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SASInstitute A00-406 Exam Overview:
| Certification Vendor: | SAS Institute |
|---|---|
| Exam Name: | SAS® Viya® Supervised Machine Learning Pipelines |
| Exam Number: | A00-406 |
| Available Languages: | English |
| Passing Score: | 62% |
| Exam Format: | Short answer, Multiple choice |
| Real Exam Qty: | 50-55 |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 5 years |
| Exam Price: | $180 USD |
| Recommended Training: | SAS Viya Machine Learning Path SAS Visual Data Mining and Machine Learning |
| Exam Registration: | SAS Certification Registration Pearson VUE Scheduling |
| Sample Questions: | SASInstitute A00-406 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | Basic knowledge of SAS Viya, statistics, and supervised machine learning concepts; experience with Model Studio recommended |
| Official Syllabus URL: | https://www.sas.com/en_us/certification/credentials/advanced-analytics/machine-learning-using-sas-viya.html |
SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Assessment and Deployment | 24-30% | - Select champion models - Apply model assessment principles - Deploy models into production - Evaluate and compare model performance |
| Topic 2: Building Models | 40-46% | - Understand supervised machine learning concepts - Build neural networks and SVM models - Integrate custom code - Build decision trees and ensemble models - Use model interpretability tools - Build regression models |
| Topic 3: Data Sources | 30-36% | - Perform variable selection - Create a project in Model Studio - Reduce dimensionality - Modify and prepare data - Explore and understand data |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
Question #1
What is the primary purpose of a supervised machine learning pipeline in SAS Viya?
- A. Data storage and retrieval
- B. Data preprocessing and cleaning
- C. Data visualization
- D. Model training and evaluation
Correct Answer: D 🗳️
Question #2
What is "model deployment" in the context of data science and machine learning?
- A. The process of data cleaning
- B. The process of selecting features
- C. The process of building a model
- D. Making the model available for use in real-world applications
Correct Answer: D 🗳️
Question #3
When building a recommendation system, what does "collaborative filtering" rely on?
- A. Item-based clustering
- B. The popularity of items
- C. The past behavior or preferences of users
- D. The characteristics of the items being recommended
Correct Answer: C 🗳️
Question #4
In a machine learning pipeline, what is the purpose of cross-validation?
- A. To train multiple models on different subsets of the data to assess generalization
- B. To split the dataset into training and testing sets
- C. To evaluate the model's performance on new data
- D. To visualize the data distribution
Correct Answer: A 🗳️
Question #5
When building a recommendation system, which type of filtering is based on the user's behavior and preferences?
- A. Content-based filtering
- B. Matrix factorization
- C. Singular Value Decomposition (SVD)
- D. Collaborative filtering
Correct Answer: D 🗳️
By Webb
