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100+ Free DataRobot Certified Professional Practice Questions
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DataRobot flags target leakage using normalized ACE importance thresholds. At which threshold does it automatically remove a high-risk feature?
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2026 Statistics
Key Facts: DataRobot Certified Professional Exam
Free
Exam Fee
DataRobot University
1 year
Credential Validity
DataRobot (Credly badge)
6 hours
Continuing Education Per Year
DataRobot
Credly badge
Awarded on Completion
DataRobot
Multiple choice
Assessment Format
DataRobot University
6 domains
Skill Areas Covered
OpenExamPrep practice mapping
DataRobot's Citizen Data Scientist Professional certification is a free online multiple-choice assessment from DataRobot University that issues a Credly badge and is valid for one year (maintained with 6 hours of CE per year). It validates solving business problems with DataRobot's automated AI across six areas: AutoML workflow (Autopilot, blueprints), data ingestion and feature engineering, leaderboard evaluation, model interpretation (Feature Impact, Prediction Explanations), deployment and MLOps monitoring (data drift, accuracy), and business application. DataRobot does not publish a fixed question count, time limit, or passing-score percentage.
Sample DataRobot Certified Professional Practice Questions
Try these sample questions to test your DataRobot Certified Professional exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.
1In DataRobot, what is the primary purpose of Autopilot?
A.To automatically build, evaluate, and rank multiple machine learning models for a chosen target with minimal manual configuration
B.To clean raw data files by removing duplicate rows before they are uploaded
C.To schedule batch prediction jobs against a deployed model on a recurring basis
D.To generate written compliance documentation for regulators after a model is deployed
Explanation: Autopilot is DataRobot's automated modeling workflow: it selects appropriate blueprints, trains many models on increasing samples of the data, scores them, and ranks them on the Leaderboard so a user can pick the best one. It is the engine that automates feature engineering, algorithm selection, and validation.
2A user wants DataRobot to run an abridged version of Autopilot that builds a representative subset of models quickly at a 64% sample size. Which modeling mode should they choose?
A.Quick (Quick Autopilot) mode
B.Comprehensive mode
C.Manual mode
D.Time-aware mode
Explanation: Quick Autopilot is the default, shortened version of full Autopilot. It runs a representative subset of models at a 64% sample size to deliver a base set of strong models and insights faster than full Autopilot, which runs more models across multiple sample stages.
3In DataRobot, what does a blueprint represent?
A.A graph of the preprocessing steps and the modeling algorithm that transform raw data into predictions
B.A saved snapshot of a deployment's service-health metrics over time
C.The business case document that justifies an AI project's expected ROI
D.A list of users authorized to access a particular project
Explanation: A blueprint is the end-to-end recipe DataRobot uses to build a model. It shows the sequence of preprocessing and feature-engineering tasks (imputation, encoding, text mining, etc.) feeding into a specific algorithm, all displayed as a graph. Models that share the same tasks and feature list within a project get the same blueprint ID.
4After uploading data and selecting a target, which two steps does a DataRobot user typically perform before launching modeling?
A.Manually write the cross-validation code and choose a random seed
B.Deploy a placeholder model and configure data drift tracking
C.Confirm the target feature and review EDA, then start Autopilot
D.Export the Leaderboard to CSV and pick an optimization metric by hand
Explanation: The core DataRobot workflow is: ingest data, select the target feature, review the exploratory data analysis DataRobot generates, then start Autopilot. DataRobot automatically selects an optimization metric and partitioning, so the user mainly confirms the target and launches modeling.
5During full Autopilot, how does DataRobot decide which models advance to larger sample sizes?
A.It trains every model on 100% of the data at once and keeps only the fastest
B.It randomly selects half of the models at each stage regardless of score
C.It builds models on a smaller sample first, then advances the top performers to larger samples in stages
D.It only ever trains models on the holdout partition
Explanation: Full Autopilot uses a staged approach: it first builds many models at a small sample (such as 16%), scores them, advances the top performers to 32%, then takes the best of those to 64%. This runs more model diversity early while spending compute only on the strongest candidates at higher samples.
6A user wants full control to choose specific blueprints from the Repository rather than letting DataRobot decide. Which mode supports this?
A.Quick Autopilot
B.Manual mode
C.Smart downsampling
D.Comprehensive mode
Explanation: Manual mode does not run Autopilot. Instead, after EDA2 completes, DataRobot links to the Repository so the user can hand-pick which blueprints to execute. This gives full control over which algorithms and pipelines are trained.
7What is a blender (ensemble) model in DataRobot?
A.A model that combines the predictions of between two and eight other models to potentially improve accuracy
B.A model that blends training and holdout data into a single partition
C.A preprocessing step that merges categorical columns
D.A deployment that routes traffic between two model versions
Explanation: A blender, or ensemble, combines the predictions of between two and eight models to potentially increase accuracy. Autopilot can automatically create blenders from the top Leaderboard models (for example AVG, GLM, and ENET blenders), and users can also create them manually.
8In the DataRobot Leaderboard, what does the blueprint ID (for example, BP12) identify?
A.The unique deployment endpoint URL for a model
B.The number of features used in the project
C.An instance of a single model type and feature list, shared by models that use the same tasks regardless of sample size
D.The order in which a model finished training
Explanation: A blueprint ID represents a specific combination of model type (including version) and feature list. Models that share those characteristics within the same project carry the same blueprint ID even at different sample sizes. Blender models show the blueprints they combine, such as BP6+17+20.
9Which statement best describes how DataRobot supports a citizen data scientist who lacks deep coding skills?
A.It automates feature engineering, algorithm selection, validation, and ranking so users focus on the business problem
B.It requires every model to be written in Python before it can run
C.It only supports SQL queries and no machine learning
D.It hides all model metrics so users cannot second-guess the platform
Explanation: DataRobot is designed to democratize machine learning by automating the heavy data-science tasks: feature engineering, blueprint and algorithm selection, partitioning, validation, and Leaderboard ranking. This lets domain experts who are not professional coders frame and solve business problems with trusted predictions.
10What does Comprehensive Autopilot mode do that Quick and full Autopilot do not?
A.It skips validation to finish faster
B.It runs all blueprints in the repository, which can be considerably slower
C.It automatically deploys the winning model to production
D.It disables target leakage detection
Explanation: Comprehensive mode runs every blueprint available in the repository for the project, exploring the widest set of algorithms. Because it builds so many models, it can be extremely slow compared with Quick or full Autopilot, which run curated subsets.
About the DataRobot Certified Professional Practice Questions
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