100+ Free Dataiku ML Practitioner Practice Questions
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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?
2Which two kinds of machine learning tasks are the core supervised and unsupervised options offered in a Dataiku 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?
4When the target column has exactly two possible values, such as 'churned' and 'retained', which Dataiku prediction type applies?
5Dataiku documentation recommends NOT using multi-class classification when the target has roughly how many or more distinct classes?
6Which AutoML prediction style in Dataiku trains a few simple models quickly to give you a fast first result?
7A team needs the most accurate possible model and can accept long training times and lower interpretability. Which AutoML prediction style fits best?
8In Dataiku's AutoML engine, what does the engine automatically perform in addition to selecting algorithms?
9Which algorithm builds many decision trees on random subsets of data and features, then averages or votes across them?
10In Dataiku's in-memory Python engine, which gradient boosting algorithm includes an early-stopping mechanism that optimizes the actual number of trees?
About the Dataiku ML Practitioner Exam
The Dataiku ML Practitioner certification validates the ability to create, evaluate, and tune machine learning models using Dataiku's visual ML tool, deploy a model from the Lab to the Flow, and apply it to score new data. It covers visual machine learning and AutoML (prediction versus clustering, model design, algorithms, hyperparameter tuning), feature handling and preprocessing (rescaling, encoding, missing-value imputation), model evaluation (confusion matrix, precision/recall, ROC/AUC, regression metrics), deployment basics (Saved Models, scoring recipe, API endpoints), and interactive statistics for exploratory data analysis. Candidates must first hold the Dataiku Core Designer certification, which establishes core concepts such as datasets, the Flow, and visual recipes. The free assessment is delivered online through the Dataiku Academy and includes both a multiple-choice exam and a hands-on project.
Assessment
Question count not published by the exam provider
Time Limit
120 minutes
Passing Score
Not publicly published
Exam Fee
Free (Dataiku (Dataiku Academy))
Dataiku ML Practitioner Exam Content Outline
Visual machine learning and AutoML
Build prediction and clustering ML tasks in the Lab, choose the prediction type (regression, two-class, multi-class), pick AutoML styles such as Quick Prototypes, Interpretable Models, and High Performance, select algorithms including Random Forest, XGBoost, and logistic regression, and run hyperparameter optimization with cross-validation while keeping the test set pristine.
Feature handling and preprocessing
Configure rescaling (Standard and Min-max), categorical encoding (dummy/one-hot, target/impact, frequency, ordinal), missing-value strategies (mean, median, constant, drop rows, or a 'missing' category), datetime cyclical encoding, text handling such as TF-IDF, and semi-automatic feature generation.
Model evaluation and scoring
Read confusion matrices and compute precision, recall, F1, accuracy, and threshold-independent ROC/AUC for classification, plus RMSE and R-squared for regression; compare models on the same test set; diagnose overfitting; and interpret variable importance, partial dependence, and subpopulation analysis.
Data exploration and interactive statistics
Use Statistics worksheets and cards for univariate, bivariate, and multivariate analysis, including histograms, box plots, scatter and mosaic plots, correlation matrices, PCA, distribution fitting, and hypothesis tests such as chi-square and Shapiro-Wilk at a default alpha of 0.05.
Model deployment basics
Deploy a model from the Lab to the Flow as a Saved Model, score new data with the scoring recipe (predicted class plus probabilities), evaluate with the Evaluate recipe and Model Evaluation Store for drift monitoring, manage model versions, and expose prediction endpoints through the API Designer and API Deployer.
Core Designer concepts (prerequisite)
Apply foundational Dataiku concepts carried over from Core Designer: projects, datasets and connections, the Flow pipeline, visual recipes such as Prepare, building datasets, and the separation between the experimental Lab and the production Flow.
How to Pass the Dataiku ML Practitioner Exam
What You Need to Know
- Passing score: Not publicly published
- Assessment: Question count not published by the exam provider
- Time limit: 120 minutes
- 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
Dataiku ML Practitioner Study Tips from Top Performers
Frequently Asked Questions
What are the key facts about the Dataiku ML Practitioner certification?
It is a free certification from the Dataiku Academy with a 120-minute assessment that combines a multiple-choice exam and a hands-on project. You must first complete the Dataiku Core Designer certification. Dataiku does not publish the exact question count or a fixed passing score.
What does the ML Practitioner exam cover?
It validates building, evaluating, and tuning models with Dataiku's visual ML tool, deploying from the Lab to the Flow, scoring new data, analyzing data with interactive statistics, and applying Responsible AI tools such as subpopulation analysis and individual prediction explanations.
Do I need the Core Designer certification first?
Yes. The Dataiku certification path requires the upstream Core Designer certification before you can take the ML Practitioner assessment, since it establishes core concepts like datasets, the Flow, and visual recipes.
Which courses prepare me for the ML Practitioner certification?
The ML Practitioner learning path centers on three mandatory courses: Machine Learning Basics, Scoring Basics, and Interactive Visual Statistics, with optional advanced courses on images, NLP, and time series.
How much does the Dataiku ML Practitioner certification cost?
Registration and the assessment are free through the Dataiku Academy. Third-party instructor-led prep courses may charge a fee, but the official Dataiku Academy learning path and certification are free.
What is the best way to prepare for the exam?
Get hands-on in the Lab building prediction and clustering models, practice feature handling and AutoML styles, read confusion matrices and ROC/AUC, and deploy a Saved Model with the scoring recipe. Drill interactive statistics and explainability tools until each workflow feels routine.