a data catalog

Maintaining and improving data quality is a major goal of modern data

engineering. Which of the following contributes directly to high levels of

data quality within the Databricks Lakehouse Platform? Select two

responses.




- Data expectations enforcement

- Business intelligence tool integrations

- Simplified machine learning model serving

- Table schema evolution


- Apache Spark's data format flexibility - 🧠ANSWER ✔✔data expectations

enforcement, Apache Spark's data format flexibility?

, Which of the following is a benefit of the Databricks Lakehouse Platform

being designed to support all data and artificial intelligence (AI) workloads?

Select four responses.




1 Analysts can easily integrate their favorite business intelligence (BI) tools

for further analysis.

2 Data analysts, data engineers, and data scientists can easily collaborate

within a single platform.

3 Data workloads can be automatically scaled when needed.

4 There is increased need for multiple, specialist platform administrators to

maintain each component of the unified platform.

5 Data teams can all utilize secure data from a single source to deliver

reliable, consistent results across workloads at scale. - 🧠ANSWER ✔✔1, 2,

3, 5

Data sharing has traditionally been performed by proprietary vendor

solutions, SSH File Transfer Protocol (SFTP), or cloud-specific solutions.

However, each of these sharing tools and solutions comes with its own set




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