AIF-C01 Dumps with Practice Exam Questions Answers

A summarization chatbot is ideal for extracting key points from legal documents. Large language models (LLMs) can be used to summarize complex texts, such as legal documents, making them more accessible and understandable.

Option C (Correct): "Develop a summarization chatbot":This is the correct answer because a summarization chatbot uses LLMs to condense and extract key information from text, which is precisely the requirement for reading and summarizing legal documents.

Option A:"Build an automatic named entity recognition system" is incorrect because it focuses on identifying specific entities, not summarizing documents.

Option B:"Create a recommendation engine" is incorrect as it is used to suggest products or content, not summarize text.

Option D:"Develop a multi-language translation system" is incorrect because translation is unrelated to summarizing text.

AWS AI Practitioner References:

Using LLMs for Text Summarization on AWS:AWS supports developing summarization tools using its AI services, including Amazon Bedrock.

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.

BERT-based Models:

BERT is a pre-trained transformer model designed for natural language understanding tasks, including text completion, where certain words are missing.

It excels at understanding context and relationships between words in a sentence, making it ideal for suggesting potential words to fill in missing text.

Why Option D is Correct:

Contextual Understanding:BERT uses its bidirectional training to understand the context around missing words, making it highly accurate in suggesting suitable replacements.

Text Completion Capability:BERT's architecture is explicitly designed for tasks like masked language modeling, where certain words in a text are masked (or missing), and the model predicts the missing words.

Why Other Options are Incorrect:

A. Topic modeling:Focuses on identifying topics in a text corpus, not on predicting missing words.

B. Clustering models:Group similar data points together, which is not suitable for predicting missing text.

C. Prescriptive ML models:Focus on providing recommendations based on data analysis, not on natural language processing tasks like filling in missing text.

BLEU (Bilingual Evaluation Understudy) is a metric used to evaluate the accuracy of machine-generated translations by comparing them against reference translations. It is commonly used for translation tasks to measure how close the generated output is to professional human translations.

Option A (Correct): "Bilingual Evaluation Understudy (BLEU)":This is the correct answer because BLEU is specifically designed to evaluate the quality of translations, making it suitable for the company's use case.

Option B:"Root mean squared error (RMSE)" is incorrect because RMSE is used for regression tasks to measure prediction errors, not translation quality.

Option C:"Recall-Oriented Understudy for Gisting Evaluation (ROUGE)" is incorrect as it is used to evaluate text summarization, not translation.

Option D:"F1 score" is incorrect because it is typically used for classification tasks, not for evaluating translation accuracy.

AWS AI Practitioner References:

Model Evaluation Metrics on AWS:AWS supports various metrics like BLEU for specific use cases, such as evaluating machine translation models.

Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating parts of the software development process.

Option A (Correct): "Create software snippets, reference tracking, and open-source license tracking":This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.

Option B:"Run an application without provisioning or managing servers" is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer.

Option C:"Enable voice commands for coding and providing natural language search" is incorrect because this is not a function of Amazon Q Developer.

Option D:"Convert audio files to text documents by using ML models" is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.

AWS AI Practitioner References:

Amazon Q Developer Features:AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.

Batch transform in Amazon SageMaker is designed for offline processing of large datasets. It is ideal for scenarios where immediate predictions are not required, and the inference can be done on large datasets that are multiple gigabytes in size. This method processes data in batches, making it suitable for analyzing archived data without the need for real-time access to predictions.

Option A (Correct): "Batch transform":This is the correct answer because batch transform is optimized for handling large datasets and is suitable when immediate access to predictions is not required.

Option B:"Real-time inference" is incorrect because it is used for low-latency, real-time prediction needs, which is not required in this case.

Option C:"Serverless inference" is incorrect because it is designed for small-scale, intermittent inference requests, not for large batch processing.

Option D:"Asynchronous inference" is incorrect because it is used when immediate predictions are required, but with high throughput, whereas batch transform is more suitable for very large datasets.

AWS AI Practitioner References:

Batch Transform on AWS SageMaker:AWS recommends using batch transform for large datasets when real-time processing is not needed, ensuring cost-effectiveness and scalability.