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Databricks Certifications Practice Exams

Prepare for Databricks certifications in data analysis, data engineering, machine learning and generative AI. Compare Associate and Professional practice exams, work through exam-style questions and explanations, and choose the preparation that fits your next goal.

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Data + AI challengeExamsDigest preview

Explore three original questions about data and AI. Open each one to review the explanation.

1. Which SQL expression counts every input row, even when customer_id is NULL?

A. COUNT(*) — correct. It counts rows regardless of NULL values in individual columns. B. COUNT(customer_id) skips rows where customer_id is NULL. C. SUM(customer_id) adds numeric values rather than counting rows. D. COUNT(DISTINCT customer_id) counts unique, non-NULL values.

2. Which medallion layer usually holds business-ready aggregates?

C. Gold — correct. Gold typically contains curated data shaped for business questions. A. Bronze retains incoming raw data. B. Silver focuses on cleaned and validated data. D. A compute cluster executes workloads; it is not a medallion data layer.

3. What should you use to group and compare related MLflow training runs?

B. An MLflow experiment — correct. It groups runs so you can compare their logged parameters and metrics. A. A SQL warehouse provides compute for SQL workloads. C. A dashboard presents visual insights, but is not MLflow's run-organizing object. D. A storage folder can hold files, but does not by itself create MLflow experiment tracking.

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Exam objectives help you understand the topics a certification covers. Use them to organize your study, connect practice questions with the relevant knowledge areas and identify subjects that need further reading or practical work.

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Official Databricks exam guides

Explore certification-specific objective guides and study topics. Match the guide's exam code to your planned exam before using it as your study checklist.

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Browse the available practice exams for Data Analyst Associate, Data Engineer Associate and Professional, Machine Learning Associate and Professional, and Generative AI Engineer Associate.
Choose the role and tasks closest to your experience. Data Analyst Associate is relevant to SQL analysis and reporting; Data Engineer Associate focuses on data pipelines; Machine Learning Associate covers the ML workflow; and Generative AI Engineer Associate focuses on building generative AI applications. Read the official exam guide before choosing your preparation materials.
The official Data Engineer Professional and Machine Learning Professional pages currently list no formal prerequisites. That does not make them entry-level exams: review their recommended experience and objectives before deciding whether you are ready.
That depends on your certification. Data Analyst preparation requires SQL skills. Data Engineering and Machine Learning paths involve coding and platform workflows, with SQL or Python requirements described in their official guides. Use practice questions alongside hands-on work with the tools relevant to your exam.
Open the individual product page to see its question count, practice formats, explanations and included study resources. Check that page for details of timed sets, flashcards, study guides and access terms; the exact contents are specific to the pack you choose.
Yes. Compare the ExamsDigest lifetime access plans to see which practice products and platforms are included. The ExamsDigest-only and All-in-One plans have different scopes, so review the current plan details and access terms before choosing.
No. Databricks lists fundamentals accreditations separately from its certification exams. This page focuses on the certification practice products in the catalog. Match your preparation to the exact credential you intend to earn.

Individual purchases have a 30-day refund window, subject to the published eligibility and usage conditions, including the restriction on completing more than 20% of any included simulator. Membership plans have different conditions. Review the Refund Policy before purchasing.