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 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?
AReal-time inference
BServerless inference
CAsynchronous inference
DBatch transform
A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible.
Which solution will meet these requirements?
ADeploy optimized small language models (SLMs) on edge devices.
BDeploy optimized large language models (LLMs) on edge devices.
CIncorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.
DIncorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.
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?
AAmazon SageMaker Feature Store
BAmazon SageMaker Data Wrangler
CAmazon SageMaker Clarify
DAmazon SageMaker Model Cards
A company has petabytes of unlabeled customer data to use for an advertisement campaign. The company wants to classify its customers into tiers to advertise and promote the company's products.
Which methodology should the company use to meet these requirements?
ASupervised learning
BUnsupervised learning
CReinforcement learning
DReinforcement learning from human feedback (RLHF)
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Applications of Foundation Models
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Question 7
Security, Compliance, and Governance for AI Solutions
Question 8
Guidelines for Responsible AI
Question 9
Applications of Foundation Models
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Security, Compliance, and Governance for AI Solutions
Question 11
Applications of Foundation Models
Question 12
Security, Compliance, and Governance for AI Solutions
Question 13
Fundamentals of AI and ML
Question 14
Applications of Foundation Models
Question 15
Fundamentals of AI and ML
Question 16
Guidelines for Responsible AI
Question 17
Fundamentals of AI and ML
Question 18
Applications of Foundation Models
Question 19
Fundamentals of AI and ML
Question 20
Applications of Foundation Models
Question 21
Applications of Foundation Models
Question 22
Applications of Foundation Models
Question 23
Applications of Foundation Models
Question 24
Applications of Foundation Models
Question 25
Fundamentals of GenAI
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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?
AMulti-modal embedding model
BText embedding model
CMulti-modal generation model
DImage generation model
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?
ABuild a speech recognition system.
BCreate a natural language processing (NLP) named entity recognition system.
CDevelop an anomaly detection system.
DCreate a fraud forecasting system.
A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these forecasts.
An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders.
What should the AI practitioner include in the report to meet the transparency and explainability requirements?
ACode for model training
BPartial dependence plots (PDPs)
CSample data for training
DModel convergence tables
A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents.
Which solution meets these requirements?
ABuild an automatic named entity recognition system.
BCreate a recommendation engine.
CDevelop a summarization chatbot.
DDevelop a multi-language translation system.
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?
AEnsure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
BSet the access permissions for the S3 buckets to allow public access to enable access over the internet.
CUse prompt engineering techniques to tell the model to look for information in Amazon S3.
DEnsure that the S3 data does not contain sensitive information.
A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.
What can Amazon Q Developer do to help the company meet these requirements?
ACreate software snippets, reference tracking, and open source license tracking.
BRun an application without provisioning or managing servers.
CEnable voice commands for coding and providing natural language search.
DConvert audio files to text documents by using ML models.
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?
AAWS PrivateLink
BAmazon Macie
CAmazon CloudFront
DInternet gateway
A company wants to develop an educational game where users answer questions such as the following: "A jar contains six red, four green, and three yellow marbles. What is the probability of choosing a green marble from the jar?"
Which solution meets these requirements with the LEAST operational overhead?
AUse supervised learning to create a regression model that will predict probability.
BUse reinforcement learning to train a model to return the probability.
CUse code that will calculate probability by using simple rules and computations.
DUse unsupervised learning to create a model that will estimate probability density.
A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. 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?
AAdjust the prompt.
BChoose an LLM of a different size.
CIncrease the temperature.
DIncrease the Top K value.
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?
AIncrease the number of epochs.
BUse transfer learning.
CDecrease the number of epochs.
DUse unsupervised learning.
A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations.
Which solution will meet these requirements?
AHuman-in-the-loop validation by using Amazon SageMaker Ground Truth Plus
BData augmentation by using an Amazon Bedrock knowledge base
CImage recognition by using Amazon Rekognition
DData summarization by using Amazon QuickSight Q
Which metric measures the runtime efficiency of operating AI models?
ACustomer satisfaction score (CSAT)
BTraining time for each epoch
CAverage response time
DNumber of training instances
A company is building a contact center application and wants to gain insights from customer conversations. The company wants to analyze and extract key information from the audio of the customer calls.
Which solution meets these requirements?
ABuild a conversational chatbot by using Amazon Lex.
BTranscribe call recordings by using Amazon Transcribe.
CExtract information from call recordings by using Amazon SageMaker Model Monitor.
DCreate classification labels by using Amazon Comprehend.
A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output.
Which ML algorithm meets these requirements?
ADecision trees
BLinear regression
CLogistic regression
DNeural networks
A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company's data.
Which strategy will successfully fine-tune the model?
AProvide labeled data with the prompt field and the completion field.
BPrepare the training dataset by creating a .txt file that contains multiple lines in .csv format.
CPurchase Provisioned Throughput for Amazon Bedrock.
DTrain the model on journals and textbooks.
Which feature of Amazon OpenSearch Service gives companies the ability to build vector database applications?
AIntegration with Amazon S3 for object storage
BSupport for geospatial indexing and queries
CScalable index management and nearest neighbor search capability
DAbility to perform real-time analysis on streaming data
Which option is a use case for generative AI models?
AImproving network security by using intrusion detection systems
BCreating photorealistic images from text descriptions for digital marketing
CEnhancing database performance by using optimized indexing
DAnalyzing financial data to forecast stock market trends
A company wants to build a generative AI application by using Amazon Bedrock and needs to choose a foundation model (FM). The company wants to know how much information can fit into one prompt.
Which consideration will inform the company's decision?
ATemperature
BContext window
CBatch size
DModel size
A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention.
The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.
Which solution meets these requirements?
ASet a low limit on the number of tokens the FM can produce.
BUse batch inferencing to process detailed responses.
CExperiment and refine the prompt until the FM produces the desired responses.
DDefine a higher number for the temperature parameter.
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?
AProvide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.
BProvide a detailed explanation of sentiment analysis and how LLMs work in the prompt.
CProvide the new text passage to be classified without any additional context or examples.
DProvide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.