Free AWS-CERTIFIED-AI-PRACTITIONER-AIF-C01 Amazon AWS-CERTIFIED-AI-PRACTITIONER-AIF-C01 Practice Test Question - Page 3

Question 7 (Topic 1)

A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company needs the LLM to produce more consistent responses to the same input prompt. Which adjustment to an inference parameter should the company make to meet these requirements?

Select an option, then click Submit answer.

○ Decrease the temperature value.
○ Increase the temperature value.
○ Decrease the length of output tokens.
○ Increase the maximum generation length.

Reference / correct answer:

Decrease the temperature value.

Most accepted answer: A. Decrease the temperature value.

Community votes: A=4

Selected Answer: A Temperature controls the randomness/creativity of model outputs: - Lower temperature (→ 0) = more deterministic, consistent, and predictable responses - Higher temperature (→ 1) = more random, creative, and varied responses upvoted 1 times

Selected Answer: A A is the correct answer upvoted 1 times

Selected Answer: U A. Diminua o valor da temperatura. ✅ Explicação:O parâmetro temperatura controla o nível de aleatoriedade nas respostas geradas por um modelo de linguagem. Temperatura baixa (ex: 0.2) → respostas mais determinísticas e consistentes. Temperatura alta (ex: 0.8 ou 1.0) → respostas mais variadas e criativas. Aplicação ao caso: Para que o LLM produza respostas mais consistentes ao mesmo prompt (como é desejado na análise de sentimentos, onde a estabilidade na resposta é essencial), a empresa deve diminuir a temperatura. upvoted 1 times

Selected Answer: A A. Decrease the temperature value: The temperature parameter controls the randomness of the model’s output. Lower temperatures make the model more deterministic and lead to more consistent and focused responses, while higher temperatures introduce more randomness and variety. For sentiment analysis, where you want consistent outputs for the same input, decreasing the temperature will help achieve more predictable and reliable results. upvoted 4 times

Selected Answer: A Lowering the temperature value in an LLM controls the randomness of the model's output. A lower temperature (close to 0) makes the model's predictions more deterministic and consistent, leading to similar outputs for identical prompts. This is particularly beneficial in tasks like sentiment analysis, where consistency and reliability in responses are crucial. upvoted 2 times

Question 8 (Topic 1)

Which strategy evaluates the accuracy of a foundation model (FM) that is used in image classification tasks?

Select an option, then click Submit answer.

○ Calculate the total cost of resources used by the model.
○ Measure the model's accuracy against a predefined benchmark dataset.
○ Count the number of layers in the neural network.
○ Assess the color accuracy of images processed by the model.

Reference / correct answer:

Measure the model's accuracy against a predefined benchmark dataset.

Most accepted answer: B. Measure the model's accuracy against a predefined benchmark dataset.

Community votes: B=4

Selected Answer: B Explicação: Em tarefas de classificação de imagens, a precisão (accuracy) é uma métrica comum que compara as previsões do modelo com os rótulos reais de um conjunto de dados de teste conhecido (conjunto de dados de referência). Por que as outras opções estão incorretas: A. Calcule o custo total dos recursos usados pelo modelo: Isso mede eficiência ou custo operacional, não a precisão do modelo. C. Conte o número de camadas na rede neural: Isso fornece informações sobre a complexidade do modelo, não sua precisão ou desempenho real. D. Avalie a precisão de cores das imagens processadas pelo modelo: Irrelevante para tarefas de classificação; a precisão de cores não mede se a classificação foi correta. upvoted 2 times

Selected Answer: B B. Measure the model's accuracy against a predefined benchmark dataset: This is the correct strategy for evaluating the performance of a foundation model (FM) in an image classification task. Accuracy is typically evaluated by comparing the model's predictions to the known labels of a benchmark dataset that is representative of the problem domain. This allows you to quantify how well the model is performing. upvoted 3 times

Selected Answer: B B. Measure the model's accuracy against a predefined benchmark dataset. Reasoning: Accuracy in Image Classification: The standard way to evaluate the accuracy of a foundation model in image classification tasks is to compare the model's predictions against the ground truth labels in a predefined benchmark dataset. This ensures consistency and reliability in performance evaluation. Benchmark Dataset: A benchmark dataset contains labelled images that serve as a standard for evaluating the performance of image classification models. Examples include ImageNet, CIFAR-10, or MNIST, depending on the task and complexity. Evaluation Metrics: Metrics such as accuracy, precision, recall, and F1 score are typically calculated using the predictions and ground truth labels in the benchmark dataset. upvoted 3 times

Selected Answer: B B is good upvoted 1 times

Question 9 (Topic 1)

What are tokens in the context of generative AI models?

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○ Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units.
○ Tokens are the mathematical representations of words or concepts used in generative AI models.
○ Tokens are the pre-trained weights of a generative AI model that are fine-tuned for specific tasks.
○ Tokens are the specific prompts or instructions given to a generative AI model to generate output.

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