Databricks Certified Machine Learning Associate
Exam General Information
Review the exam status, fees, eligibility, structure, delivery, scheduling, venue, retake, and renewal rules before studying.
Official Scope and Verification
This lesson is mapped to the verified Databricks Certified Machine Learning Associate outline. Official sources and public status were rechecked on 2026-07-13. Provider pages remain authoritative for late-breaking blueprint, availability, scheduling, price, language, delivery, and retake changes.
Current Databricks proctored certification with published domain percentages.
Official Objective Map
| Domain or objective area | Published weight | Key objective groups | Official source |
|---|---|---|---|
| Databricks Machine Learning | 38% | Identify the best practices of an MLOps strategy; Identify the advantages of using ML runtimes; Identify how AutoML facilitates model and feature selection; Identify the advantages AutoML brings to the model development process; Identify benefits of account-level Unity Catalog feature store tables over workspace-level tables; Create a feature store table in Unity Catalog; Write data to a feature store table; Train a model with features from a feature store table; Score a model using features from a feature store table; Describe the differences between online and offline feature tables; Identify the best run using the MLflow Client API; Manually log metrics, artifacts, and models in an MLflow Run; Identify information available in the MLflow UI; Register a model using the MLflow Client API in the Unity Catalog registry; Identify benefits of registering models in the Unity Catalog registry over the workspace registry; Identify when promoting code is preferred over promoting models and vice versa; Set or remove a tag for a model; Promote a challenger model to a champion model using aliases | Databricks official Machine Learning Associate exam guide PDF |
| Data Processing | 19% | Compute summary statistics on a Spark DataFrame using .summary() or dbutils data summaries; Remove outliers from a Spark DataFrame based on standard deviation or IQR; Create visualizations for categorical or continuous features; Compare two categorical or two continuous features using the appropriate method; Compare and contrast imputing missing values with the mean, median, or mode; Impute missing values with the mode, mean, or median value; Use one-hot encoding for categorical features; Identify model types or data sets where one-hot encoding is or is not appropriate; Identify scenarios where log scale transformation is appropriate | Databricks official Machine Learning Associate exam guide PDF |
| Model Development | 31% | Use ML foundations to select the appropriate algorithm for a given model scenario; Identify methods to mitigate data imbalance in training data; Compare estimators and transformers; Develop a training pipeline; Use Hyperopt fmin to tune model hyperparameters; Perform random, grid, or Bayesian search for hyperparameter tuning; Parallelize single node models for hyperparameter tuning; Describe benefits and downsides of cross-validation over a train-validation split; Perform cross-validation as part of model fitting; Identify the number of models trained in grid-search and cross-validation; Use common classification metrics such as F1, log loss, and ROC/AUC; Use common regression metrics such as RMSE, MAE, and R-squared; Choose the most appropriate metric for a scenario objective; Exponentiate log-transformed variables before calculating metrics or interpreting predictions; Assess the impact of model complexity and the bias-variance tradeoff on model performance | Databricks official Machine Learning Associate exam guide PDF |
| Model Deployment | 12% | Identify differences and advantages of batch, realtime, and streaming model serving approaches; Deploy a custom model to a model endpoint; Use pandas to perform batch inference; Identify how streaming inference is performed with Delta Live Tables; Deploy and query a model for realtime inference; Split data between endpoints for realtime inference | Databricks official Machine Learning Associate exam guide PDF |
Authoritative Sources for This Scope
- Databricks official Machine Learning Associate exam guide PDF - Official source; accessed 2026-07-13.
Exam General Information At A Glance
This is the administrative starting point for Databricks Certified Machine Learning Associate. The information was reviewed on July 14, 2026. Providers and testing vendors can change prices, appointment inventory, delivery methods, languages, identity rules, and retake terms, so follow the official links below and recheck the checkout screen before paying.
| Planning item | Current guidance |
|---|---|
| Credential and current status | Current in the local verified catalog. |
| Exam or assessment code | No separate public exam code is stated in the local verified title; register by the full credential name. |
| Who should take it | Candidates whose role and experience match the official exam page and objective guide. |
| Requirements and prerequisites | No formal prerequisite; related training and roughly six months of hands-on Databricks experience are recommended. |
| When to take it | Schedule while the exam is active. Appointment dates and seats depend on country, language, delivery vendor, and test-center or online-proctor availability. |
| Registration and scheduling | Register from Databricks Academy / the official certification page through the current authorized exam platform. |
| Where to take it / exam venues | Online-proctored delivery is standard for Databricks certification exams; beta or conference exams may use a stated onsite window. |
| Fee and payment | USD 200 for the public Databricks certification exams covered here, before applicable tax; accreditation assessments follow their learning-portal terms. |
| Duration and exam structure | 45 scored multiple-choice or multiple-selection questions in 90 minutes; unscored items may also appear and are not identified. |
| Scoring, results, and passing rule | The provider does not publish a fixed raw passing percentage for this track in the public materials reviewed. Follow the current pass/fail or scaled-score rule in the candidate guide and score report. |
| Languages and accommodations | Choose only a language shown in the registration flow. Request accommodations through the provider or testing vendor before booking; approval may take time. |
| Identification, check-in, and equipment | Use an accepted, unexpired government ID whose name matches the registration profile. For online delivery, run the system test and prepare a private, compliant room; test centers supply their own equipment. |
| Cancellation and rescheduling | Check the appointment confirmation for the current cancellation, rescheduling, late-change, refund, and no-show deadline. Vendor and region rules can differ. |
| Retake rule and repeat fees | A failed Databricks certification exam can be repurchased after the 14-day waiting period. Use the candidate agreement for the current annual attempt limit; accreditation assessments use their own portal rules. |
| Validity, expiration, and renewal | Databricks certifications are valid for two years and require passing the then-current full exam to recertify. |
What To Verify Before You Pay Or Enroll
- The credential is still available in your country, and the exam code matches this course.
- The final checkout amount, currency, tax, voucher, membership discount, bundle, and refund terms are acceptable.
- Your chosen online or test-center appointment is available on the date you need; a provider offering an exam does not guarantee a seat at every venue.
- Your legal name matches the accepted identification, and any accommodation request has been approved before scheduling.
- You understand the exact attempt, waiting-period, cancellation, rescheduling, no-show, expiration, and renewal rules shown by the provider.
Official Registration And Policy Sources
- Databricks Certification and Badging - Official Databricks certification and accreditation catalog.
- Databricks Academy - Official learning platform entry point.
Start here if you are learning on your own. This module turns Databricks Certified Machine Learning Associate into a concrete study route: what the credential is for, what you need before you begin, where to verify cost and retake rules, and how to practice without getting lost in product trivia or stale third-party claims.
Administrative facts were reviewed for this course build on July 14, 2026. Fees, retake rules, testing vendors, beta status, language availability, delivery format, and renewal rules can change, so use the official Databricks links below as the final source before you pay or schedule.
What This Credential Measures
Databricks Certified Machine Learning Associate belongs in the lakehouse AI, ML engineering, generative AI, context engineering, and data governance area. In practical terms, it asks whether you can recognize the right AI concept, choose an appropriate provider capability or governance action, and explain why a tempting alternative does not fit the scenario.
Local catalog summary: Current verified credential track. Current Databricks proctored certification with published domain percentages.
- Best audience: data and ML practitioners who need to connect data preparation, modeling, evaluation, deployment, and monitoring.
- Exam mindset: look for role or learner goal, data source, risk level, required effort, and outcome words before choosing an answer or completing a task.
- Not enough by itself: memorizing product names. You need to know when the product, workflow, or control is appropriate.
Track-Specific Study Focus
- Read the exact credential title first. Many AI credentials are role-based, so the same AI concept can be tested differently for an engineer, architect, auditor, business leader, teacher, or administrator.
- Translate every objective into a real scenario with a user, data source, risk constraint, and expected output.
- Separate durable AI principles from provider product names so you can still reason when a product name changes.
- Connect supervised learning, unsupervised learning, feature handling, model selection, validation, deployment, and drift monitoring.
- Treat data quality, leakage, label definition, and evaluation design as first-class exam topics.
- Know when an experiment, notebook, pipeline, model registry, endpoint, or monitoring control is the next logical step.
What You Need To Get Started
- Official preparation source. Download or bookmark the official exam guide, course page, exam topics, or credential outline before using third-party notes.
- AI vocabulary. Be comfortable with AI, ML, GenAI, model, prompt, token, embedding, inference, grounding, RAG, fine-tuning, hallucination, bias, evaluation, and human oversight.
- Credential vocabulary. Build a short glossary for the Databricks product names, roles, concepts, policies, and artifacts that appear in the credential. For each one, write what problem it solves and when it is not enough.
- Security basics. Know identity, least privilege, privacy, data classification, and why AI prompts and outputs need appropriate protection for the people and setting involved.
- Practice environment. Use official labs, free tiers, sandboxes, demos, or documentation walkthroughs only where they help you understand a scenario. Do not spend money on cloud resources without a budget limit.
- Error notebook. Track every missed practice item by writing the requirement word that changed the answer, not just the correct option.
Cost, Retake Rules, And Registration Checks
Do not assume that the fee or retake rule you saw in an old blog post still applies. Before paying for Databricks Certified Machine Learning Associate, open the official Databricks credential page and confirm the current checkout amount, taxes, vouchers, attempt rules, waiting period after a failed attempt, cancellation or reschedule window, online-proctor rules, ID requirements, expiration period, and renewal process. Where a public official page does not list a fixed price, treat the testing vendor checkout or provider portal as the authoritative price source.
| Question to verify | Where to check | Why it matters |
|---|---|---|
| How much does it cost? | Official credential page or testing-vendor checkout. | The public price may vary by country, membership, voucher, bundle, tax, or beta program. |
| What happens if I fail? | Retake policy, exam terms, testing-vendor rules, or credential FAQ. | Some programs require a waiting period, charge again, limit attempts, or treat beta exams differently. |
| Can I reschedule or cancel? | Scheduling confirmation, testing-vendor policy, or provider exam policy. | Missing the allowed window can forfeit the fee even when you were otherwise ready. |
| What exam format and identification rules apply? | Official exam page and appointment confirmation. | Delivery, allowed materials, check-in, and identification requirements are provider-specific. |
| How long is it valid? | Certification renewal or continuing education page. | You may need renewal assessments, continuing education, membership, or a recertification exam. |
How To Study The Official Objectives
- Convert each objective into a question. If the guide says "identify", ask: "Given this scenario, what should I identify?"
- Build one example per objective. Use a simple workplace case, not an abstract definition.
- Separate concept from tool. First decide whether the question is about data, model behavior, governance, implementation, or operations. Then choose the tool.
- Practice adjacent choices together. Mix similar options so you can explain why the second-best answer is not best.
- Review weak topics twice. Re-read the official page, write a one-paragraph explanation, and answer a mixed quiz before marking the topic complete.
Example: Reading A Scenario
Scenario: A model performs well in a notebook but poorly after deployment. The first review should compare data, features, environment, model version, and monitoring evidence.
Reasoning: Identify the role, business outcome, data source, operational constraint, and risk level. Then apply this lens: Connect the requirement to data preparation, governed features, MLflow tracking, model serving, vector retrieval, or agent evaluation.
Common trap: Jumping to a new algorithm when the scenario is really about data leakage, evaluation design, or production monitoring.
Self-Study Cadence
- Pass 1 - orient. Read the official page, this general-information module, and the five other modules in this six-module course. Write the top objectives from memory.
- Pass 2 - map. Create a two-column map: scenario cue on the left, correct concept or provider capability on the right.
- Pass 3 - drill. Use flashcards and quizzes. Do not mark an answer "known" until you can reject at least two distractors.
- Pass 4 - simulate. Do timed mixed sets. Practice flagging uncertain questions, making the best available choice, and moving on.
- Pass 5 - remediate. Spend the last review cycle only on missed topics, policy details, and confusing service pairs.
Official Links
- Databricks Certification and Badging - Official Databricks certification and accreditation catalog.
- Databricks Academy - Official learning platform entry point.