AWS Certified AI Practitioner AIF-C01

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?

A.

Code for model training

B.

Partial dependence plots (PDPs)

C.

Sample data for training

D.

Model convergence tables

Partial dependence plots (PDPs)

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?

A.

Deploy optimized small language models (SLMs) on edge devices.

B.

Deploy optimized large language models (LLMs) on edge devices.

C.

Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.

D.

Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.

Deploy optimized small language models (SLMs) on edge devices.

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

Fairness

A company is developing an ML model to make loan approvals. The company must implement a solution to detect bias in the model. The company must also be able to explain the model's predictions.

Which solution will meet these requirements?

A.

Amazon SageMaker Clarify

B.

Amazon SageMaker Data Wrangler

C.

Amazon SageMaker Model Cards

D.

AWS AI Service Cards

Amazon SageMaker Clarify

A company has developed a generative text summarization model by using Amazon Bedrock. The company will use Amazon Bedrock automatic model evaluation capabilities.

Which metric should the company use to evaluate the accuracy of the model?

A.

Area Under the ROC Curve (AUC) score

B.

F1 score

C.

BERTScore

D.

Real world knowledge (RWK) score

BERTScore

An AI practitioner wants to predict the classification of flowers based on petal length, petal width, sepal length, and sepal width.

Which algorithm meets these requirements?

A.

K-nearest neighbors (k-NN)

B.

K-mean

C.

Autoregressive Integrated Moving Average (ARIMA)

D.

Linear regression

K-nearest neighbors (k-NN)

A company is using custom models in Amazon Bedrock for a generative AI application. The company wants to use a company managed encryption key to encrypt the model artifacts that the model customization jobs create.

Which AWS service meets these requirements?

A.

AWS Key Management Service (AWS KMS)

B.

Amazon Inspector

C.

Amazon Macie

D.

AWS Secrets Manager

AWS Key Management Service (AWS KMS)

A company wants to use large language models (LLMs) to produce code from natural language code comments.

Which LLM feature meets these requirements?

A.

Text summarization

B.

Text generation

C.

Text completion

D.

Text classification

Text generation

A company is introducing a mobile app that helps users learn foreign languages. The app makes text more coherent by calling a large language model (LLM). The company collected a diverse dataset of text and supplemented the dataset with examples of more readable versions. The company wants the LLM output to resemble the provided examples.

Which metric should the company use to assess whether the LLM meets these requirements?

A.

Value of the loss function

B.

Semantic robustness

C.

Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score

D.

Latency of the text generation

Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score

A company notices that its foundation model (FM) generates images that are unrelated to the prompts. The company wants to modify the prompt techniques to decrease unrelated images.

Which solution meets these requirements?

A.

Use zero-shot prompts.

B.

Use negative prompts.

C.

Use positive prompts.

D.

Use ambiguous prompts.

Use negative prompts.

A company wants to use a large language model (LLM) to generate concise, feature-specific descriptions for the company’s products.

Which prompt engineering technique meets these requirements?

A.

Create one prompt that covers all products. Edit the responses to make the responses more specific, concise, and tailored to each product.

B.

Create prompts for each product category that highlight the key features. Include the desired output format and length for each prompt response.

C.

Include a diverse range of product features in each prompt to generate creative and unique descriptions.

D.

Provide detailed, product-specific prompts to ensure precise and customized descriptions.

Create prompts for each product category that highlight the key features. Include the desired output format and length for each prompt response.

A company is developing an ML model to predict customer churn. The model performs well on the training dataset but does not accurately predict churn for new data.

Which solution will resolve this issue?

A.

Decrease the regularization parameter to increase model complexity.

B.

Increase the regularization parameter to decrease model complexity.

C.

Add more features to the input data.

D.

Train the model for more epochs.

Increase the regularization parameter to decrease model complexity.

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 Feature Store

B.

Amazon SageMaker Data Wrangler

C.

Amazon SageMaker Clarify

D.

Amazon SageMaker Model Cards

Amazon SageMaker Feature Store

A company is implementing intelligent agents to provide conversational search experiences for its customers. The company needs a database service that will support storage and queries of embeddings from a generative AI model as vectors in the database.

Which AWS service will meet these requirements?

A.

Amazon Athena

B.

Amazon Aurora PostgreSQL

C.

Amazon Redshift

D.

Amazon EMR

Amazon Aurora PostgreSQL

A financial institution is building an AI solution to make loan approval decisions by using a foundation model (FM). For security and audit purposes, the company needs the AI solution's decisions to be explainable.

Which factor relates to the explainability of the AI solution's decisions?

A.

Model complexity

B.

Training time

C.

Number of hyperparameters

D.

Deployment time

Model complexity

A pharmaceutical company wants to analyze user reviews of new medications and provide a concise overview for each medication.

Which solution meets these requirements?

A.

Create a time-series forecasting model to analyze the medication reviews by using Amazon Personalize.

B.

Create medication review summaries by using Amazon Bedrock large language models (LLMs).

C.

Create a classification model that categorizes medications into different groups by using Amazon SageMaker.

D.

Create medication review summaries by using Amazon Rekognition.

Create medication review summaries by using Amazon Bedrock large language models (LLMs).

A company wants to build a lead prioritization application for its employees to contact potential customers. The application must give employees the ability to view and adjust the weights assigned to different variables in the model based on domain knowledge and expertise.

Which ML model type meets these requirements?

A.

Logistic regression model

B.

Deep learning model built on principal components

C.

K-nearest neighbors (k-NN) model

D.

Neural network

Logistic regression model

HOTSPOT

-

A company wants to build an ML application.

Select and order the correct steps from the following list to develop a well-architected ML workload. Each step should be selected one time.

Step 1: Define business goal and frame ML problem.

Step 2: Develop model

Step 3: Deploy model

Step 4: Monitor model

Which strategy will determine if a foundation model (FM) effectively meets business objectives?

A.

Evaluate the model's performance on benchmark datasets.

B.

Analyze the model's architecture and hyperparameters.

C.

Assess the model's alignment with specific use cases.

D.

Measure the computational resources required for model deployment.

Assess the model's alignment with specific use cases.

A company needs to train an ML model to classify images of different types of animals. The company has a large dataset of labeled images and will not label more data.

Which type of learning should the company use to train the model?

A.

Supervised learning

B.

Unsupervised learning

C.

Reinforcement learning

D.

Active learning

Supervised learning

Which phase of the ML lifecycle determines compliance and regulatory requirements?

A.

Feature engineering

B.

Model training

C.

Data collection

D.

Business goal identification

Business goal identification

A food service company wants to develop an ML model to help decrease daily food waste and increase sales revenue. The company needs to continuously improve the model's accuracy.

Which solution meets these requirements?

A.

Use Amazon SageMaker and iterate with newer data.

B.

Use Amazon Personalize and iterate with historical data.

C.

Use Amazon CloudWatch to analyze customer orders.

D.

Use Amazon Rekognition to optimize the model.

Use Amazon SageMaker and iterate with newer data.

A company has developed an ML model to predict real estate sale prices. The company wants to deploy the model to make predictions without managing servers or infrastructure.

Which solution meets these requirements?

A.

Deploy the model on an Amazon EC2 instance.

B.

Deploy the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.

C.

Deploy the model by using Amazon CloudFront with an Amazon S3 integration.

D.

Deploy the model by using an Amazon SageMaker endpoint.

Deploy the model by using an Amazon SageMaker endpoint.

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?

A.

Create software snippets, reference tracking, and open source license tracking.

B.

Run an application without provisioning or managing servers.

C.

Enable voice commands for coding and providing natural language search.

D.

Convert audio files to text documents by using ML models.

Create software snippets, reference tracking, and open source license tracking.

A company wants to develop an AI application to help its employees check open customer claims, identify details for a specific claim, and access documents for a claim.

Which solution meets these requirements?

A.

Use Agents for Amazon Bedrock with Amazon Fraud Detector to build the application.

B.

Use Agents for Amazon Bedrock with Amazon Bedrock knowledge bases to build the application.

C.

Use Amazon Personalize with Amazon Bedrock knowledge bases to build the application.

D.

Use Amazon SageMaker to build the application by training a new ML model.

Use Agents for Amazon Bedrock with Amazon Bedrock knowledge bases to build the application.

A manufacturing company uses AI to inspect products and find any damages or defects.

Which type of AI application is the company using?

A.

Recommendation system

B.

Natural language processing (NLP)

C.

Computer vision

D.

Image processing

Computer vision

A company wants to create an ML model to predict customer satisfaction. The company needs fully automated model tuning.

Which AWS service meets these requirements?

A.

Amazon Personalize

B.

Amazon SageMaker

C.

Amazon Athena

D.

Amazon Comprehend

Amazon SageMaker

Which technique can a company use to lower bias and toxicity in generative AI applications during the post-processing ML lifecycle?

A.

Human-in-the-loop

B.

Data augmentation

C.

Feature engineering

D.

Adversarial training

Human-in-the-loop

A bank has fine-tuned a large language model (LLM) to expedite the loan approval process. During an external audit of the model, the company discovered that the model was approving loans at a faster pace for a specific demographic than for other demographics.

How should the bank fix this issue MOST cost-effectively?

A.

Include more diverse training data. Fine-tune the model again by using the new data.

B.

Use Retrieval Augmented Generation (RAG) with the fine-tuned model.

C.

Use AWS Trusted Advisor checks to eliminate bias.

D.

Pre-train a new LLM with more diverse training data.

Include more diverse training data. Fine-tune the model again by using the new data.

HOTSPOT

-

A company has developed a large language model (LLM) and wants to make the LLM available to multiple internal teams. The company needs to select the appropriate inference mode for each team.

Select the correct inference mode from the following list for each use case. Each inference mode should be selected one or more times.

Realtime

Batch

Realtime

A company needs to log all requests made to its Amazon Bedrock API. The company must retain the logs securely for 5 years at the lowest possible cost.

Which combination of AWS service and storage class meets these requirements? (Choose two.)

A.

AWS CloudTrail

B.

Amazon CloudWatch

C.

AWS Audit Manager

D.

Amazon S3 Intelligent-Tiering

E.

Amazon S3 Standard

AWS CloudTrail
Amazon S3 Intelligent-Tiering

An ecommerce company wants to improve search engine recommendations by customizing the results for each user of the company’s ecommerce platform.

Which AWS service meets these requirements?

A.

Amazon Personalize

B.

Amazon Kendra

C.

Amazon Rekognition

D.

Amazon Transcribe

Amazon Personalize

A hospital is developing an AI system to assist doctors in diagnosing diseases based on patient records and medical images. To comply with regulations, the sensitive patient data must not leave the country the data is located in.

Which data governance strategy will ensure compliance and protect patient privacy?

A.

Data residency

B.

Data quality

C.

Data discoverability

D.

Data enrichment

Data residency

A company needs to monitor the performance of its ML systems by using a highly scalable AWS service.

Which AWS service meets these requirements?

A.

Amazon CloudWatch

B.

AWS CloudTrail

C.

AWS Trusted Advisor

D.

AWS Config

Amazon CloudWatch

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?

AWS PrivateLink

An AI practitioner is developing a prompt for an Amazon Titan model. The model is hosted on Amazon Bedrock. The AI practitioner is using the model to solve numerical reasoning challenges. The AI practitioner adds the following phrase to the end of the prompt: “Ask the model to show its work by explaining its reasoning step by step.”

Which prompt engineering technique is the AI practitioner using?

A.

Chain-of-thought prompting

B.

Prompt injection

C.

Few-shot prompting

D.

Prompt templating

Chain-of-thought prompting

Which AWS service makes foundation models (FMs) available to help users build and scale generative AI applications?

A.

Amazon Q Developer

B.

Amazon Bedrock

C.

Amazon Kendra

D.

Amazon Comprehend

Amazon Bedrock

A company is building a mobile app for users who have a visual impairment. The app must be able to hear what users say and provide voice responses.

Which solution will meet these requirements?

A.

Use a deep learning neural network to perform speech recognition.

B.

Build ML models to search for patterns in numeric data.

C.

Use generative AI summarization to generate human-like text.

D.

Build custom models for image classification and recognition.

Use a deep learning neural network to perform speech recognition.

A company wants to enhance response quality for a large language model (LLM) for complex problem-solving tasks. The tasks require detailed reasoning and a step-by-step explanation process.

Which prompt engineering technique meets these requirements?

A.

Few-shot prompting

B.

Zero-shot prompting

C.

Directional stimulus prompting

D.

Chain-of-thought prompting

Chain-of-thought prompting

A company wants to keep its foundation model (FM) relevant by using the most recent data. The company wants to implement a model training strategy that includes regular updates to the FM.

Which solution meets these requirements?

A.

Batch learning

B.

Continuous pre-training

C.

Static training

D.

Latent training

Continuous pre-training

A company wants to develop ML applications to improve business operations and efficiency.

Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be selected one or more times.

Supervise learning

Supervise learning

Unsupervised learning.

Unsupervised learning

Which option is a characteristic of AI governance frameworks for building trust and deploying human-centered AI technologies?

A.

Expanding initiatives across business units to create long-term business value

B.

Ensuring alignment with business standards, revenue goals, and stakeholder expectations

C.

Overcoming challenges to drive business transformation and growth

D.

Developing policies and guidelines for data, transparency, responsible AI, and compliance

Developing policies and guidelines for data, transparency, responsible AI, and compliance

An ecommerce company is using a generative AI chatbot to respond to customer inquiries. The company wants to measure the financial effect of the chatbot on the company’s operations.

Which metric should the company use?

A.

Number of customer inquiries handled

B.

Cost of training AI models

C.

Cost for each customer conversation

D.

Average handled time (AHT)

Cost for each customer conversation

A company wants to find groups for its customers based on the customers’ demographics and buying patterns.

Which algorithm should the company use to meet this requirement?

A.

K-nearest neighbors (k-NN)

B.

K-means

C.

Decision tree

D.

Support vector machine

K-means

A company’s large language model (LLM) is experiencing hallucinations.

How can the company decrease hallucinations?

A.

Set up Agents for Amazon Bedrock to supervise the model training.

B.

Use data pre-processing and remove any data that causes hallucinations.

C.

Decrease the temperature inference parameter for the model.

D.

Use a foundation model (FM) that is trained to not hallucinate.

Decrease the temperature inference parameter for the model.

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?

A.

Use supervised learning to create a regression model that will predict probability.

B.

Use reinforcement learning to train a model to return the probability.

C.

Use code that will calculate probability by using simple rules and computations.

D.

Use unsupervised learning to create a model that will estimate probability density.

Use code that will calculate probability by using simple rules and computations.

A company is using a large language model (LLM) on Amazon Bedrock to build a chatbot. The chatbot processes customer support requests. To resolve a request, the customer and the chatbot must interact a few times.

Which solution gives the LLM the ability to use content from previous customer messages?

A.

Turn on model invocation logging to collect messages.

B.

Add messages to the model prompt.

C.

Use Amazon Personalize to save conversation history.

D.

Use Provisioned Throughput for the LLM.

Add messages to the model prompt.

A company’s employees provide product descriptions and recommendations to customers when customers call the customer service center. These recommendations are based on where the customers are located. The company wants to use foundation models (FMs) to automate this process.

Which AWS service meets these requirements?

A.

Amazon Macie

B.

Amazon Transcribe

C.

Amazon Bedrock

D.

Amazon Textract

Amazon Bedrock

A company wants to upload customer service email messages to Amazon S3 to develop a business analysis application. The messages sometimes contain sensitive data. The company wants to receive an alert every time sensitive information is found.

Which solution fully automates the sensitive information detection process with the LEAST development effort?

A.

Configure Amazon Macie to detect sensitive information in the documents that are uploaded to Amazon S3.

B.

Use Amazon SageMaker endpoints to deploy a large language model (LLM) to redact sensitive data.

C.

Develop multiple regex patterns to detect sensitive data. Expose the regex patterns on an Amazon SageMaker notebook.

D.

Ask the customers to avoid sharing sensitive information in their email messages.

Configure Amazon Macie to detect sensitive information in the documents that are uploaded to Amazon S3.

A company is training its employees on how to structure prompts for foundation models.

Select the correct prompt engineering technique from the following list for each prompt template. Each prompt engineering technique should be selected one time.

Zero shot

Few shot

Chain of thought

A company is using a generative AI model to develop a digital assistant. The model’s responses occasionally include undesirable and potentially harmful content.

Select the correct Amazon Bedrock filter policy from the following list for each mitigation action. Each filter policy should be selected one time.

Content filters

Denied topics

Word filters

Contextual grounding check

Which option is a benefit of using Amazon SageMaker Model Cards to document AI models?

A.

Providing a visually appealing summary of a mode’s capabilities.

B.

Standardizing information about a model’s purpose, performance, and limitations.

C.

Reducing the overall computational requirements of a model.

D.

Physically storing models for archival purposes.

Standardizing information about a model’s purpose, performance, and limitations.

What does an F1 score measure in the context of foundation model (FM) performance?

A.

Model precision and recall

B.

Model speed in generating responses

C.

Financial cost of operating the model

D.

Energy efficiency of the model’s computations

Model precision and recall

A company deployed an AI/ML solution to help customer service agents respond to frequently asked questions. The questions can change over time. The company wants to give customer service agents the ability to ask questions and receive automatically generated answers to common customer questions.

Which strategy will meet these requirements MOST cost-effectively?

A.

Fine-tune the model regularly.

B.

Train the model by using context data.

C.

Pre-train and benchmark the model by using context data.

D.

Use Retrieval Augmented Generation (RAG) with prompt engineering techniques.

Use Retrieval Augmented Generation (RAG) with prompt engineering techniques.

A company built an AI-powered resume screening system. The company used a large dataset to train the model. The dataset contained resumes that were not representative of all demographics.

Which core dimension of responsible AI does this scenario present?

A.

Fairness

B.

Explainability

C.

Privacy and security

D.

Transparency

Fairness

A global financial company has developed an ML application to analyze stock market data and provide stock market trends. The company wants to continuously monitor the application development phases and to ensure that company policies and industry regulations are followed.

Which AWS services will help the company assess compliance requirements? (Choose two.)

A.

AWS Audit Manager

B.

AWS Config

C.

Amazon Inspector

D.

Amazon CloudWatch

E.

AWS CloudTrail

AWS Audit Manager
AWS Config

Which metric measures the runtime efficiency of operating AI models?

A.

Customer satisfaction score (CSAT)

B.

Training time for each epoch

C.

Average response time

D.

Number of training instances

Average response time

A company wants to improve the accuracy of the responses from a generative AI application. The application uses a foundation model (FM) on Amazon Bedrock.

Which solution meets these requirements MOST cost-effectively?

A.

Fine-tune the FM.

B.

Retrain the FM.

C.

Train a new FM.

D.

Use prompt engineering.

Use prompt engineering.

A company wants to identify harmful language in the comments section of social media posts by using an ML model. The company will not use labeled data to train the model.

Which strategy should the company use to identify harmful language?

A.

Use Amazon Rekognition moderation.

B.

Use Amazon Comprehend toxicity detection.

C.

Use Amazon SageMaker built-in algorithms to train the model.

D.

Use Amazon Polly to monitor comments.

Use Amazon Comprehend toxicity detection.

A media company wants to analyze viewer behavior and demographics to recommend personalized content. The company wants to deploy a customized ML model in its production environment. The company also wants to observe if the model quality drifts over time.

Which AWS service or feature meets these requirements?

A.

Amazon Rekognition

B.

Amazon SageMaker Clarify

C.

Amazon Comprehend

D.

Amazon SageMaker Model Monitor

Amazon SageMaker Model Monitor

A company is deploying AI/ML models by using AWS services. The company wants to offer transparency into the models’ decision-making processes and provide explanations for the model outputs.

Which AWS service or feature meets these requirements?

A.

Amazon SageMaker Model Cards

B.

Amazon Rekognition

C.

Amazon Comprehend

D.

Amazon Lex

Amazon SageMaker Model Cards

A manufacturing company wants to create product descriptions in multiple languages.

Which AWS service will automate this task?

A.

Amazon Translate

B.

Amazon Transcribe

C.

Amazon Kendra

D.

Amazon Polly

Amazon Translate

A company wants more customized responses to its generative AI models’ prompts.

Select the correct customization methodology from the following list for each use case. Each use case should be selected one time.

Model fine tuning

Data augmentation

Continued pre-training

Which AWS feature records details about ML instance data for governance and reporting?

A.

Amazon SageMaker Model Cards

B.

Amazon SageMaker Debugger

C.

Amazon SageMaker Model Monitor

D.

Amazon SageMaker JumpStart

Amazon SageMaker Model Cards

A financial company is using ML to help with some of the company’s tasks.

Which option is a use of generative AI models?

A.

Summarizing customer complaints

B.

Classifying customers based on product usage

C.

Segmenting customers based on type of investments

D.

Forecasting revenue for certain products

Summarizing customer complaints

A medical company wants to develop an AI application that can access structured patient records, extract relevant information, and generate concise summaries.

Which solution will meet these requirements?

A.

Use Amazon Comprehend Medical to extract relevant medical entities and relationships. Apply rule-based logic to structure and format summaries.

B.

Use Amazon Personalize to analyze patient engagement patterns. Integrate the output with a general purpose text summarization tool.

C.

Use Amazon Textract to convert scanned documents into digital text. Design a keyword extraction system to generate summaries.

D.

Implement Amazon Kendra to provide a searchable index for medical records. Use a template-based system to format summaries.

Use Amazon Comprehend Medical to extract relevant medical entities and relationships. Apply rule-based logic to structure and format summaries.

Which option describes embeddings in the context of AI?

A.

A method for compressing large datasets

B.

An encryption method for securing sensitive data

C.

A method for visualizing high-dimensional data

D.

A numerical method for data representation in a reduced dimensionality space

A numerical method for data representation in a reduced dimensionality space

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?

A.

Build a conversational chatbot by using Amazon Lex.

B.

Transcribe call recordings by using Amazon Transcribe.

C.

Extract information from call recordings by using Amazon SageMaker Model Monitor.

D.

Create classification labels by using Amazon Comprehend.

Transcribe call recordings by using Amazon Transcribe.

A company is building an AI application to summarize books of varying lengths. During testing, the application fails to summarize some books.

Why does the application fail to summarize some books?

A.

The temperature is set too high.

B.

The selected model does not support fine-tuning.

C.

The Top P value is too high.

D.

The input tokens exceed the model’s context size.

The input tokens exceed the model’s context size.

An airline company wants to build a conversational AI assistant to answer customer questions about flight schedules, booking, and payments. The company wants to use large language models (LLMs) and a knowledge base to create a text-based chatbot interface.

Which solution will meet these requirements with the LEAST development effort?

A.

Train models on Amazon SageMaker Autopilot.

B.

Develop a Retrieval Augmented Generation (RAG) agent by using Amazon Bedrock.

C.

Create a Python application by using Amazon Q Developer.

D.

Fine-tune models on Amazon SageMaker Jumpstart.

Develop a Retrieval Augmented Generation (RAG) agent by using Amazon Bedrock.

What is tokenization used for in natural language processing (NLP)?

A.

To encrypt text data

B.

To compress text files

C.

To break text into smaller units for processing

D.

To translate text between languages

To break text into smaller units for processing

Which option is a characteristic of transformer-based language models?

A.

Transformer-based language models use convolutional layers to apply filters across an input to capture local patterns through filtered views.

B.

Transformer-based language models can process only text data.

C.

Transformer-based language models use self-attention mechanisms to capture contextual relationships.

D.

Transformer-based language models process data sequences one element at a time in cyclic iterations.

Transformer-based language models use self-attention mechanisms to capture contextual relationships.

A financial company is using AI systems to obtain customer credit scores as part of the loan application process. The company wants to expand to a new market in a different geographic area. The company must ensure that it can operate in that geographic area.

Which compliance laws should the company review?

A.

Local health data protection laws

B.

Local payment card data protection laws

C.

Local education privacy laws

D.

Local algorithm accountability laws

Local algorithm accountability laws

A company uses Amazon Bedrock for its generative AI application. The company wants to use Amazon Bedrock Guardrails to detect and filter harmful user inputs and model-generated outputs.

Which content categories can the guardrails filter? (Choose two.)

A.

Hate

B.

Politics

C.

Violence

D.

Gambling

E.

Religion

Hate
Violence

Which scenario describes a potential risk and limitation of prompt engineering in the context of a generative AI model?

A.

Prompt engineering does not ensure that the model always produces consistent and deterministic outputs, eliminating the need for validation.

B.

Prompt engineering could expose the model to vulnerabilities such as prompt injection attacks.

C.

Properly designed prompts reduce but do not eliminate the risk of data poisoning or model hijacking.

D.

Prompt engineering does not ensure that the model will consistently generate highly reliable outputs when working with real-world data.

Prompt engineering could expose the model to vulnerabilities such as prompt injection attacks.

A publishing company built a Retrieval Augmented Generation (RAG) based solution to give its users the ability to interact with published content. New content is published daily. The company wants to provide a near real-time experience to users.

Which steps in the RAG pipeline should the company implement by using offline batch processing to meet these requirements? (Choose two.)

A.

Generation of content embeddings

B.

Generation of embeddings for user queries

C.

Creation of the search index

D.

Retrieval of relevant content

E.

Response generation for the user

Generation of content embeddings
Generation of content embeddings

Which technique breaks a complex task into smaller subtasks that are sent sequentially to a large language model (LLM)?

A.

One-shot prompting

B.

Prompt chaining

C.

Tree of thoughts

D.

Retrieval Augmented Generation (RAG)

Prompt chaining

An AI practitioner needs to improve the accuracy of a natural language generation model. The model uses rapidly changing inventory data.

Which technique will improve the model's accuracy?

A.

Transfer learning

B.

Federated learning

C.

Retrieval Augmented Generation (RAG)

D.

One-shot prompting

Retrieval Augmented Generation (RAG)

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?

A.

Supervised learning

B.

Unsupervised learning

C.

Reinforcement learning

D.

Reinforcement learning from human feedback (RLHF)

Unsupervised learning

A company wants to collaborate with several research institutes to develop an AI model. The company needs standardized documentation of model version tracking and a record of model development.

Which solution meets these requirements?

A.

Track the model changes by using Git.

B.

Track the model changes by using Amazon Fraud Detector.

C.

Track the model changes by using Amazon SageMaker Model Cards.

D.

Track the model changes by using Amazon Comprehend.

Track the model changes by using Amazon SageMaker Model Cards.

A company that uses multiple ML models wants to identify changes in original model quality so that the company can resolve any issues.

Which AWS service or feature meets these requirements?

A.

Amazon SageMaker JumpStart

B.

Amazon SageMaker HyperPod

C.

Amazon SageMaker Data Wrangler

D.

Amazon SageMaker Model Monitor

Amazon SageMaker Model Monitor

What is the purpose of chunking in Retrieval Augmented Generation (RAG)?

A.

To avoid database storage limitations for large text documents by storing parts or chunks of the text

B.

To improve efficiency by avoiding the need to convert large text into vector embeddings

C.

To improve the contextual relevancy of results retrieved from the vector index

D.

To decrease the cost of storage by storing parts or chunks of the text

To improve the contextual relevancy of results retrieved from the vector index

A company is developing an editorial assistant application that uses generative AI. During the pilot phase, usage is low and application performance is not a concern. The company cannot predict application usage after the application is fully deployed and wants to minimize application costs.

Which solution will meet these requirements?

A.

Use GPU-powered Amazon EC2 instances.

B.

Use Amazon Bedrock with Provisioned Throughput.

C.

Use Amazon Bedrock with On-Demand Throughput.

D.

Use Amazon SageMaker JumpStart.

Use Amazon Bedrock with On-Demand Throughput.

A company deployed a Retrieval Augmented Generation (RAG) application on Amazon Bedrock that gathers financial news to distribute in daily newsletters. Users have recently reported politically influenced ideas in the newsletters.

Which Amazon Bedrock guardrail can identify and filter this content?

A.

Word filters

B.

Denied topics

C.

Sensitive information filters

D.

Content filters

Denied topics

A financial company is developing a fraud detection system that flags potential fraud cases in credit card transactions. Employees will evaluate the flagged fraud cases. The company wants to minimize the amount of time the employees spend reviewing flagged fraud cases that are not actually fraudulent.

Which evaluation metric meets these requirements?

A.

Recall

B.

Accuracy

C.

Precision

D.

Lift chart

Precision

A company designed an AI-powered agent to answer customer inquiries based on product manuals.

Which strategy can improve customer confidence levels in the AI-powered agent's responses?

A.

Writing the confidence level in the response

B.

Including referenced product manual links in the response

C.

Designing an agent avatar that looks like a computer

D.

Training the agent to respond in the company's language style

Including referenced product manual links in the response

A hospital developed an AI system to provide personalized treatment recommendations for patients. The AI system must provide the rationale behind the recommendations and make the insights accessible to doctors and patients.

Which human-centered design principle does this scenario present?

A.

Explainability

B.

Privacy and security

C.

Fairness

D.

Data governance

Explainability

Which statement presents an advantage of using Retrieval Augmented Generation (RAG) for natural language processing (NLP) tasks?

A.

RAG can use external knowledge sources to generate more accurate and informative responses.

B.

RAG is designed to improve the speed of language model training.

C.

RAG is primarily used for speech recognition tasks.

D.

RAG is a technique for data augmentation in computer vision tasks.

RAG can use external knowledge sources to generate more accurate and informative responses.

A company has created a custom model by fine-tuning an existing large language model (LLM) from Amazon Bedrock. The company wants to deploy the model to production and use the model to handle a steady rate of requests each minute.

Which solution meets these requirements MOST cost-effectively?

A.

Deploy the model by using an Amazon EC2 compute optimized instance.

B.

Use the model with on-demand throughput on Amazon Bedrock.

C.

Store the model in Amazon S3 and host the model by using AWS Lambda.

D.

Purchase Provisioned Throughput for the model on Amazon Bedrock.

Purchase Provisioned Throughput for the model on Amazon Bedrock.

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

Multi-modal embedding model

Which technique involves training AI models on labeled datasets to adapt the models to specific industry terminology and requirements?

A.

Data augmentation

B.

Fine-tuning

C.

Model quantization

D.

Continuous pre-training

Fine-tuning

A company is creating an agent for its application by using Amazon Bedrock Agents. The agent is performing well, but the company wants to improve the agent’s accuracy by providing some specific examples.

Which solution meets these requirements?

A.

Modify the advanced prompts for the agent to include the examples.

B.

Create a guardrail for the agent that includes the examples.

C.

Use Amazon SageMaker Ground Truth to label the examples.

D.

Run a script in AWS Lambda that adds the examples to the training dataset.

Modify the advanced prompts for the agent to include the examples.

Which option is a benefit of using infrastructure as code (IaC) in machine learning operations (MLOps)?

A.

IaC eliminates the need for hyperparameter tuning.

B.

IaC always provisions powerful compute instances, contributing to the training of more accurate models.

C.

IaC streamlines the deployment of scalable and consistent ML workloads in cloud environments.

D.

IaC minimizes overall expenses by deploying only low-cost instances

IaC streamlines the deployment of scalable and consistent ML workloads in cloud environments.

A company wants to fine-tune a foundation model (FM) to answer questions for a specific domain. The company wants to use instruction-based fine-tuning.

How should the company prepare the training data?

A.

Gather company internal documents and industry-specific materials. Merge the documents and materials into a single file.

B.

Collect external company reviews from various online sources. Manually label each review as either positive or negative.

C.

Create pairs of questions and answers that specifically address topics related to the company's industry domain.

D.

Create few-shot prompts to instruct the model to answer only domain knowledge.

Create pairs of questions and answers that specifically address topics related to the company's industry domain.

Which ML technique ensures data compliance and privacy when training AI models on AWS?

A.

Reinforcement learning

B.

Transfer learning

C.

Federated learning

D.

Unsupervised learning

Federated learning

A company needs to customize a base model that is hosted on Amazon Bedrock.

Select the correct model customization method from the following list of company requirements. Each model customization method should be selected one or more times.

Fine-tuning
Continued pre-training

Continued pre-training

A manufacturing company has an application that ingests consumer complaints from publicly available sources. The application uses complex hard-coded logic to process the complaints. The company wants to scale this logic across markets and product lines.

Which advantage do generative AI models offer for this scenario?

A.

Predictability of outputs

B.

Adaptability

C.

Less sensitivity to changes in inputs

D.

Explainability

Adaptability

A financial company wants to flag all credit card activity as possibly fraudulent or non-fraudulent based on transaction data.

Which type of ML model meets these requirements?

A.

Regression

B.

Diffusion

C.

Binary classification

D.

Multi-class classification

Binary classification

A company is designing a customer service chatbot by using a fine-tuned large language model (LLM). The company wants to ensure that the chatbot uses responsible AI characteristics.

Select the correct responsible AI characteristic from the following list for each application design action. Each responsible AI characteristic should be selected one time or not at all.

Q1: Privacy and security

Q2: Transparency

Q3: Safety

A hospital wants to use a generative AI solution with speech-to-text functionality to help improve employee skills in dictating clinical notes.

Which AWS service meets these requirements?

A.

Amazon Q Developer

B.

Amazon Polly

C.

Amazon Rekognition

D.

AWS HealthScribe

AWS HealthScribe