Free AWS-CERTIFIED-AI-PRACTITIONER-AIF-C01 – Amazon AWS-CERTIFIED-AI-PRACTITIONER-AIF-C01 Practice Test Question

Reference / correct answer:

Most accepted answer: B. Accuracy

Community votes: B=9

Accuracy is the most straightforward metric, measuring the proportion of correctly predicted instances out of the total instances. It is suitable when the classes are balanced (but can be misleading for imbalanced datasets). upvoted 9 times

Selected Answer: B Accuracy= Total predictions / Number of correct predictions ​ If you see how many were correctly classified then it is Accuracy (binary & multiclass) If you see regression values then it is R² or RMSE (predicting continuous values) No metric called Learning rate Classification metrics These apply when theproblem is classification (e.g., predicting illness vs healthy, disease types, etc.): Accuracy , AUC, BalancedAccuracy ,F1 score ,F1macro ,Precision , PrecisionMacro ,Recall ,RecallMacro —,LogLoss — Regression metrics For regression tasks (predicting continuous values): MAE — mean absolute error , MSE — mean squared error. Other metrics InferenceLatency (useful for performance diagnostics). upvoted 3 times

Selected Answer: B B is correct. upvoted 1 times

Selected Answer: B Accuracy is a common metric for evaluating image classification , when the accuracy is high, it generally means the model is performing well by making a higher proportion of correct predictions. upvoted 1 times

Selected Answer: B B is the correct answer. upvoted 1 times

Reference / correct answer:

Most accepted answer: A. Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus

Community votes: A=5

Selected Answer: A Human-in-the-loop validation combines the efficiency of machine learning with human expertise to ensure high-quality labeled data. Amazon SageMaker Ground Truth Plus enables you to have human labelers validate and correct model predictions, which reduces errors in annotations and increases the accuracy of the training data. This is particularly useful when you need to generate accurate images or annotations for protective eyewear and want to ensure that the annotations are reliable. upvoted 10 times

Selected Answer: A RLHF combined with Sagemaker Ground Truth Plus for labeling capability to enhance accuray upvoted 1 times

Selected Answer: A Using Amazon SageMaker GroundTruth, human workforce to create label for the datasets which will help to get accuracy for the datasets. upvoted 2 times

Selected Answer: A A: Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus Explanation: Amazon SageMaker Ground Truth Plus is designed for creating high-quality labeled datasets with human-in-the-loop validation to ensure accuracy. This solution helps minimize the risk of incorrect annotations by involving human reviewers to verify and correct the model's predictions. It is particularly useful for scenarios requiring precision, such as generating images with specific requirements like protective eyewear. upvoted 4 times

Selected Answer: A A. Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus upvoted 3 times

Reference / correct answer:

Most accepted answer: C. Scalable index management and nearest neighbor search capability

Community votes: C=7

Selected Answer: C Scalable index management and nearest neighbor search capability: Amazon OpenSearch Service provides built-in support for vector search, which allows for efficient nearest neighbor search (such as k-nearest neighbors or k-NN) in large datasets. This is essential for vector databases, which store high-dimensional data (such as embeddings from machine learning models) and support fast similarity search. The scalable index management ensures that these searches can be performed efficiently even with large datasets. upvoted 11 times

Selected Answer: C C is the correct answer. upvoted 1 times

Selected Answer: C C is the correct answer. upvoted 1 times

Selected Answer: C Scalable index management and k-NN algorithms which support to build and handle the recommendation systems, semantic search and anomalies detection. upvoted 1 times

Selected Answer: C C: Scalable index management and nearest neighbor search capability Explanation: The Amazon OpenSearch Service supports building vector database applications by enabling nearest neighbor search capability. This feature allows the service to efficiently perform similarity searches, which is crucial for applications that rely on vector embeddings (e.g., recommendation systems, image or text similarity searches). Combined with scalable index management, this makes OpenSearch an excellent choice for vector database applications. upvoted 2 times