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

Demo: 53 questions
Total 177 questions

AWS Certified AI Practitioner Exam Questions and Answers

Question 1

Which option is a use case for generative AI models?

Options:

A.

Improving network security by using intrusion detection systems

B.

Creating photorealistic images from text descriptions for digital marketing

C.

Enhancing database performance by using optimized indexing

D.

Analyzing financial data to forecast stock market trends

Question 2

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?

Options:

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).

Question 3

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 4

A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources.

Which solution will meet this requirement?

Options:

A.

Use a different FM

B.

Choose a lower temperature value

C.

Create an Amazon Bedrock knowledge base

D.

Enable model invocation logging

Question 5

A company wants to deploy a conversational chatbot to answer customer questions. The chatbot is based on a fine-tuned Amazon SageMaker JumpStart model. The application must comply with multiple regulatory frameworks.

Which capabilities can the company show compliance for? (Select TWO.)

Options:

A.

Auto scaling inference endpoints

B.

Threat detection

C.

Data protection

D.

Cost optimization

E.

Loosely coupled microservices

Question 6

A company deployed a model to production. After 4 months, the model inference quality degraded. The company wants to receive a notification if the model inference quality degrades. The company also wants to ensure that the problem does not happen again.

Which solution will meet these requirements?

Options:

A.

Retrain the model. Monitor model drift by using Amazon SageMaker Clarify.

B.

Retrain the model. Monitor model drift by using Amazon SageMaker Model Monitor.

C.

Build a new model. Monitor model drift by using Amazon SageMaker Feature Store.

D.

Build a new model. Monitor model drift by using Amazon SageMaker JumpStart.

Question 7

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

Options:

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.

Question 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?

Options:

A.

Text data

B.

Image data

C.

Time series data

D.

Binary data

Question 9

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

Options:

A.

Data augmentation

B.

Fine-tuning

C.

Model quantization

D.

Continuous pre-training

Question 10

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

Which AWS service will automate this task?

Options:

A.

Amazon Translate

B.

Amazon Transcribe

C.

Amazon Kendra

D.

Amazon Polly

Question 11

Which AWS service or feature can help an AI development team quickly deploy and consume a foundation model (FM) within the team's VPC?

Options:

A.

Amazon Personalize

B.

Amazon SageMaker JumpStart

C.

PartyRock, an Amazon Bedrock Playground

D.

Amazon SageMaker endpoints

Question 12

A student at a university is copying content from generative AI to write essays.

Which challenge of responsible generative AI does this scenario represent?

Options:

A.

Toxicity

B.

Hallucinations

C.

Plagiarism

D.

Privacy

Question 13

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?

Options:

A.

Amazon Rekognition

B.

Amazon SageMaker Clarify

C.

Amazon Comprehend

D.

Amazon SageMaker Model Monitor

Question 14

Why does overfilting occur in ML models?

Options:

A.

The training dataset does not reptesent all possible input values.

B.

The model contains a regularization method.

C.

The model training stops early because of an early stopping criterion.

D.

The training dataset contains too many features.

Question 15

A company has a database of petabytes of unstructured data from internal sources. The company wants to transform this data into a structured format so that its data scientists can perform machine learning (ML) tasks.

Which service will meet these requirements?

Options:

A.

Amazon Lex

B.

Amazon Rekognition

C.

Amazon Kinesis Data Streams

D.

AWS Glue

Question 16

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

Options:

A.

Feature engineering

B.

Model training

C.

Data collection

D.

Business goal identification

Question 17

HOTSPOT

Select the correct AI term from the following list for each statement. Each AI term should be selected one time. (Select THREE.)

• AI

• Deep learning

• ML

Options:

Question 18

A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.

What should the company do to meet these requirements?

Options:

A.

Evaluate the models by using built-in prompt datasets.

B.

Evaluate the models by using a human workforce and custom prompt datasets.

C.

Use public model leaderboards to identify the model.

D.

Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models.

Question 19

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 20

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?

Options:

A.

Batch learning

B.

Continuous pre-training

C.

Static training

D.

Latent training

Question 21

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 22

A retail company is tagging its product inventory. A tag is automatically assigned to each product based on the product description. The company created one product category by using a large language model (LLM) on Amazon Bedrock in few-shot learning mode.

The company collected a labeled dataset and wants to scale the solution to all product categories.

Which solution meets these requirements?

Options:

A.

Use prompt engineering with zero-shot learning.

B.

Use prompt engineering with prompt templates.

C.

Customize the model with continued pre-training.

D.

Customize the model with fine-tuning.

Question 23

A company is building a large language model (LLM) question answering chatbot. The company wants to decrease the number of actions call center employees need to take to respond to customer questions.

Which business objective should the company use to evaluate the effect of the LLM chatbot?

Options:

A.

Website engagement rate

B.

Average call duration

C.

Corporate social responsibility

D.

Regulatory compliance

Question 24

A company stores millions of PDF documents in an Amazon S3 bucket. The company needs to extract the text from the PDFs, generate summaries of the text, and index the summaries for fast searching.

Which combination of AWS services will meet these requirements? (Select TWO.)

Options:

A.

Amazon Translate

B.

Amazon Bedrock

C.

Amazon Transcribe

D.

Amazon Polly

E.

Amazon Textract

Question 25

A company is developing an ML application. The application must automatically group similar customers and products based on their characteristics.

Which ML strategy should the company use to meet these requirements?

Options:

A.

Unsupervised learning

B.

Supervised learning

C.

Reinforcement learning

D.

Semi-supervised learning

Question 26

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?

Options:

A.

Measurement bias

B.

Sampling bias

C.

Observer bias

D.

Confirmation bias

Question 27

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 28

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?

Options:

A.

Amazon SageMaker Feature Store

B.

Amazon SageMaker Data Wrangler

C.

Amazon SageMaker Clarify

D.

Amazon SageMaker Model Cards

Question 29

Which option is an example of unsupervised learning?

Options:

A.

A model that groups customers based on their purchase history

B.

A model that classifies images as dogs or cats

C.

A model that predicts a house's price based on various features

D.

A model that learns to play chess by using trial and error

Question 30

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?

Options:

A.

Amazon S3

B.

Amazon Elastic Block Store (Amazon EBS)

C.

Amazon Elastic File System (Amazon EFS)

D.

AWS Snowcone

Question 31

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?

Options:

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.

Question 32

An AI practitioner is building a model to generate images of humans in various professions. The AI practitioner discovered that the input data is biased and that specific attributes affect the image generation and create bias in the model.

Which technique will solve the problem?

Options:

A.

Data augmentation for imbalanced classes

B.

Model monitoring for class distribution

C.

Retrieval Augmented Generation (RAG)

D.

Watermark detection for images

Question 33

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 34

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?

Options:

A.

Batch transform

B.

Real-time inference

C.

Serverless inference

D.

Asynchronous inference

Question 35

A bank is fine-tuning a large language model (LLM) on Amazon Bedrock to assist customers with questions about their loans. The bank wants to ensure that the model does not reveal any private customer data.

Which solution meets these requirements?

Options:

A.

Use Amazon Bedrock Guardrails.

B.

Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM.

C.

Increase the Top-K parameter of the LLM.

D.

Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM.

Question 36

Which scenario represents a practical use case for generative AI?

Options:

A.

Using an ML model to forecast product demand

B.

Employing a chatbot to provide human-like responses to customer queries in real time

C.

Using an analytics dashboard to track website traffic and user behavior

D.

Implementing a rule-based recommendation engine to suggest products to customers

Question 37

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 38

A pharmaceutical company wants to analyze user reviews of new medications and provide a concise overview for each medication. Which solution meets these requirements?

Options:

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.

Question 39

A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns. The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.

Which solution meets these requirements?

Options:

A.

Optimize the model's architecture and hyperparameters to improve the model's overall performance.

B.

Increase the model's complexity by adding more layers to the model's architecture.

C.

Create effective prompts that provide clear instructions and context to guide the model's generation.

D.

Select a large, diverse dataset to pre-train a new generative model.

Question 40

A company wants to create an application to summarize meetings by using meeting audio recordings.

Select and order the correct steps from the following list to create the application. Each step should be selected one time or not at all. (Select and order THREE.)

• Convert meeting audio recordings to meeting text files by using Amazon Polly.

• Convert meeting audio recordings to meeting text files by using Amazon Transcribe.

• Store meeting audio recordings in an Amazon S3 bucket.

• Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.

• Summarize meeting text files by using Amazon Bedrock.

• Summarize meeting text files by using Amazon Lex.

Options:

Question 41

Which strategy evaluates the accuracy of a foundation model (FM) that is used in image classification tasks?

Options:

A.

Calculate the total cost of resources used by the model.

B.

Measure the model's accuracy against a predefined benchmark dataset.

C.

Count the number of layers in the neural network.

D.

Assess the color accuracy of images processed by the model.

Question 42

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?

Options:

A.

Fine-tune the FM.

B.

Retrain the FM.

C.

Train a new FM.

D.

Use prompt engineering.

Question 43

An ecommerce company is using a chatbot to automate the customer order submission process. The chatbot is powered by AI and Is available to customers directly from the company's website 24 hours a day, 7 days a week.

Which option is an AI system input vulnerability that the company needs to resolve before the chatbot is made available?

Options:

A.

Data leakage

B.

Prompt injection

C.

Large language model (LLM) hallucinations

D.

Concept drift

Question 44

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?

Options:

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.

Question 45

Which option describes embeddings in the context of AI?

Options:

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

Question 46

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?

Options:

A.

Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.

B.

Set the access permissions for the S3 buckets to allow public access to enable access over the internet.

C.

Use prompt engineering techniques to tell the model to look for information in Amazon S3.

D.

Ensure that the S3 data does not contain sensitive information.

Question 47

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?

Options:

A.

Few-shot prompting

B.

Zero-shot prompting

C.

Directional stimulus prompting

D.

Chain-of-thought prompting

Question 48

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?

Options:

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.

Question 49

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

How can the company decrease hallucinations?

Options:

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.

Question 50

Which functionality does Amazon SageMaker Clarify provide?

Options:

A.

Integrates a Retrieval Augmented Generation (RAG) workflow

B.

Monitors the quality of ML models in production

C.

Documents critical details about ML models

D.

Identifies potential bias during data preparation

Question 51

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?

Options:

A.

Data residency

B.

Data quality

C.

Data discoverability

D.

Data enrichment

Question 52

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 53

An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.

Which AWS service can the company use to meet this requirement?

Options:

A.

AWS Audit Manager

B.

AWS Artifact

C.

AWS Trusted Advisor

D.

AWS Data Exchange

Demo: 53 questions
Total 177 questions