AIF-C01 Online Practice Questions and Answers

Correct Answer: C

Few-shot learning involves providing a model with a few examples (shots) to learn from. For improving intent detection accuracy in a chatbot using a large language model (LLM), the data should consist of pairs of user messages and their corresponding correct intents.

Correct Answer: D

BERT-based models (Bidirectional Encoder Representations from Transformers) are suitable for tasks that involve understanding the context of words in a sentence and suggesting missing words. These models use bidirectional training, which considers the context from both directions (left and right of the missing word) to predict the appropriate word to fill in the gaps.

Correct Answer: A

Embeddings are numerical representations of objects (such as words, sentences, or documents) that capture the objects' semantic meanings in a form that AI and NLP models can easily understand. These representations help models

improve their understanding of textual information by representing concepts in a continuous vector space. Option A (Correct): "Embeddings":This is the correct term, as embeddings provide a way for models to learn relationships between

different objects in their input space, improving their understanding and processing capabilities. Option B:"Tokens" are pieces of text used in processing, but they do not capture semantic meanings like embeddings do.

Option C:"Models" are the algorithms that use embeddings and other inputs, not the representations themselves.

Option D:"Binaries" refer to data represented in binary form, which is unrelated to the concept of embeddings.

AWS AI Practitioner References:

Understanding Embeddings in AI and NLP:AWS provides resources and tools, like Amazon SageMaker, that utilize embeddings to represent data in formats suitable for machine learning models.

Correct Answer: B

Inference is the correct answer because it is the AI process that occurs when a deployed model analyzes new data (such as an image) to make predictions or identify objects.

Correct Answer: C

To manage the flow of data from Amazon S3 to SageMaker Studio notebooks securely, using a VPC with an S3 endpoint is the best solution.

Correct Answer: A

Amazon Textract is a service that automatically extracts text and data from scanned documents, including PDFs. It is the best choice for converting resumes from PDF format to plain text for further processing.

Correct Answer: A

Providing examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified is the correct prompt engineering strategy for using a large language model (LLM) on Amazon Bedrock for sentiment analysis.

Correct Answer: A

When a model is trained on a dataset containing confidential or sensitive data, the model may inadvertently learn patterns from this data, which could then be reflected in its inference responses. To ensure that a model does not generate responses based on confidential data, the most effective approach is to remove the confidential data from the training dataset and then retrain the model. of Each Option: Option A (Correct):"Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model."This option is correct because it directly addresses the core issue: the model has been trained on confidential data. The only way to ensure that the model does not produce inferences based on this data is to remove the confidential information from the training dataset and then retrain the model from scratch. Simply deleting the model and retraining it ensures that no confidential data is learned or retained by the model. This approach follows the best practices recommended by AWS for handling sensitive data when using machine learning services like Amazon Bedrock. Option B:"Mask the confidential data in the inference responses by using dynamic data masking."This option is incorrect because dynamic data masking is typically used to mask or obfuscate sensitive data in a database. It does not address the core problem of the model being trained on confidential data. Masking data in inference responses does not prevent the model from using confidential data it learned during training. Option C:"Encrypt the confidential data in the inference responses by using Amazon SageMaker."This option is incorrect because encrypting the inference responses does not prevent the model from generating outputs based on confidential data. Encryption only secures the data at rest or in transit but does not affect the model's underlying knowledge or training process. Option D:"Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS)."This option is incorrect as well because encrypting the data within the model does not prevent the model from generating responses based on the confidential data it learned during training. AWS KMS can encrypt data, but it does not modify the learning that the model has already performed. AWS AI Practitioner References: Data Handling Best Practices in AWS Machine Learning:AWS advises practitioners to carefully handle training data, especially when it involves sensitive or confidential information. This includes preprocessing steps like data anonymization or removal of sensitive data before using it to train machine learning models. Amazon Bedrock and Model Training Security:Amazon Bedrock provides foundational models and customization capabilities, but any training involving sensitive data should follow best practices, such as removing or anonymizing confidential data to prevent unintended data leakage.

Correct Answer: B

Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on Amazon Bedrock, as costs are often based on the number of tokens processed by the model.

Correct Answer: A

Amazon Bedrock offers an on-demand pricing model that provides flexibility without long- term commitments. This model allows companies to pay only for the resources they use, which is ideal for a limited budget and offers flexibility. Option

A (Correct): "On-Demand":This is the correct answer because on-demand pricing allows the company to use Amazon Bedrock without any long-term commitments and to manage costs according to their budget. Option B:"Model

customization" is a feature, not a pricing model. Option C:"Provisioned Throughput" involves reserving capacity ahead of time, which might not offer the desired flexibility and could lead to higher costs if the capacity is not fully used.

Option D:"Spot Instance" is a pricing model for EC2 instances and does not apply to Amazon Bedrock.

AWS AI Practitioner

References:

AWS Pricing Models for Flexibility:On-demand pricing is a key AWS model for services that require flexibility and no long-term commitment, ensuring cost- effectiveness for projects with variable usage patterns.