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SASInstitute A00-406 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Model Evaluation and Deployment | - Model assessment metrics
|
| Topic 2: Supervised Machine Learning Models | - Model selection techniques
|
| Topic 3: Machine Learning Pipelines in SAS Viya | - Pipeline construction
|
| Topic 4: Data Preparation for Machine Learning | - Feature engineering
|
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. Which technique is commonly used for feature scaling or normalization in machine learning pipelines?
A) Decision Trees
B) One-Hot Encoding
C) Principal Component Analysis (PCA)
D) Standardization
2. What is the purpose of data profiling in data source management?
A) To create data visualizations
B) To assess the quality and characteristics of data
C) To execute data queries
D) To optimize data storage
3. What is the main goal of data preprocessing in a machine learning pipeline?
A) To train the model
B) To visualize the data
C) To remove irrelevant features
D) To prepare the data for analysis and modeling
4. When deploying a machine learning model, what is meant by "model latency"?
A) The time it takes to train a model
B) The time it takes for the model to make predictions once deployed
C) The time it takes to build a model
D) The time it takes to create synthetic data
5. What is a data lake?
A) A centralized repository for storing all structured and unstructured data at any scale
B) A specialized database for time-series data
C) A backup system for relational databases
D) A data storage solution designed for high-speed data retrieval
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: A |
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