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100+ Free Dataiku ML Practitioner Practice Questions

Dataiku ML Practitioner Certification (Dataiku Academy) practice questions are available now; exam metadata is being verified.

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What is the role of a 'session' in the Dataiku visual ML Result tab?

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Key Facts: Dataiku ML Practitioner Exam

Free

Certification Cost

Dataiku Academy

120 min

Assessment Duration

Dataiku Academy

Core Designer

Required Prerequisite

Dataiku Academy certification path

3 mandatory

Learning Path Courses

Dataiku Academy (ML Basics, Scoring Basics, Interactive Visual Statistics)

MCQ + hands-on

Assessment Format

Dataiku Academy

Not published

Passing Score

Dataiku (no public threshold)

The Dataiku ML Practitioner certification (Dataiku Academy) is a free, 120-minute assessment combining a multiple-choice exam with a hands-on project. It requires the upstream Core Designer certification and covers visual machine learning and AutoML, feature handling and preprocessing, model evaluation and scoring, data exploration with interactive statistics, model deployment basics, and core Designer concepts. Dataiku does not publish the question count or a fixed passing score.

Sample Dataiku ML Practitioner Practice Questions

Try these sample questions to test your Dataiku ML Practitioner exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1In Dataiku DSS, where do you build and iterate on machine learning models before they affect the project's Flow?
A.The Lab
B.The Wiki
C.The Dashboard
D.The Catalog
Explanation: Machine learning models are created in the Lab, a workspace for drafting visual analyses, ML tasks, and code notebooks. Any work done in the Lab does not change datasets in the Flow until you explicitly deploy it, which keeps experimentation isolated from the production pipeline.
2Which two kinds of machine learning tasks are the core supervised and unsupervised options offered in a Dataiku visual analysis?
A.Regression and forecasting only
B.Prediction and clustering
C.Scoring and labeling
D.Encoding and rescaling
Explanation: A Dataiku ML Task is either a Prediction task, where you predict a single target variable (supervised learning), or a Clustering task, which groups records without a target (unsupervised learning). Both are created from the Lab as part of a visual analysis.
3A data scientist wants to predict apartment price, a numeric value, from features such as size and location. Which prediction type should they choose in Dataiku?
A.Multi-class classification
B.Two-class classification
C.Regression
D.Clustering
Explanation: Regression is used when the target variable is numeric, such as the price of an apartment. Dataiku automatically suggests the prediction type based on the target column, but it can be set explicitly in the target settings.
4When the target column has exactly two possible values, such as 'churned' and 'retained', which Dataiku prediction type applies?
A.Multi-class classification
B.Time series forecasting
C.Regression
D.Two-class classification
Explanation: Two-class (binary) classification is used when the target can take one of two categories, for example presence or absence of an outcome. Dataiku exposes threshold-dependent metrics and a probability threshold specifically for this prediction type.
5Dataiku documentation recommends NOT using multi-class classification when the target has roughly how many or more distinct classes?
A.About 5 classes
B.About 50 classes
C.About 10 classes
D.About 500 classes
Explanation: Dataiku notes that its visual ML cannot handle a very large number of classes and recommends avoiding multi-class classification beyond about 50 classes. With too many classes, per-class data becomes sparse and model quality degrades.
6Which AutoML prediction style in Dataiku trains a few simple models quickly to give you a fast first result?
A.Interpretable Models
B.High Performance
C.Quick Prototypes
D.Expert mode
Explanation: Quick Prototypes is the AutoML prediction style designed for speed; it trains a small set of models with sensible defaults so you can rapidly assess feasibility. It trades depth of optimization for a quick first iteration.
7A team needs the most accurate possible model and can accept long training times and lower interpretability. Which AutoML prediction style fits best?
A.Quick Prototypes
B.Interpretable Models
C.Trivial identity
D.High Performance
Explanation: The High Performance prediction style selects a variety of tree-based models and performs a very deep hyperparameter optimization search, generally producing the best predictive performance at the expense of interpretability and training time.
8In Dataiku's AutoML engine, what does the engine automatically perform in addition to selecting algorithms?
A.Features handling, scaling, missing-value handling, and hyperparameter optimization
B.Only deployment to a Kubernetes cluster
C.Only writing the project's documentation
D.Only exporting the model to ONNX
Explanation: The DSS automated machine learning engine analyzes the dataset and selects feature handling (including categorical and text processing), scaling, missing-value strategies, and runs hyperparameter optimization in addition to choosing algorithms. This minimizes manual setup while still allowing full customization.
9Which algorithm builds many decision trees on random subsets of data and features, then averages or votes across them?
A.K-Means
B.Logistic Regression
C.Random Forest
D.Ridge Regression
Explanation: A Random Forest is an ensemble of many decision trees, where each tree is grown on a random sample of the training set and considers a random subset of features at each split. Predictions are aggregated by averaging (regression) or voting (classification), which generally yields strong results at the cost of explainability.
10In Dataiku's in-memory Python engine, which gradient boosting algorithm includes an early-stopping mechanism that optimizes the actual number of trees?
A.Naive Bayes
B.Decision Tree
C.K-Nearest Neighbors
D.XGBoost
Explanation: XGBoost in Dataiku uses a built-in early-stopping mechanism, so the exact number of trees is optimized up to the configured maximum based on the cross-validation scheme. This prevents unnecessary trees from inflating training and prediction time.

About the Dataiku ML Practitioner Practice Questions

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