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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 |
| Certificate Validity Period: | 3 years |
| Exam Format: | Multiple Choice, Scenario-based Questions |
| Available Languages: | English |
| Exam Duration: | 110 minutes |
| Exam Price: | USD 180 |
| Related Certifications: | SAS Certified Specialist: Machine Learning Using SAS Viya |
| Recommended Training: | SAS Machine Learning Using SAS Viya Training |
| Exam Registration: | SAS Certification Portal Registration |
| Sample Questions: | SASInstitute A00-406 Sample Questions |
| Exam Way: | Online proctored exam via Pearson VUE or authorized testing centers |
| Pre Condition: | No strict prerequisites; recommended familiarity with basic statistics, machine learning concepts, and SAS Viya platform |
| Official Syllabus URL: | https://www.sas.com/en_us/certification.html |
SASInstitute A00-406 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Preparation for Machine Learning | - Feature engineering
|
| Topic 2: Model Evaluation and Deployment | - Model assessment metrics
|
| Topic 3: Supervised Machine Learning Models | - Model selection techniques
|
| Topic 4: Machine Learning Pipelines in SAS Viya | - Pipeline construction
|
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. Which data source allows for real-time data streaming and processing?
A) Static data files
B) Data warehouses
C) Cloud storage
D) IoT devices
2. Which algorithm is commonly used for binary classification in machine learning pipelines, especially when dealing with imbalanced datasets?
A) Linear Regression
B) Principal Component Analysis (PCA)
C) K-Means Clustering
D) Support Vector Machine (SVM)
3. What does the term "bias" in machine learning refer to?
A) A model's inability to generalize to new data
B) Systematic errors that cause a model to consistently underpredict or overpredict
C) The simplicity of a model
D) The overall accuracy of a model
4. What is metadata in the context of data sources?
A) Data that is in a non-standard, proprietary format
B) Data that is encrypted for security
C) Data that is stored in a physical format
D) Data about data, providing information such as data source, structure, and context
5. What does "feature selection" refer to in the context of model building?
A) The creation of synthetic features from existing data
B) The evaluation of model accuracy
C) The visualization of data distribution
D) The process of choosing the most relevant variables (features) for the model
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: D |
By Sidney
