100+ Free H2O.ai Certified Machine Learning Practice Questions

Prepare for the H2O.ai Certified Machine Learning (H2O.ai University) exam with instant access — no signup required.

✓ No registration✓ No credit card✓ No hidden fees✓ Start practicing immediately

100+ Questions

100% Free

Loading practice questions...

Same family resources

Explore More AI/ML Platform Certifications

Continue into nearby exams from the same family. Each card keeps practice questions, study guides, flashcards, videos, and articles in one place.

2026 Statistics

Key Facts: H2O.ai Certified Machine Learning Exam

Free

Certification Cost on H2O.ai University

H2O.ai University

70%

Passing Score Required

H2O.ai University FAQ

Not published

Question Count and Time Limit

H2O.ai University

MOJO/POJO

Scoring Artifacts Tested

H2O.ai documentation

AUC default

AutoML Binary Leaderboard Metric

H2O AutoML documentation

200,000+

Data Scientists in the H2O.ai Community

H2O.ai

H2O.ai Certified Machine Learning is a free credential from H2O.ai University earned by scoring 70% or higher on course quizzes. It validates skills across H2O-3 AutoML and Stacked Ensembles, data preparation and feature engineering, model training, tuning, and MLI explainability (SHAP, partial dependence, k-LIME), MOJO/POJO deployment and scoring, Driverless AI experiments and recipes, and core supervised and unsupervised ML concepts. The question count and time limit are not published; quizzes are self-paced multiple-choice.

Sample H2O.ai Certified Machine Learning Practice Questions

Try these sample questions to test your H2O.ai Certified Machine Learning exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1In H2O-3, what is the primary purpose of calling h2o.init() at the start of a session?

A.It loads a CSV file directly into a pandas DataFrame

B.It trains a default AutoML model on the most recent dataset

C.It starts or connects the client to an H2O cluster (JVM) that performs the distributed computation

D.It exports the current model as a MOJO scoring artifact

Explanation: H2O-3 runs its algorithms inside a Java cluster. h2o.init() launches a local single-node cluster or connects to an existing/remote cluster, and the Python or R client then sends commands to that JVM. All data frames and models live in cluster memory, not in the client process.

2When H2OAutoML finishes a binary classification run, which type of model is typically ranked at the top of the leaderboard?

A.A single Generalized Linear Model (GLM)

B.A Stacked Ensemble that combines the individual base models

C.The Deep Neural Net, because it always generalizes best

D.The first XGBoost model trained, by default

Explanation: AutoML trains many base learners (XGBoost, GBM, DRF, GLM, deep learning) and then builds Stacked Ensembles on top of them. In most cases the 'All Models' or 'Best of Family' Stacked Ensemble is the top-performing model on the leaderboard because stacking combines the strengths of the diverse base models.

3By default, how does H2OAutoML rank models on the leaderboard for a binary classification problem?

A.By RMSE, lowest first

B.By mean per-class error, lowest first

C.By the model's training time, fastest first

D.By AUC, highest first

Explanation: The default leaderboard sort metric depends on the problem type: AUC for binary classification, mean per-class error for multiclass, and RMSE for regression. For binomial problems AUC is used and higher AUC ranks higher on the leaderboard.

4In H2OAutoML, which pair of parameters acts as the main stopping criteria, where at least one must effectively be set to bound the run?

A.nfolds and seed

B.max_models and max_runtime_secs

C.balance_classes and class_sampling_factors

D.stopping_rounds and learn_rate

Explanation: AutoML uses two main stopping strategies: max_models limits the number of models trained, and max_runtime_secs limits the wall-clock time. When both are set, AutoML stops as soon as either limit is reached. Bounding the run this way controls how exhaustively the search proceeds.

5What happens to Stacked Ensembles in AutoML if you set nfolds = 0?

A.AutoML trains more stacked ensembles than usual

B.Cross-validation is disabled, which also disables Stacked Ensembles unless a blending frame is used

C.AutoML automatically switches to leave-one-out cross-validation

D.Stacked ensembles are still built using the validation_frame's labels directly

Explanation: Stacked Ensembles need cross-validated predictions (a level-one frame) to train the metalearner. Setting nfolds = 0 disables cross-validation, which by default disables Stacked Ensembles and lowers the best achievable performance. Providing a blending_frame is the alternative way to still build ensembles via holdout stacking.

6Which two Stacked Ensembles does H2O AutoML build by default during a run?

A.A bagged ensemble and a boosted ensemble

B.A linear ensemble and a tree-only ensemble

C.An 'All Models' ensemble and a 'Best of Family' ensemble

D.A training ensemble and a validation ensemble

Explanation: AutoML constructs two ensembles: 'All Models', which stacks every base model trained in the run and is usually the most accurate, and 'Best of Family', a lighter ensemble that stacks only the single best model from each algorithm family (best GBM, best XGBoost, best DRF, etc.). Best of Family is often preferred for production because it is smaller and faster to score.

7In H2O-3, what is the metalearner in a Stacked Ensemble responsible for?

A.Cleaning and imputing the raw training data before modeling

B.Learning the optimal way to combine the predictions of the base models

C.Selecting which rows go into the training versus test split

D.Generating MOJO artifacts for each base model

Explanation: Stacking works in two levels. Base models make predictions, and those cross-validated predictions form a level-one frame. The metalearner (a GLM by default) is trained on that frame to find the best combination of base-model outputs, producing a single ensemble prediction that typically outperforms any individual base model.

8Which statement best describes an H2OFrame in H2O-3?

A.A client-side pandas object stored in the Python process memory

B.A serialized MOJO file used only for scoring

C.A distributed, columnar, in-memory data structure stored in the H2O cluster

D.A configuration object that holds AutoML hyperparameters

Explanation: An H2OFrame is H2O-3's core data abstraction: a distributed, column-compressed table that lives in the cluster's memory and can scale across multiple nodes. Operations on an H2OFrame are executed in the JVM cluster, so even very large datasets can be processed in parallel.

9Which web-based GUI is built into H2O-3 for running algorithms and inspecting models without writing code?

A.H2O Wave

B.Hydrogen Torch

C.Driverless AI

D.H2O Flow

Explanation: H2O Flow is the notebook-style web UI bundled with H2O-3, accessible in a browser once the cluster starts. It lets users import data, build models, and view results interactively using executable cells, all backed by the same cluster as the R and Python clients.

10Which list of supervised algorithms is correctly available natively in H2O-3?

A.Prophet, ARIMA, and LSTM only

B.Only XGBoost and a single fixed neural network

C.GLM, GBM, DRF, XGBoost, Deep Learning, and Stacked Ensembles

D.Logistic regression implemented exclusively in Python with scikit-learn

Explanation: H2O-3 ships a broad set of distributed supervised algorithms, including Generalized Linear Models (GLM), Gradient Boosting Machines (GBM), Distributed Random Forest (DRF), XGBoost, Deep Learning (multilayer neural nets), and Stacked Ensembles. AutoML draws from these same algorithms when it searches for the best model.

About the H2O.ai Certified Machine Learning Exam

H2O.ai Certified Machine Learning credentials are earned through H2O.ai University, which integrates video lessons and quizzes directly on the H2O.ai website. Candidates demonstrate practical command of the H2O.ai stack, including the open-source H2O-3 platform with H2OAutoML, the leaderboard, and Stacked Ensembles; H2O Driverless AI experiments with automatic feature engineering and custom recipes; Machine Learning Interpretability (MLI) techniques such as SHAP, partial dependence, variable importance, and k-LIME; and productionization with MOJO and POJO scoring pipelines. The assessment is a multiple-choice quiz, and a score of 70% or higher earns the certificate and badge. Certification on the H2O.ai website is free, with additional paid options on Udemy and Coursera.

Assessment

Question count not published by the exam provider

Time Limit

Not published (self-paced quizzes)

Passing Score

70% or higher

Exam Fee

Free (H2O.ai University)

H2O.ai Certified Machine Learning Exam Content Outline

20-25%

H2O-3 platform fundamentals and AutoML

Initialize an H2O cluster with h2o.init(), work with distributed H2OFrames and the H2O Flow GUI, and run H2OAutoML. Read the leaderboard (AUC for binary, RMSE for regression, mean per-class error for multiclass), set stopping criteria with max_models and max_runtime_secs, and understand the All Models and Best of Family Stacked Ensembles and their GLM metalearner.

12-18%

Data preparation and feature engineering

Import data with h2o.import_file, split frames with split_frame, and handle categorical columns as enum factors via asfactor(). Rely on native missing-value handling in tree models, impute when needed, scale features for GLM and Deep Learning, and apply target encoding with holdout strategies to avoid leakage.

20-25%

Model training, tuning, and interpretation (MLI)

Train GLM (Elastic Net), GBM, DRF, XGBoost, and Deep Learning; tune with H2OGridSearch, cross-validation, early stopping, and monotonic constraints. Interpret models using variable importance, predict_contributions (SHAP), partial dependence and ICE plots, k-LIME and LOCO, and the explain() suite to build trust and detect leakage.

14-18%

Model deployment and scoring (MOJO/POJO)

Export models with download_mojo(), score standalone with the h2o-genmodel runtime without a running cluster, and choose MOJO over POJO for large models and version independence. Deploy and monitor via H2O MLOps, watch for data drift, and request contributions or leafNodeAssignments at scoring time.

14-18%

Driverless AI concepts

Run experiments configured by the Accuracy, Time, and Interpretability dials; use automatic feature engineering, auto-visualization, and custom Python recipes for transformers, scorers, and models. Generate MOJO and Python scoring pipelines, review MLI dashboards, and produce AutoDoc and Disparate Impact Analysis for governance.

10-15%

General supervised and unsupervised ML concepts

Distinguish supervised from unsupervised learning (K-Means, PCA, Isolation Forest), manage the bias-variance tradeoff and overfitting, and contrast boosting with bagging. Select appropriate metrics such as AUC, logloss, RMSE, precision, and recall, and recognize why accuracy misleads on imbalanced data.

How to Pass the H2O.ai Certified Machine Learning Exam

What You Need to Know

Passing score: 70% or higher
Assessment: Question count not published by the exam provider
Time limit: Not published (self-paced quizzes)
Exam fee: Free

Keys to Passing

Work through all 100 available questions
Review every answer and explanation
Track weak areas and revisit them
Use our AI tutor for tough concepts

H2O.ai Certified Machine Learning Study Tips from Top Performers

1Run a hands-on H2OAutoML experiment: inspect aml.leaderboard, identify the All Models and Best of Family Stacked Ensembles, and confirm the default sort metric is AUC for binary, RMSE for regression, and mean per-class error for multiclass.

2Practice exporting a trained model with download_mojo() and scoring it using the h2o-genmodel runtime so you understand why a MOJO needs no running cluster, versus a POJO that must be compiled.

3Build intuition for MLI: generate SHAP contributions with predict_contributions(), read partial dependence and ICE plots, and explain how k-LIME surrogate GLMs and LOCO produce reason codes.

4Know the AutoML control parameters cold: max_models and max_runtime_secs (stopping), nfolds and keep_cross_validation_predictions (enable Stacked Ensembles), exclude_algos/include_algos, balance_classes, and seed for reproducibility.

5For Driverless AI, memorize what the Accuracy, Time, and Interpretability dials trade off, what custom recipes add (transformers, scorers, models), and that experiments output MOJO and Python scoring pipelines plus AutoDoc.

6Drill core ML fundamentals: supervised versus unsupervised, overfitting and the bias-variance tradeoff, boosting versus bagging, and when AUC, logloss, RMSE, precision, or recall is the right metric, especially on imbalanced data.

Frequently Asked Questions

How much does H2O.ai certification cost and what score is needed to pass?

You can learn and get certified for free directly on the H2O.ai University website, where videos and quizzes are integrated into each course. A score of 70% or higher is required to pass and earn the certificate and badge.

How many questions are on the H2O.ai certification quiz and how long is it?

H2O.ai University does not publish a fixed question count or time limit. Certification is delivered as a self-paced multiple-choice quiz built into each course, and you must reach 70% or higher to pass.

What topics does the H2O.ai certification cover?

It covers the H2O-3 platform and AutoML (leaderboard, Stacked Ensembles), data preparation and feature engineering, model training, tuning, and MLI explainability such as SHAP and partial dependence, MOJO/POJO deployment and scoring, Driverless AI experiments and recipes, and core supervised and unsupervised ML concepts.

Do I need to know Python or R to get H2O.ai certified?

There are no formal prerequisites, but foundational machine learning knowledge and basic Python or R help, since H2O-3 exposes Python and R APIs. Driverless AI also offers a point-and-click UI, so non-coders can follow much of the workflow.

What is the difference between H2O-3 and Driverless AI on the exam?

H2O-3 is the open-source distributed platform with algorithms like GLM, GBM, DRF, XGBoost, and H2OAutoML. Driverless AI is the commercial AutoML product that adds automatic feature engineering, custom recipes, MLI dashboards, and MOJO/Python scoring pipelines for enterprise use.

What is a MOJO and why does it matter for the exam?

A MOJO (Model Object, Optimized) is a low-latency, self-contained scoring artifact that runs in any Java environment using the h2o-genmodel runtime, without a live cluster. H2O.ai recommends MOJOs over POJOs for production because they are smaller, faster, and version-independent.