SASInstitute A00-406 real exam prep : SAS® Viya® Supervised Machine Learning Pipelines
- Exam Code: A00-406
- Exam Name: SAS® Viya® Supervised Machine Learning Pipelines
- Updated: Sep 23, 2026
- Q&As: 98 Questions and Answers
About SASInstitute A00-406 Real Exam
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SASInstitute A00-406 Exam Overview:
| Certification Vendor: | SAS Institute |
|---|---|
| Exam Name: | SAS® Viya® Supervised Machine Learning Pipelines (SAS Certified Specialist: Machine Learning Using SAS Viya) |
| Exam Number: | A00-406 |
| Certificate Validity Period: | 5 years |
| Available Languages: | English |
| Exam Format: | Short answer, Multiple choice |
| Related Certifications: | SAS Certified Specialist: Forecasting and Optimization Using SAS Viya SAS Certified Associate: Applied Statistics for Machine Learning SAS Certified Specialist: Natural Language Processing and Computer Vision Using SAS Viya |
| Real Exam Qty: | 50-55 |
| Exam Price: | USD 180 |
| Exam Duration: | 90 minutes |
| Passing Score: | 62% |
| Sample Questions: |  |
| Exam Way: | Administered by SAS and Pearson VUE (online or test center) |
| Pre Condition: | Familiarity with SAS Visual Data Mining and Machine Learning software; basic understanding of statistics recommended |
| Official Syllabus URL: | https://www.sas.com/en_gb/certification/credentials/advanced-analytics/machine-learning-using-sas-viya.html |
SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Model Assessment and Deployment | 24-30% | - Deploying models into production - Assessing model performance (metrics, ROC curves, confusion matrices) |
| Data Sources | 30-36% | - Exploring and modifying data - Importing and preparing data - Dimensionality reduction and feature engineering |
| Building Models | 40-46% | - Model comparison and selection - Supervised model creation (decision trees, ensembles, SVM, neural networks) |
A00-406 Exam Questions Answered: Details and Services
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Currently, the A00-406 exam requires a passing score of 62% and carries a registration fee of USD 180. SASInstitute can revise either figure, so confirm both on the official site before you schedule.
Because every step is built to save time. The A00-406 bank is carefully made — expert-verified answers across the SASInstitute SAS® Viya® Supervised Machine Learning Pipelines objectives, organized for efficient study rather than endless reading. Delivery is instant upon payment, updates are free for 365 days and arrive by email automatically, and our customer service team is online 24 hours to give fast, precise replies whenever a question about the materials comes up. Efficiency is the design goal, from first click to exam day.
The SASInstitute SAS® Viya® Supervised Machine Learning Pipelines blueprint is structured around these main domains:
- Model Assessment and Deployment (24-30%)
- Data Sources (30-36%)
- Building Models (40-46%)
Per the latest exam information, the A00-406 exam includes 50-55 questions and allows 90 minutes minutes. Rehearsing under the same limit at home removes one more unknown from exam day.
Upon successful payment, our system automatically emails the product to your mailbox — typically within about a minute — with an instant download link on screen, so you can start studying right away. If nothing arrives within two hours, check your spam folder and contact our 24-hour support team. Installations are unlimited, and your purchase includes 365 days of free updates delivered by email, plus a 50% renewal discount afterward.
SASInstitute lists these prerequisites for the SASInstitute SAS® Viya® Supervised Machine Learning Pipelines: Familiarity with SAS Visual Data Mining and Machine Learning software; basic understanding of statistics recommended.
Confirm the current requirements on the official certification page before registering.
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
Which feature extraction method can take both interval variables and class variables as inputs?
- A. Autoencoder
- B. Principal component analysis
- C. Robust PCA
- D. Singular value decomposition
Correct Answer: A 🗳️
Which of the following is a common source for external data in the context of business analytics?
- A. Company financial reports
- B. Intranet databases
- C. CRM data
- D. Employee records
Correct Answer: A 🗳️
What is a data lake architecture designed to store primarily?
- A. Data from a single source or department
- B. Only unstructured data in raw form
- C. All types of data, including structured and unstructured data
- D. Highly structured data in tabular format
Correct Answer: C 🗳️
What is overfitting in machine learning, and how can it be addressed in a pipeline?
- A. Overfitting is not a concern in machine learning pipelines.
- B. Overfitting occurs when the model is too complex and overperforms.
- C. Overfitting occurs when the model fits the training data too closely and may not generalize well. It can be addressed by regularization techniques.
- D. Overfitting occurs when the model is too simple and underperforms.
Correct Answer: C 🗳️
In the context of data integration, what does "data transformation" refer to?
- A. Extracting data from source systems
- B. Converting and reshaping data to match the target schema
- C. Backing up data for disaster recovery
- D. Storing data in a centralized repository
Correct Answer: B 🗳️
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