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NEW QUESTION # 26
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 AWS Batch to host the model and serve predictions.
- C. Use Amazon CloudFront to deploy the model.
- D. Use Amazon API Gateway to host the model and serve predictions.
Answer: A
NEW QUESTION # 27
A company wants to deploy a conversational chatbot to answer customer questions. The chatbot is based on a fine-tuned Amazon SageMaker JumpStart model. The application must comply with multiple regulatory frameworks.
Which capabilities can the company show compliance for? (Select TWO.)
- A. Data protection
- B. Threat detection
- C. Cost optimization
- D. Auto scaling inference endpoints
- E. Loosely coupled microservices
Answer: A,B
Explanation:
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NEW QUESTION # 28
Which option is a benefit of ongoing pre-training when fine-tuning a foundation model (FM)?
- A. Decreases the training time requirement
- B. Helps decrease the model's complexity
- C. Optimizes model inference time
- D. Improves model performance over time
Answer: D
NEW QUESTION # 29
A company is using Amazon SageMaker Studio notebooks to build and train ML models. The company stores the data in an Amazon S3 bucket. The company needs to manage the flow of data from Amazon S3 to SageMaker Studio notebooks.
Which solution will meet this requirement?
- A. Configure SageMaker to use S3 Glacier Deep Archive.
- B. Use Amazon Macie to monitor SageMaker Studio.
- C. Use Amazon Inspector to monitor SageMaker Studio.
- D. Configure SageMaker to use a VPC with an S3 endpoint.
Answer: D
NEW QUESTION # 30
A company has thousands of customer support interactions per day and wants to analyze these interactions to identify frequently asked questions and develop insights.
Which AWS service can the company use to meet this requirement?
- A. Amazon Transcribe
- B. Amazon Comprehend
- C. Amazon Translate
- D. Amazon Lex
Answer: B
NEW QUESTION # 31
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?
- A. Increase the maximum generation length
- B. Decrease the temperature value
- C. Increase the temperature value
- D. Decrease the length of output tokens
Answer: B
NEW QUESTION # 32
A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns.
The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.
Which solution meets these requirements?
- A. Increase the model's complexity by adding more layers to the model's architecture.
- B. Optimize the model's architecture and hyperparameters to improve the model's overall performance.
- C. Select a large, diverse dataset to pre-train a new generative model.
- D. Create effective prompts that provide clear instructions and context to guide the model's generation.
Answer: D
NEW QUESTION # 33
Which functionality does Amazon SageMaker Clarify provide?
- A. Monitors the quality of ML models in production
- B. Identifies potential bias during data preparation
- C. Documents critical details about ML models
- D. Integrates a Retrieval Augmented Generation (RAG) workflow
Answer: B
NEW QUESTION # 34
A company has installed a security camera. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group.
Which type of bias is affecting the model output?
- A. Observer bias
- B. Sampling bias
- C. Confirmation bias
- D. Measurement bias
Answer: B
NEW QUESTION # 35
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. Model deployment
- B. Bias correction
- C. Training
- D. Inference
Answer: D
NEW QUESTION # 36
How can companies use large language models (LLMs) securely on Amazon Bedrock?
- A. Use Amazon CloudWatch Logs to make models explainable and to monitor for bias.
- B. Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.
- C. Enable Amazon Bedrock automatic model evaluation jobs.
- D. Enable AWS Audit Manager for automatic model evaluation jobs.
Answer: B
NEW QUESTION # 37
A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost.
Which solution will meet these requirements?
- A. Customize the model by using fine-tuning.
- B. Increase the number of tokens in the prompt.
- C. Use Provisioned Throughput.
- D. Decrease the number of tokens in the prompt.
Answer: D
NEW QUESTION # 38
A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly.
Which evaluation metric should the company use to measure the model's performance?
- A. Learning rate
- B. Accuracy
- C. Root mean squared error (RMSE)
- D. R-squared score
Answer: B
NEW QUESTION # 39
An accounting firm wants to implement a large language model (LLM) to automate document processing.
The firm must proceed responsibly to avoid potential harms.
What should the firm do when developing and deploying the LLM? (Select TWO.)
- A. Adjust the temperature parameter of the model.
- B. Include fairness metrics for model evaluation.
- C. Avoid overfitting on the training data.
- D. Apply prompt engineering techniques.
- E. Modify the training data to mitigate bias.
Answer: B,E
Explanation:
I'll continue with more questions. Stay tuned!
NEW QUESTION # 40
A company is using a pre-trained large language model (LLM) to build a chatbot for productrecommendations. The company needs the LLM outputs to be short and written in a specific language.
Which solution will align the LLM response quality with the company's expectations?
- A. Increase the temperature.
- B. Increase the Top K value.
- C. Adjust the prompt.
- D. Choose an LLM of a different size.
Answer: C
NEW QUESTION # 41
A company is building an ML model to analyze archived data. The company must perform inference on large datasets that are multiple GBs in size. The company does not need to access the model predictions immediately.
Which Amazon SageMaker inference option will meet these requirements?
- A. Batch transform
- B. Real-time inference
- C. Serverless inference
- D. Asynchronous inference
Answer: A
Explanation:
I'll continue with more questions shortly. Stay tuned!
NEW QUESTION # 42
A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.
Which SageMaker inference option meets these requirements?
- A. Batch transform
- B. Serverless inference
- C. Real-time inference
- D. Asynchronous inference
Answer: C
NEW QUESTION # 43
An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential data. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.
How should the AI practitioner prevent responses based on confidential data?
- A. Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).
- B. Encrypt the confidential data in the inference responses by using Amazon SageMaker.
- C. Mask the confidential data in the inference responses by using dynamic data masking.
- D. Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.
Answer: D
NEW QUESTION # 44
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. Use prompt engineering techniques to tell the model to look for information in Amazon S3.
- B. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
- C. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
- D. Ensure that the S3 data does not contain sensitive information.
Answer: C
NEW QUESTION # 45
A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?
- A. Feature engineering
- B. Exploratory data analysis
- C. Hyperparameter tuning
- D. Data pre-processing
Answer: B
NEW QUESTION # 46
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis.
The company wants to classify the sentiment of text passages as positive or negative.
Which prompt engineering strategy meets these requirements?
- A. Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.
- B. Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.
- C. Provide the new text passage to be classified without any additional context or examples.
- D. Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.
Answer: A
NEW QUESTION # 47
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 AWS Audit Manager as the logs destination for the model.
- C. Configure model invocation logging in Amazon EventBridge.
- D. Configure AWS CloudTrail as the logs destination for the model.
Answer: A
NEW QUESTION # 48
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