Amazon AIF-C01 AWS Certified AI Practitioner Exam Practice Test

Answer : A

The F1 score is a metric used to evaluate the performance of a classification model by considering both precision and recall. Precision measures the accuracy of positive predictions (i.e., the proportion of true positive predictions among all positive predictions made by the model), while recall measures the model's ability to identify all relevant positive instances (i.e., the proportion of true positive predictions among all actual positive instances). The F1 score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. This is particularly useful when dealing with imbalanced datasets or when the cost of false positives and false negatives is significant. Options B, C, and D pertain to other aspects of model performance but are not related to the F1 score.

Answer : C

Amazon SageMaker's DeepAR is a supervised learning algorithm designed for forecasting scalar (one-dimensional) time series data. Time series data consists of sequences of data points indexed in time order, typically with consistent intervals between them. In the context of a retail store aiming to predict product demand, relevant time series data might include historical sales figures, inventory levels, or related metrics recorded over regular time intervals (e.g., daily or weekly). By training the DeepAR model on this historical time series data, the store can generate forecasts for future product demand. This capability is particularly useful for inventory management, staffing, and supply chain optimization. Other data types, such as text, image, or binary data, are not suitable for time series forecasting tasks and would not be appropriate inputs for the DeepAR algorithm.

Answer : A

Fairness refers to the absence of bias in AI models. Using non-representative datasets leads to biased predictions, affecting specific demographics unfairly. Explainability, privacy, and transparency are important but not directly related to this scenario. Reference: AWS Responsible AI Framework.

Answer : D

RAG combines large pre-trained models with retrieval mechanisms to fetch relevant context from a knowledge base. This approach is cost-effective as it eliminates the need for frequent model retraining while ensuring responses are contextually accurate and up to date. Reference: AWS RAG Techniques.

Answer : A

Supervised learning is appropriate when the dataset is labeled. The model uses this data to learn patterns and classify images. Unsupervised learning, reinforcement learning, and active learning are not suitable since they either require unlabeled data or different problem settings. Reference: AWS Machine Learning Best Practices.

Answer : A

The F1 score is the harmonic mean of precision and recall, making it a balanced metric for evaluating model performance when there is an imbalance between false positives and false negatives. Speed, cost, and energy efficiency are unrelated to the F1 score. Reference: AWS Foundation Models Guide.

Answer : C

The problem involves a simple probability calculation that can be handled efficiently by straightforward mathematical rules and computations. Using machine learning techniques would introduce unnecessary complexity and operational overhead.

Option C (Correct): 'Use code that will calculate probability by using simple rules and computations': This is the correct answer because it directly solves the problem with minimal overhead, using basic probability rules.

Option A: 'Use supervised learning to create a regression model' is incorrect as it overcomplicates the solution for a simple probability problem.

Option B: 'Use reinforcement learning to train a model' is incorrect because reinforcement learning is not needed for a simple probability calculation.

Option D: 'Use unsupervised learning to create a model' is incorrect as unsupervised learning is not applicable to this task.

AWS AI Practitioner Reference:

Choosing the Right Solution for AI Tasks: AWS recommends using the simplest and most efficient approach to solve a given problem, avoiding unnecessary machine learning techniques for straightforward tasks.