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Amazon Web Services AIF-C01 AWS Certified AI Practitioner Exam(AI1-C01) Exam Practice Test

Demo: 26 questions
Total 87 questions

AWS Certified AI Practitioner Exam(AI1-C01) Questions and Answers

Question 1

A security company is using Amazon Bedrock to run foundation models (FMs). The company wants to ensure that only authorized users invoke the models. The company needs to identify any unauthorized access attempts to set appropriate AWS Identity and Access Management (IAM) policies and roles for future iterations of the FMs.

Which AWS service should the company use to identify unauthorized users that are trying to access Amazon Bedrock?

Options:

A.

AWS Audit Manager

B.

AWS CloudTrail

C.

Amazon Fraud Detector

D.

AWS Trusted Advisor

Question 2

A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text.

Which type of model meets this requirement?

Options:

A.

Topic modeling

B.

Clustering models

C.

Prescriptive ML models

D.

BERT-based models

Question 3

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?

Options:

A.

Build an automatic named entity recognition system.

B.

Create a recommendation engine.

C.

Develop a summarization chatbot.

D.

Develop a multi-language translation system.

Question 4

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?

Options:

A.

Implement moderation APIs.

B.

Retrain the model with a general public dataset.

C.

Perform model validation.

D.

Automate user feedback integration.

Question 5

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?

Options:

A.

Confusion matrix

B.

Correlation matrix

C.

R2 score

D.

Mean squared error (MSE)

Question 6

A company wants to develop a large language model (LLM) application by using Amazon Bedrock and customer data that is uploaded to Amazon S3. The company's security policy states that each team can access data for only the team's own customers.

Which solution will meet these requirements?

Options:

A.

Create an Amazon Bedrock custom service role for each team that has access to only the team's customer data.

B.

Create a custom service role that has Amazon S3 access. Ask teams to specify the customer name on each Amazon Bedrock request.

C.

Redact personal data in Amazon S3. Update the S3 bucket policy to allow team access to customer data.

D.

Create one Amazon Bedrock role that has full Amazon S3 access. Create IAM roles for each team that have access to only each team's customer folders.

Question 7

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?

Options:

A.

Build a speech recognition system.

B.

Create a natural language processing (NLP) named entity recognition system.

C.

Develop an anomaly detection system.

D.

Create a fraud forecasting system.

Question 8

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?

Options:

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.

Question 9

A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.

Which AWS solution should the company use to automate the generation of graphs?

Options:

A.

Amazon Q in Amazon EC2

B.

Amazon Q Developer

C.

Amazon Q in Amazon QuickSight

D.

Amazon Q in AWS Chatbot

Question 10

A social media company wants to use a large language model (LLM) for content moderation. The company wants to evaluate the LLM outputs for bias and potential discrimination against specific groups or individuals.

Which data source should the company use to evaluate the LLM outputs with the LEAST administrative effort?

Options:

A.

User-generated content

B.

Moderation logs

C.

Content moderation guidelines

D.

Benchmark datasets

Question 11

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?

Options:

A.

Amazon Lex

B.

Amazon Comprehend

C.

Amazon Transcribe

D.

Amazon Translate

Question 12

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?

Options:

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

Question 13

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?

Options:

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

Question 14

A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteria. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers.

Which actions should the company take to meet these requirements? (Select TWO.)

Options:

A.

Detect imbalances or disparities in the data.

B.

Ensure that the model runs frequently.

C.

Evaluate the model's behavior so that the company can provide transparency to stakeholders.

D.

Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate.

E.

Ensure that the model's inference time is within the accepted limits.

Question 15

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?

Options:

A.

Adjust the prompt.

B.

Choose an LLM of a different size.

C.

Increase the temperature.

D.

Increase the Top K value.

Question 16

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 on internal data and external data.

Which solution will meet these requirements?

Options:

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.

Question 17

A company wants to use a large language model (LLM) to develop a conversational agent. The company needs to prevent the LLM from being manipulated with common prompt engineering techniques to perform undesirable actions or expose sensitive information.

Which action will reduce these risks?

Options:

A.

Create a prompt template that teaches the LLM to detect attack patterns.

B.

Increase the temperature parameter on invocation requests to the LLM.

C.

Avoid using LLMs that are not listed in Amazon SageMaker.

D.

Decrease the number of input tokens on invocations of the LLM.

Question 18

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?

Options:

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.

Question 19

A company needs to build its own large language model (LLM) based on only the company's private data. The company is concerned about the environmental effect of the training process.

Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?

Options:

A.

Amazon EC2 C series

B.

Amazon EC2 G series

C.

Amazon EC2 P series

D.

Amazon EC2 Trn series

Question 20

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?

Options:

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.

Question 21

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?

Options:

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.

Question 22

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?

Options:

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.

Question 23

A company is building a chatbot to improve user experience. The company is using a large language model (LLM) from Amazon Bedrock for intent detection. The company wants to use few-shot learning to improve intent detection accuracy.

Which additional data does the company need to meet these requirements?

Options:

A.

Pairs of chatbot responses and correct user intents

B.

Pairs of user messages and correct chatbot responses

C.

Pairs of user messages and correct user intents

D.

Pairs of user intents and correct chatbot responses

Question 24

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?

Options:

A.

Multi-modal embedding model

B.

Text embedding model

C.

Multi-modal generation model

D.

Image generation model

Question 25

A company wants to build an interactive application for children that generates new stories based on classic stories. The company wants to use Amazon Bedrock and needs to ensure that the results and topics are appropriate for children.

Which AWS service or feature will meet these requirements?

Options:

A.

Amazon Rekognition

B.

Amazon Bedrock playgrounds

C.

Guardrails for Amazon Bedrock

D.

Agents for Amazon Bedrock

Question 26

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?

Options:

A.

Number of tokens consumed

B.

Temperature value

C.

Amount of data used to train the LLM

D.

Total training time

Demo: 26 questions
Total 87 questions