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WorksheetsAWS AI Practitioner - 51 até 75
Total questions: 25
Worksheet time: 25mins
Name
Class
Date
1.
A company is developing a mobile ML app that uses a phone's camera to diagnose and treat insect bites. The company wants to train an image classification model by using a diverse dataset of insect bite photos from different genders, ethnicities, and geographic locations around the world.
Which principle of responsible AI does the company demonstrate in this scenario?
a)
Fairness
b)
Explainability
c)
Governance
d)
Transparency
2.
A company wants to create a new solution by using AWS Glue. The company has minimal programming experience with AWS Glue.
Which AWS service can help the company use AWS Glue?
a)
Amazon Q Developer
b)
AWS Config
c)
Amazon Personalize
d)
Amazon Comprehend
3.
A company wants to implement a large language model (LLM) based chatbot to provide customer service agents with real-time contextual responses to customers' inquiries. The company will use the company's policies as the knowledge base.
Which solution will meet these requirements MOST cost-effectively?
a)
Retrain the LLM on the company policy data.
b)
Fine-tune the LLM on the company policy data.
c)
Implement Retrieval Augmented Generation (RAG) for in-context responses.
d)
Use pre-training and data augmentation on the company policy data.
4.
A company is using Retrieval Augmented Generation (RAG) with Amazon Bedrock and Stable Diffusion to generate product images based on text descriptions. The results are often random and lack specific details. The company wants to increase the specificity of the generated images.
Which solution meets these requirements?
a)
Increase the number of generation steps.
b)
Use the MASK_IMAGE_BLACK mask source option.
c)
Increase the classifier-free guidance (CFG) scale.
d)
Increase the prompt strength.
5.
A company has fine-tuned a large language model (LLM) to answer questions for a help desk. The company wants to determine if the fine-tuning has enhanced the model's accuracy.
Which metric should the company use for the evaluation?
a)
Precision
b)
Time to first token
c)
F1 score
d)
Word error rate
6.
Which prompting technique can protect against prompt injection attacks?
a)
Adversarial prompting
b)
Zero-shot prompting
c)
Least-to-most prompting
d)
Chain-of-thought prompting
7.
A large retail bank wants to develop an ML system to help the risk management team decide on loan allocations for different demographics.
What must the bank do to develop an unbiased ML model?
a)
Reduce the size of the training dataset.
b)
Ensure that the ML model predictions are consistent with historical results.
c)
Create a different ML model for each demographic group.
d)
Measure class imbalance on the training dataset. Adapt the training process accordingly.
8.
A retail store wants to predict the demand for a specific product for the next few weeks by using the Amazon SageMaker DeepAR forecasting algorithm.
Which type of data will meet this requirement?
a)
Text data
b)
Image data
c)
Time series data
d)
Binary data
9.
A software company builds tools for customers. The company wants to use AI to increase software development productivity.
Which solution will meet these requirements?
a)
Use a binary classification model to generate code reviews.
b)
Install code recommendation software in the company's developer tools.
c)
Install a code forecasting tool to predict potential code issues.
d)
Use a natural language processing (NLP) tool to generate code.
10.
An ML research team develops custom ML models. The model artifacts are shared with other teams for integration into products and services. The ML team retains the model training code and data. The ML team wants to build a mechanism that the ML team can use to audit models.
Which solution should the ML team use when publishing the custom ML models?
a)
Create documents with the relevant information. Store the documents in Amazon S3.
b)
Use AWS AI Service Cards for transparency and understanding models.
c)
Create Amazon SageMaker Model Cards with intended uses and training and inference details.
d)
Create model training scripts. Commit the model training scripts to a Git repository.
11.
A company needs to use Amazon SageMaker for model training and inference. The company must comply with regulatory requirements to run SageMaker jobs in an isolated environment without internet access.
Which solution will meet these requirements?
a)
Run SageMaker training and inference by using SageMaker Experiments.
b)
Run SageMaker training and Inference by using network Isolation.
c)
Encrypt the data at rest by using encryption for SageMaker geospatial capabilities.
d)
Associate appropriate AWS Identity and Access Management (IAM) roles with the SageMaker jobs.
12.
A company is testing the security of a foundation model (FM). During testing, the company wants to get around the safety features and make harmful content.
Which security technique is this an example of?
a)
Fuzzing training data to find vulnerabilities
b)
Denial of service (DoS)
c)
Penetration testing with authorization
d)
Jailbreak
13.
A social media company wants to use a large language model (LLM) to summarize messages. The company has chosen a few LLMs that are available on Amazon SageMaker JumpStart. The company wants to compare the generated output toxicity of these models.
Which strategy gives the company the ability to evaluate the LLMs with the LEAST operational overhead?
a)
Crowd-sourced evaluation
b)
Automatic model evaluation
c)
Model evaluation with human workers
d)
Reinforcement learning from human feedback (RLHF)
14.
A company wants to use Amazon Bedrock. The company needs to review which security aspects the company is responsible for when using Amazon Bedrock.
Which security aspect will the company be responsible for?
a)
Patching and updating the versions of Amazon Bedrock
b)
Protecting the infrastructure that hosts Amazon Bedrock
c)
Securing the company's data in transit and at rest
d)
Provisioning Amazon Bedrock within the company network
15.
Which prompting attack directly exposes the configured behavior of a large language model (LLM)?
a)
Prompted persona switches
b)
Exploiting friendliness and trust
c)
Ignoring the prompt template
d)
Extracting the prompt template
16.
A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation.
Which AWS service meets these requirements?
a)
Amazon S3
b)
Amazon Elastic Block Store (Amazon EBS)
c)
Amazon Elastic File System (Amazon EFS)
d)
AWS Snowcone
17.
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)
Use Amazon Inspector to monitor SageMaker Studio.
b)
Use Amazon Macie to monitor SageMaker Studio.
c)
Configure SageMaker to use a VPC with an S3 endpoint.
d)
Configure SageMaker to use S3 Glacier Deep Archive.
18.
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)
Number of tokens consumed
c)
Amount of data used to train the LLM
d)
Total training time
19.
What are tokens in the context of generative AI models?
a)
Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units.
b)
Tokens are the mathematical representations of words or concepts used in generative AI models.
c)
Tokens are the pre-trained weights of a generative AI model that are fine-tuned for specific tasks.
d)
Tokens are the specific prompts or instructions given to a generative AI model to generate output.
20.
Which option is a benefit of ongoing pre-training when fine-tuning a foundation model (FM)?
a)
Helps decrease the model's complexity
b)
Improves model performance over time
c)
Decreases the training time requirement
d)
Optimizes model inference time
21.
A large retailer receives thousands of customer support inquiries about products every day. The customer support inquiries need to be processed and responded to quickly. The company wants to implement Agents for Amazon Bedrock.
What are the key benefits of using Amazon Bedrock agents that could help this retailer?
a)
Generation of custom foundation models (FMs) to predict customer needs
b)
Automation of repetitive tasks and orchestration of complex workflows
c)
Automatically calling multiple foundation models (FMs) and consolidating the results
d)
Selecting the foundation model (FM) based on predefined criteria and metrics
22.
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)
Bilingual Evaluation Understudy (BLEU)
b)
Root mean squared error (RMSE)
c)
Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
d)
F1 score
23.
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)
Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.
b)
Mask the confidential data in the inference responses by using dynamic data masking.
c)
Encrypt the confidential data in the inference responses by using Amazon SageMaker.
d)
Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).
24.
An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and factual but is incorrect.
Which problem is the LLM having?
a)
Data leakage
b)
Hallucination
c)
Overfitting
d)
Underfitting
25.
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)
Decrease the number of tokens in the prompt.
c)
Increase the number of tokens in the prompt.
d)
Use Provisioned Throughput.
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