The Best Valid AIF-C01 Dumps for Helping Passing AIF-C01 Exam!
UPDATED Amazon AIF-C01 Exam Questions & Answer
NEW QUESTION # 35
A company has built a solution by using generative AI. The solution uses large language models (LLMs) to translate training manuals from English into other languages. The company wants to evaluate the accuracy of the solution by examining the text generated for the manuals.
Which model evaluation strategy meets these requirements?
- A. Root mean squared error (RMSE)
- B. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
- C. Bilingual Evaluation Understudy (BLEU)
- D. F1 score
Answer: C
NEW QUESTION # 36
A research company implemented a chatbot by using a foundation model (FM) from Amazon Bedrock. The chatbot searches for answers to questions from a large database of research papers.
After multiple prompt engineering attempts, the company notices that the FM is performing poorly because of the complex scientific terms in the research papers.
How can the company improve the performance of the chatbot?
- A. Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.
- B. Clean the research paper data to remove complex scientific terms.
- C. Change the FM inference parameters.
- D. Use few-shot prompting to define how the FM can answer the questions.
Answer: A
NEW QUESTION # 37
Which functionality does Amazon SageMaker Clarify provide?
- A. Identifies potential bias during data preparation
- B. Documents critical details about ML models
- C. Monitors the quality of ML models in production
- D. Integrates a Retrieval Augmented Generation (RAG) workflow
Answer: A
NEW QUESTION # 38
An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.
Which strategy should the AI practitioner use?
- A. Enable invocation logging in Amazon Bedrock.
- B. Configure model invocation logging in Amazon EventBridge.
- C. Configure AWS CloudTrail as the logs destination for the model.
- D. Configure AWS Audit Manager as the logs destination for the model.
Answer: A
NEW QUESTION # 39
How can companies use large language models (LLMs) securely on Amazon Bedrock?
- A. Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.
- B. Use Amazon CloudWatch Logs to make models explainable and to monitor for bias.
- C. Enable Amazon Bedrock automatic model evaluation jobs.
- D. Enable AWS Audit Manager for automatic model evaluation jobs.
Answer: A
NEW QUESTION # 40
A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.
Which solution will meet these requirements?
- A. Gather more data. Use Amazon Rekognition to add custom labels to the data.
- B. Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
- C. Encrypt and secure training data by using Amazon Macie.
- D. Configure the security and compliance by using Amazon Inspector.
Answer: B
NEW QUESTION # 41
A company built a deep learning model for object detection and deployed the model to production.
Which AI process occurs when the model analyzes a new image to identify objects?
- A. Training
- B. Model deployment
- C. Inference
- D. Bias correction
Answer: C
NEW QUESTION # 42
A medical company deployed a disease detection model on Amazon Bedrock. To comply with privacy policies, the company wants to prevent the model from including personal patient information in its responses. The company also wants to receive notification when policy violations occur.
Which solution meets these requirements?
- A. Configure AWS CloudTrail to monitor the model's responses and create alerts for any detected personal information.
- B. Use Amazon Macie to scan the model's output for sensitive data and set up alerts for potential violations.
- C. Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations.
- D. Implement Amazon SageMaker Model Monitor to detect data drift and receive alerts when model quality degrades.
Answer: C
Explanation:
I'll continue with more questions shortly. Stay tuned!
NEW QUESTION # 43
A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket.
The data is encrypted with Amazon S3 managed keys (SSE-S3).
The FM encounters a failure when attempting to access the S3 bucket data.
Which solution will meet these requirements?
- A. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
- B. Ensure that the S3 data does not contain sensitive information.
- C. Use prompt engineering techniques to tell the model to look for information in Amazon S3.
- D. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
Answer: A
NEW QUESTION # 44
An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.
Which type of FM should the AI practitioner use to power the search application?
- A. Multi-modal embedding model
- B. Text embedding model
- C. Multi-modal generation model
- D. Image generation model
Answer: A
NEW QUESTION # 45
A financial institution is using Amazon Bedrock to develop an AI application. The application is hosted in a VPC. To meet regulatory compliance standards, the VPC is not allowed access to any internet traffic.
Which AWS service or feature will meet these requirements?
- A. Amazon CloudFront
- B. AWS PrivateLink
- C. Internet gateway
- D. Amazon Macie
Answer: B
NEW QUESTION # 46
A company wants to build an interactive application for children that generates new stories based on classic stories. The company wants to use Amazon Bedrock and needs to ensure that the results and topics are appropriate for children.
Which AWS service or feature will meet these requirements?
- A. Amazon Rekognition
- B. Guardrails for Amazon Bedrock
- C. Agents for Amazon Bedrock
- D. Amazon Bedrock playgrounds
Answer: B
NEW QUESTION # 47
A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams.
Which SageMaker feature meets these requirements?
- A. Amazon SageMaker Data Wrangler
- B. Amazon SageMaker Model Cards
- C. Amazon SageMaker Clarify
- D. Amazon SageMaker Feature Store
Answer: D
NEW QUESTION # 48
A company has developed an ML model for image classification. The company wants to deploy the model to production so that a web application can use the model.
The company needs to implement a solution to host the model and serve predictions without managing any of the underlying infrastructure.
Which solution will meet these requirements?
- A. Use Amazon SageMaker Serverless Inference to deploy the model.
- B. Use Amazon CloudFront to deploy the model.
- C. Use AWS Batch to host the model and serve predictions.
- D. Use Amazon API Gateway to host the model and serve predictions.
Answer: A
NEW QUESTION # 49
Which strategy evaluates the accuracy of a foundation model (FM) that is used in image classification tasks?
- A. Measure the model's accuracy against a predefined benchmark dataset.
- B. Count the number of layers in the neural network.
- C. Assess the color accuracy of images processed by the model.
- D. Calculate the total cost of resources used by the model.
Answer: A
NEW QUESTION # 50
A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source.
Which solution meets these requirements?
- A. Create a natural language processing (NLP) named entity recognition system.
- B. Build a speech recognition system.
- C. Create a fraud forecasting system.
- D. Develop an anomaly detection system.
Answer: D
NEW QUESTION # 51
What are tokens in the context of generative AI models?
- A. Tokens are the specific prompts or instructions given to a generative AI model to generate output.
- B. Tokens are the pre-trained weights of a generative AI model that are fine-tuned for specific tasks.
- C. Tokens are the mathematical representations of words or concepts used in generative AI models.
- D. Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units.
Answer: D
NEW QUESTION # 52
A company wants to assess the costs that are associated with using a large language model (LLM) to generate inferences. The company wants to use Amazon Bedrock to build generative AI applications.
Which factor will drive the inference costs?
- A. Temperature value
- B. Total training time
- C. Number of tokens consumed
- D. Amount of data used to train the LLM
Answer: C
NEW QUESTION # 53
A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks.
Which ML strategy meets these requirements?
- A. Decrease the number of epochs.
- B. Use unsupervised learning.
- C. Use transfer learning.
- D. Increase the number of epochs.
Answer: C
NEW QUESTION # 54
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteri a. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers.
Which actions should the company take to meet these requirements? (Select TWO.)
- A. Detect imbalances or disparities in the data.
- B. Evaluate the model's behavior so that the company can provide transparency to stakeholders.
- C. Ensure that the model's inference time is within the accepted limits.
- D. Ensure that the model runs frequently.
- E. Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate.
Answer: A,B
NEW QUESTION # 55
A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.
Which solution will meet these requirements?
- A. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.
- B. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
- C. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
- D. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
Answer: A
Explanation:
I'll continue to format the rest. Let me know if you would like me to provide them all in one go or in parts.
NEW QUESTION # 56
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