[NEW] SAS Certified Professional: AI & Machine Learning

Detailed Exam Domain Coverage: SAS Certified Professional: AI & Machine LearningTo earn your SAS Certified Professional credential, you must demonstrate mastery over the entire AI lifecycle within the SAS Viya environment. This course is built around the three core pillars of the official exam:Machine Learning (40%): Mastering the rigors of data preparation, feature engineering, and the deployment of supervised algorithms to production environments. Natural Language Processing & Computer Vision (30%): Deep dives into text preprocessing, sentiment analysis, and the complexities of image feature extraction and object classification.

Forecasting & Optimization (30%): Understanding time series data, developing accurate forecast models, and formulating complex optimization problems for business solutions. Course DescriptionI have meticulously curated this practice test bank to provide you with the most realistic exam simulation possible. With 1,500 original practice questions, I cover every technical corner of the SAS Viya ecosystem, ensuring you aren't caught off guard by the 65-question, 150-minute exam.

In the world of AI and Machine Learning, knowing "how" isn't enough—you need to know "why. " That is why I include a detailed explanation for every single answer choice. Whether it's a question on gradient boosting or object detection, I break down the logic so you can approach the actual certification with the confidence of a seasoned SAS professional.

My goal is to help you pass on your very first attempt by turning complex data science concepts into intuitive knowledge. Sample Practice QuestionsQuestion 1: When preparing data for a supervised machine learning model in SAS Viya, which technique is most effective for handling high-cardinality categorical variables? A.

One-hot encoding for all levels. B. Target encoding or binning based on the response variable.

C. Removing the variable entirely to reduce noise. D.

Using a simple label encoder (1, 2, 3... ). E.

Duplicating the rows to balance the classes. F. Replacing all values with the mean of the dataset.

Correct Answer: BExplanation:B (Correct): Target encoding helps capture the relationship between high-cardinality features and the target without creating thousands of new sparse columns. A (Incorrect): One-hot encoding creates a column for every level, leading to the "curse of dimensionality" when cardinality is high. C (Incorrect): High-cardinality variables often contain significant predictive power; removing them should be a last resort.

D (Incorrect): Simple label encoding implies an ordinal relationship that may not exist, potentially misleading the model. E (Incorrect): This is a technique for class imbalance, not for handling categorical cardinality. F (Incorrect): You cannot calculate a mean for categorical text data.

Question 2: In a Natural Language Processing (NLP) workflow using SAS, what is the primary purpose of "Stemming" during the text preprocessing phase? A. To identify the emotional tone of a sentence.

B. To translate the text into a different language. C.

To reduce words to their root form by removing suffixes. D. To count the total number of unique words in a document.

E. To fix spelling errors automatically. F.

To encrypt the data for secure transmission. Correct Answer: CExplanation:C (Correct): Stemming chops off the ends of words (e. g.

, "running" to "run") to group similar concepts together and reduce feature space. A (Incorrect): This describes Sentiment Analysis, which happens after preprocessing. B (Incorrect): Stemming is a linguistic reduction tool, not a translation tool.

D (Incorrect): This describes token frequency or vocabulary counting. E (Incorrect): Spell-checking is a separate preprocessing step; stemming assumes the word is spelled correctly. F (Incorrect): Stemming is for analysis, not security or encryption.

Question 3: A data scientist is building a forecasting model in SAS Viya. If the time series data exhibits a clear seasonal pattern that increases in magnitude as the trend increases, which model type is most appropriate? A.

Simple Linear Regression. B. Additive Seasonal Model.

C. Multiplicative Seasonal Model. D.

Moving Average with a window of 2. E. Random Walk without drift.

F. K-Means Clustering. Correct Answer: CExplanation:C (Correct): Multiplicative models are used when the seasonal variation is proportional to the level (trend) of the series.

B (Incorrect): Additive models assume the seasonal component is constant regardless of the trend. A (Incorrect): Linear regression does not inherently capture seasonality without significant feature engineering. D (Incorrect): A simple moving average is a smoothing technique, not a forecasting model for complex seasonality.

E (Incorrect): A random walk assumes no predictable pattern, contradicting the premise of a "clear seasonal pattern. "F (Incorrect): Clustering is an unsupervised technique for grouping, not for time series forecasting. Welcome to the Exams Practice Tests Academy to help you prepare for your SAS Certified Professional: AI & Machine Learning Certification.

You can retake the exams as many times as you want. This is a huge original question bank. You get support from instructors if you have questions.

Each question has a detailed explanation. Mobile-compatible with the Udemy app. 30-days money-back guarantee if you're not satisfied.

I hope that by now you're convinced! And there are a lot more questions inside the course.