Describe the AI development and deployment lifecycle: Practice Questions — Essential AI Knowledge (NVIDIA-Certified Associate: AI Infrastructure and Operations)
Practice Questions: AI Development and Deployment Lifecycle Below are multiple-choice questions designed to test your understanding of the AI...
Practice Questions: AI Development and Deployment Lifecycle
Below are multiple-choice questions designed to test your understanding of the AI development and deployment lifecycle, a key component of the NVIDIA-Certified Associate: AI Infrastructure and Operations exam.
Which phase of the AI development lifecycle primarily involves collecting and preparing data for model training?
- A. Model Deployment
- B. Data Engineering
- C. Model Training
- D. Inference
Answer: B. Data Engineering
Explanation: Data engineering focuses on gathering, cleaning, and preparing datasets, which is essential before training AI models.
During which stage is the AI model optimized to improve accuracy and performance?
- A. Model Training
- B. Data Collection
- C. Model Monitoring
- D. Data Annotation
Answer: A. Model Training
Explanation: Model training involves adjusting model parameters using data to improve prediction accuracy.
What is the primary purpose of the deployment phase in the AI lifecycle?
- A. To collect raw data
- B. To run the trained model in a production environment
- C. To label data for supervised learning
- D. To evaluate model performance offline
Answer: B. To run the trained model in a production environment
Explanation: Deployment involves integrating the trained AI model into applications or systems for real-world use.
Which activity is essential after deploying an AI model to ensure it continues to perform well?
- A. Data Annotation
- B. Model Monitoring
- C. Model Training
- D. Data Collection
Answer: B. Model Monitoring
Explanation: Monitoring tracks model accuracy and detects performance degradation, enabling timely updates.
In the AI lifecycle, what is the role of continuous integration and continuous deployment (CI/CD) pipelines?
- A. To automate data labeling
- B. To automate model updates and deployment
- C. To collect training data
- D. To monitor GPU utilization
Answer: B. To automate model updates and deployment
Explanation: CI/CD pipelines streamline the process of integrating new model versions and deploying them efficiently.
Which lifecycle stage involves validating the AI model's performance on unseen data before deployment?
- A. Model Evaluation
- B. Data Collection
- C. Model Training
- D. Model Monitoring
Answer: A. Model Evaluation
Explanation: Model evaluation assesses how well the model generalizes to new data, ensuring reliability before deployment.
Why is data versioning important in the AI development lifecycle?
- A. To track changes in model architecture
- B. To manage different versions of datasets for reproducibility
- C. To monitor GPU performance
- D. To automate deployment
Answer: B. To manage different versions of datasets for reproducibility
Explanation: Data versioning allows teams to track dataset changes and reproduce experiments accurately.
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