21. 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 of internal data and external data.
Which solution will meet these requirements?
A. 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.
B. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
C. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
D. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.
Answer
D
22. 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. Measurement bias
B. Sampling bias
C. Observer bias
D. Confirmation bias
Answer
B
23. A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources.
Which AI learning strategy provides this self-improvement capability?
A. Supervised learning with a manually curated dataset of good responses and bad responses
B. Reinforcement learning with rewards for positive customer feedback
C. Unsupervised learning to find clusters of similar customer inquiries
D. Supervised learning with a continuously updated FAQ database
Answer
B
24. An AI practitioner has built a deep learning model to classify the types of materials in images. The AI practitioner now wants to measure the model performance.
Which metric will help the AI practitioner evaluate the performance of the model?
A. Confusion matrix
B. Correlation matrix
C. R2 score
D. Mean squared error (MSE)
Answer
A
25. A company has built a chatbot that can respond to natural language questions with images. The company wants to ensure that the chatbot does not return inappropriate or unwanted images.
Which solution will meet these requirements?
A. Implement moderation APIs.
B. Retrain the model with a general public dataset.
C. Perform model validation.
D. Automate user feedback integration.
Answer
A
26. 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. Configure AWS CloudTrail as the logs destination for the model.
B. Enable invocation logging in Amazon Bedrock.
C. Configure AWS Audit Manager as the logs destination for the model.
D. Configure model invocation logging in Amazon EventBridge.
Answer
B
27. 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
28. Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?
A. Embeddings
B. Tokens
C. Models
D. Binaries
Answer
A
29. 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 few-shot prompting to define how the FM can answer the questions.
B. Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.
C. Change the FM inference parameters.
D. Clean the research paper data to remove complex scientific terms.
Answer
B
30. 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. Decrease the temperature value.
B. Increase the temperature value.
C. Decrease the length of output tokens.
D. Increase the maximum generation length.
Answer
A