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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.
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