Career upgrade: Learn practical AI skills for better jobs and higher pay.
Level up
PracticeVideosBlogFlashcardsEspañol
All Practice Exams

100+ Free SAS AI & ML Professional Practice Questions

Pass your SAS Certified Professional: AI & Machine Learning exam on the first try — instant access, no signup required.

✓ No registration✓ No credit card✓ No hidden fees✓ Start practicing immediately
100+ Questions
100% Free
1 / 100
Question 1
Score: 0/0

Which technique reduces dimensionality by projecting variables onto orthogonal axes that maximize variance?

A
B
C
D
to track
Same family resources

Explore More SAS Certifications

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

SAS Certified Associate: Applied Statistics for Machine Learning

Practice Questions

SAS Certified Associate: Modeling Using SAS Visual Statistics

Practice Questions

SAS Certified Associate: Programming Fundamentals Using SAS 9.4

Practice Questions

SAS Certified Data Integration Developer for SAS 9

Practice Questions

SAS Certified Professional Advanced Programming

Practice Questions

SAS Certified Professional: Clinical Trials Programming Using SAS 9.4

Practice Questions
2026 Statistics

Key Facts: SAS AI & ML Professional Exam

3

Specialist exams required

SAS AI & ML Professional credential page

$540

Total exam fees minimum

SAS exam pricing $180 per specialist

90 min

Time per specialist exam

SAS specialist exam content guides

50-55

Questions per specialist exam

SAS specialist exam pages

5 years

Credential validity

SAS certification policy

Viya 4

Platform version

Current SAS specialist content guides

SAS Certified Professional: AI & Machine Learning is a stacked credential earned by passing three Specialist exams on SAS Viya 4 (Machine Learning, NLP and Computer Vision, and Forecasting and Optimization). Each specialist exam is roughly 50-55 questions in 90 minutes for $180 USD. Each specialist credential is valid for 5 years.

Sample SAS AI & ML Professional Practice Questions

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

1Which SAS Viya component provides a drag-and-drop interface for building machine learning pipelines?
A.SAS Studio code editor
B.Model Studio
C.SAS Enterprise Guide
D.SAS Display Manager
Explanation: Model Studio is the visual pipeline builder in SAS Viya for assembling data preparation, modeling, and assessment nodes for machine learning, NLP, computer vision, and forecasting projects.
2Which SAS Viya procedure builds a forest of decision trees with bagging and random feature selection?
A.PROC HPSPLIT
B.PROC FOREST
C.PROC GRADBOOST
D.PROC TREEBOOST
Explanation: PROC FOREST in SAS Viya trains a random forest using bootstrap-sampled trees with random subsets of inputs at each split. PROC GRADBOOST trains gradient boosted trees and PROC HPSPLIT is a single decision tree procedure in SAS 9.4.
3When training PROC GRADBOOST, what does the LEARNINGRATE= option control?
A.The number of trees built
B.The shrinkage applied to each tree's contribution to the ensemble
C.The minimum leaf size in each tree
D.The proportion of variables sampled at each split
Explanation: LEARNINGRATE= (often called shrinkage) scales the contribution of each successive tree. Smaller learning rates typically need more trees but generalize better. NTREES controls tree count and VARS_TO_TRY controls split sampling.
4Which model assessment statistic is most appropriate for ranking customers by predicted probability of response?
A.Mean Squared Error
B.R-squared
C.Cumulative lift
D.Adjusted R-squared
Explanation: Cumulative lift is designed for ranking-based scoring tasks like marketing response or churn, comparing model-targeted decile response rates to the baseline rate. MSE and R-squared apply to interval (regression) targets.
5In a binary classifier ROC curve, what does the area under the curve (AUC) of 0.5 represent?
A.Perfect discrimination
B.Random guessing performance
C.Always predicting the majority class
D.Always predicting the minority class
Explanation: AUC of 0.5 means the classifier ranks positives above negatives no better than random. AUC of 1.0 indicates perfect discrimination. Always predicting one class produces AUC of 0.5 only if both true positive rate and false positive rate move together.
6What is the Kolmogorov-Smirnov (KS) statistic for a binary classifier?
A.The difference between sensitivity and specificity at the optimal cutoff
B.The maximum vertical separation between the cumulative distributions of predicted scores for positives and negatives
C.The harmonic mean of precision and recall
D.The ratio of true positives to predicted positives
Explanation: The KS statistic is the largest gap between the cumulative score distributions of positives versus negatives. A higher KS means the model better separates the two classes across the score range.
7Which feature engineering technique replaces categorical levels with the target's mean for that level?
A.One-hot encoding
B.Target encoding
C.Quantile binning
D.Ordinal encoding
Explanation: Target (mean) encoding replaces a category with the average target value for observations in that category. It is compact for high-cardinality features but can leak target information if not regularized or cross-validated.
8What is the purpose of the AUTOTUNE statement in SAS Viya modeling procedures?
A.Automatically partition data into training, validation, and test sets
B.Automatically search hyperparameter combinations to optimize an objective
C.Automatically engineer interaction features
D.Automatically deploy the trained model to a scoring endpoint
Explanation: AUTOTUNE in PROC FOREST, PROC GRADBOOST, PROC SVMACHINE, PROC NNET, and others searches hyperparameter values using grid, random, Latin hypercube, Bayesian, or genetic algorithm strategies to optimize a chosen objective.
9When using PROC SVMACHINE, what does the C parameter control?
A.The kernel bandwidth
B.The penalty for misclassified observations
C.The number of support vectors
D.The dimensionality of the feature space
Explanation: C is the misclassification penalty (regularization tradeoff). Larger C aims to classify training points correctly at the risk of overfitting; smaller C allows more margin violations for better generalization.
10Which PROC selects variables for an ordinary least squares regression using stepwise, forward, backward, or LASSO methods in SAS Viya?
A.PROC GLMSELECT
B.PROC LOGISTIC
C.PROC REG
D.PROC GLM
Explanation: PROC GLMSELECT performs effect selection for general linear models including LASSO, LAR, stepwise, forward, and backward methods, with cross-validation options for selecting the final model.

About the SAS AI & ML Professional Exam

The SAS Certified Professional: AI & Machine Learning credential is awarded after passing three specialist-level Pearson VUE exams: Machine Learning Using SAS Viya, Natural Language Processing and Computer Vision Using SAS Viya, and Forecasting and Optimization Using SAS Viya. It validates skills with Model Studio pipelines, supervised learning, deep learning for text and images, time series forecasting, and mathematical optimization with PROC OPTMODEL.

Assessment

50-55 questions per specialist exam across 3 specialist exams

Time Limit

90 minutes per specialist exam (270 minutes total)

Passing Score

Pass all 3 specialist exams (62-70% scaled per exam)

Exam Fee

$180 USD (SAS / Pearson VUE)

SAS AI & ML Professional Exam Content Outline

33%

Machine Learning Using SAS Viya

Data sources, feature engineering, building supervised models (PROC FOREST, PROC GRADBOOST, PROC SVMACHINE, PROC NEURAL, PROC GLMSELECT), AUTOTUNE, model assessment (ROC, KS, Gini, lift), and Model Studio pipeline deployment.

33%

Natural Language Processing and Computer Vision

Visual Text Analytics components (parsing, concepts, topics, categories, sentiment), PROC TEXTMINE, term-by-document matrices, TF-IDF, plus image classification with CNNs, object detection, and transfer learning in Model Studio.

34%

Forecasting and Optimization

Visual Forecasting and PROC HPF (ESM, ARIMA, IDM), hierarchical forecasting and reconciliation, post-forecasting overrides, plus linear, nonlinear, and mixed integer programming with PROC OPTMODEL using sets, indices, and decision variables.

How to Pass the SAS AI & ML Professional Exam

What You Need to Know

  • Passing score: Pass all 3 specialist exams (62-70% scaled per exam)
  • Assessment: 50-55 questions per specialist exam across 3 specialist exams
  • Time limit: 90 minutes per specialist exam (270 minutes total)
  • Exam fee: $180 USD

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

SAS AI & ML Professional Study Tips from Top Performers

1Build at least one full Model Studio pipeline that includes data preparation, multiple supervised models, and a model comparison node before sitting the ML exam.
2Memorize the five Visual Text Analytics components in order: parsing, concepts, topics, categories, sentiment.
3Be fluent with PROC OPTMODEL syntax for sets, indexed decision variables, constraints, and the SOLVE statement.
4Practice interpreting ROC curves, KS statistics, Gini, and lift charts side by side; the ML exam tests the relationships between them.
5For forecasting, know when to use ESM, ARIMA, and IDM (intermittent demand) and how hierarchical reconciliation enforces additive consistency.

Frequently Asked Questions

How do I earn the SAS Certified Professional: AI & Machine Learning credential?

You earn it automatically after passing three SAS Viya specialist exams: Machine Learning, Natural Language Processing and Computer Vision, and Forecasting and Optimization. There is no separate combined exam.

How much do all three exams cost in total?

Each specialist exam costs $180 USD, so the minimum total for all three is $540 USD. Academic discounts are available for students and educators through SAS Academic programs.

How many questions and how much time does each specialist exam have?

Each specialist exam typically has 50 to 55 multiple-choice and short-answer questions and a 90-minute time limit, administered through Pearson VUE.

What passing score do I need on each specialist exam?

Passing thresholds vary slightly: roughly 62 percent for Machine Learning, around 70 percent for NLP and Computer Vision, and about 68 percent for Forecasting and Optimization. Confirm the current cut score in each exam's content guide before scheduling.

How long is the credential valid?

Each underlying specialist credential is valid for 5 years from the pass date. Renewing requires retaking the relevant specialist exam against the then-current SAS Viya release.

Which SAS Viya version do the exams target?

The current specialist exams are based on SAS Viya 4 and emphasize Model Studio, Visual Text Analytics, Visual Forecasting, and PROC OPTMODEL workflows.