A medical company is customizing a foundation model (FM) for diagnostic purposes. The company
needs the model to be transparent and explainable to meet regulatory requirements.
Which solution will meet these requirements?
B
Explanation:
Amazon SageMaker Clarify provides transparency and explainability for machine learning models by
generating metrics, reports, and examples that help to understand model predictions. For a medical
company that needs a foundation model to be transparent and explainable to meet regulatory
requirements, SageMaker Clarify is the most suitable solution.
Amazon SageMaker Clarify:
It helps in identifying potential bias in the data and model, and also explains model behavior by
generating feature attributions, providing insights into which features are most influential in the
model's predictions.
These capabilities are critical in medical applications where regulatory compliance often mandates
transparency and explainability to ensure that decisions made by the model can be trusted and
audited.
Why Option B is Correct:
Transparency and Explainability: SageMaker Clarify is explicitly designed to provide insights into
machine learning models' decision-making processes, helping meet regulatory requirements by
explaining why a model made a particular prediction.
Compliance with Regulations: The tool is suitable for use in sensitive domains, such as healthcare,
where there is a need for explainable AI.
Why Other Options are Incorrect:
A . Amazon Inspector: Focuses on security assessments, not on explainability or model transparency.
C . Amazon Macie: Provides data security by identifying and protecting sensitive data, but does not
help in making models explainable.
D . Amazon Rekognition: Used for image and video analysis, not relevant to making models
explainable.
Thus, B is the correct answer for meeting transparency and explainability requirements for the
foundation model
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?
A
Explanation:
Amazon SageMaker Ground Truth Plus is a managed data labeling service that includes human-in-
the-loop (HITL) validation. This solution ensures high accuracy by involving human reviewers to
validate the annotations and reduce the risk of incorrect annotations.
Amazon SageMaker Ground Truth Plus:
It allows for the creation of high-quality training datasets with human oversight, which minimizes
errors in labeling and increases accuracy.
Human-in-the-loop workflows help verify the correctness of annotations, ensuring that generated
images for protective eyewear meet high-quality standards.
Why Option A is Correct:
High Accuracy: Human-in-the-loop validation provides the ability to catch and correct errors in
annotations, ensuring high-quality data.
Minimized Risk of Incorrect Annotations: Human review adds a layer of quality assurance, which is
especially important in use cases like generating precise images for protective eyewear.
Why Other Options are Incorrect:
B . Amazon Bedrock: Does not offer a knowledge base for data augmentation; it focuses on running
foundation models.
C . Amazon Rekognition: Provides image recognition and analysis, not a solution for minimizing
annotation errors.
D . Amazon QuickSight: A data visualization tool, not relevant to image annotation or generation
tasks.
Thus, A is the correct answer for generating high-accuracy images with minimized annotation risks.
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?
B
Explanation:
AWS CloudTrail is a service that enables governance, compliance, and operational and risk auditing
of your AWS account. It tracks API calls and identifies unauthorized access attempts to AWS
resources, including Amazon Bedrock.
AWS CloudTrail:
Provides detailed logs of all API calls made within an AWS account, including those to Amazon
Bedrock.
Can identify unauthorized access attempts by logging and monitoring the API calls, which helps in
setting appropriate IAM policies and roles.
Why Option B is Correct:
Monitoring and Security: CloudTrail logs all access requests and helps detect unauthorized access
attempts.
Auditing and Compliance: The logs can be used to audit user activity and enforce security measures.
Why Other Options are Incorrect:
A . AWS Audit Manager: Used for automating audit preparation, not for tracking real-time
unauthorized access attempts.
C . Amazon Fraud Detector: Designed to detect fraudulent online activities, not unauthorized access
to AWS services.
D . AWS Trusted Advisor: Provides best practice recommendations for AWS resources, not access
monitoring.
Thus, B is the correct answer for identifying unauthorized users attempting to access Amazon
Bedrock.
A company manually reviews all submitted resumes in PDF format. As the company grows, the
company expects the volume of resumes to exceed the company's review capacity. The company
needs an automated system to convert the PDF resumes into plain text format for additional
processing.
Which AWS service meets this requirement?
A
Explanation:
Amazon Textract is a service that automatically extracts text and data from scanned documents,
including PDFs. It is the best choice for converting resumes from PDF format to plain text for further
processing.
Amazon Textract:
Extracts text, forms, and tables from scanned documents accurately.
Ideal for automating the process of converting PDF resumes into plain text format.
Why Option A is Correct:
Automation of Text Extraction: Textract is designed to handle large volumes of documents and
convert them into machine-readable text, perfect for the company's need.
Scalability and Efficiency: Supports scalability to handle a growing volume of resumes as the
company expands.
Why Other Options are Incorrect:
B . Amazon Personalize: Used for creating personalized recommendations, not for text extraction.
C . Amazon Lex: A service for building conversational interfaces, not for processing documents.
D . Amazon Transcribe: Used for converting speech to text, not for extracting text from documents.
A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat
interface for the company's product manuals. The manuals are stored as PDF files.
Which solution meets these requirements MOST cost-effectively?
A
Explanation:
Using Amazon Bedrock with large language models (LLMs) allows for efficient utilization of AI to
answer queries based on context provided in product manuals. To achieve this cost-effectively, the
company should avoid unnecessary use of resources.
Option A (Correct): "Use prompt engineering to add one PDF file as context to the user prompt when
the prompt is submitted to Amazon Bedrock": This is the most cost-effective solution. By using
prompt engineering, only the relevant content from one PDF file is added as context to each query.
This approach minimizes the amount of data processed, which helps in reducing costs associated
with LLMs' computational requirements.
Option B: "Use prompt engineering to add all the PDF files as context to the user prompt when the
prompt is submitted to Amazon Bedrock" is incorrect. Including all PDF files would increase costs
significantly due to the large context size processed by the model.
Option C: "Use all the PDF documents to fine-tune a model with Amazon Bedrock" is incorrect. Fine-
tuning a model is more expensive than using prompt engineering, especially if done for multiple
documents.
Option D: "Upload PDF documents to an Amazon Bedrock knowledge base" is incorrect because
Amazon Bedrock does not have a built-in knowledge base feature for directly managing and querying
PDF documents.
AWS AI Practitioner Reference:
Prompt Engineering for Cost-Effective AI: AWS emphasizes the importance of using prompt
engineering to minimize costs when interacting with LLMs. By carefully selecting relevant context,
users can reduce the amount of data processed and save on expenses.
Which term describes the numerical representations of real-world objects and concepts that AI and
natural language processing (NLP) models use to improve understanding of textual information?
A
Explanation:
Embeddings are numerical representations of objects (such as words, sentences, or documents) that
capture the objects' semantic meanings in a form that AI and NLP models can easily understand.
These representations help models improve their understanding of textual information by
representing concepts in a continuous vector space.
Option A (Correct): "Embeddings": This is the correct term, as embeddings provide a way for models
to learn relationships between different objects in their input space, improving their understanding
and processing capabilities.
Option B: "Tokens" are pieces of text used in processing, but they do not capture semantic meanings
like embeddings do.
Option C: "Models" are the algorithms that use embeddings and other inputs, not the
representations themselves.
Option D: "Binaries" refer to data represented in binary form, which is unrelated to the concept of
embeddings.
AWS AI Practitioner Reference:
Understanding Embeddings in AI and NLP: AWS provides resources and tools, like Amazon
SageMaker, that utilize embeddings to represent data in formats suitable for machine learning
models.
A company is building an application that needs to generate synthetic data that is based on existing
data.
Which type of model can the company use to meet this requirement?
A
Explanation:
Generative adversarial networks (GANs) are a type of deep learning model used for generating
synthetic data based on existing datasets. GANs consist of two neural networks (a generator and a
discriminator) that work together to create realistic data.
Option A (Correct): "Generative adversarial network (GAN)": This is the correct answer because
GANs are specifically designed for generating synthetic data that closely resembles the real data they
are trained on.
Option B: "XGBoost" is a gradient boosting algorithm for classification and regression tasks, not for
generating synthetic data.
Option C: "Residual neural network" is primarily used for improving the performance of deep
networks, not for generating synthetic data.
Option D: "WaveNet" is a model architecture designed for generating raw audio waveforms, not
synthetic data in general.
AWS AI Practitioner Reference:
GANs on AWS for Synthetic Data Generation: AWS supports the use of GANs for creating synthetic
datasets, which can be crucial for applications like training machine learning models in environments
where real data is scarce or sensitive.
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?
C
Explanation:
Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating
code snippets, managing reference tracking, and handling open-source license tracking. These
features help developers by automating parts of the software development process.
Option A (Correct): "Create software snippets, reference tracking, and open-source license tracking":
This is the correct answer because these are key features that help developers streamline and
automate tasks, thus improving productivity.
Option B: "Run an application without provisioning or managing servers" is incorrect as it refers to
AWS Lambda or AWS Fargate, not Amazon Q Developer.
Option C: "Enable voice commands for coding and providing natural language search" is incorrect
because this is not a function of Amazon Q Developer.
Option D: "Convert audio files to text documents by using ML models" is incorrect as this refers to
Amazon Transcribe, not Amazon Q Developer.
AWS AI Practitioner Reference:
Amazon Q Developer Features: AWS documentation outlines how Amazon Q Developer supports
developers by offering features that reduce manual effort and improve efficiency.
A company wants to create an application by using Amazon Bedrock. The company has a limited
budget and prefers flexibility without long-term commitment.
Which Amazon Bedrock pricing model meets these requirements?
A
Explanation:
Amazon Bedrock offers an on-demand pricing model that provides flexibility without long-term
commitments. This model allows companies to pay only for the resources they use, which is ideal for
a limited budget and offers flexibility.
Option A (Correct): "On-Demand": This is the correct answer because on-demand pricing allows the
company to use Amazon Bedrock without any long-term commitments and to manage costs
according to their budget.
Option B: "Model customization" is a feature, not a pricing model.
Option C: "Provisioned Throughput" involves reserving capacity ahead of time, which might not offer
the desired flexibility and could lead to higher costs if the capacity is not fully used.
Option D: "Spot Instance" is a pricing model for EC2 instances and does not apply to Amazon
Bedrock.
AWS AI Practitioner Reference:
AWS Pricing Models for Flexibility: On-demand pricing is a key AWS model for services that require
flexibility and no long-term commitment, ensuring cost-effectiveness for projects with variable usage
patterns.
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?
D
Explanation:
Amazon SageMaker Canvas is a visual, no-code machine learning interface that allows users to build
machine learning models without having any coding experience or knowledge of machine learning
algorithms. It enables users to analyze internal and external data, and make predictions using a
guided interface.
Option D (Correct): "Import the data into Amazon SageMaker Canvas. Build ML models and demand
forecast predictions by selecting the values in the data from SageMaker Canvas": This is the correct
answer because SageMaker Canvas is designed for users without coding experience, providing a
visual interface to build predictive models with ease.
Option A: "Store the data in Amazon S3 and use SageMaker built-in algorithms" is incorrect because
it requires coding knowledge to interact with SageMaker's built-in algorithms.
Option B: "Import the data into Amazon SageMaker Data Wrangler" is incorrect. Data Wrangler is
primarily for data preparation and not directly focused on creating ML models without coding.
Option C: "Use Amazon Personalize Trending-Now recipe" is incorrect as Amazon Personalize is for
building recommendation systems, not for general demand forecasting.
AWS AI Practitioner Reference:
Amazon SageMaker Canvas Overview: AWS documentation emphasizes Canvas as a no-code solution
for building machine learning models, suitable for business analysts and users with no coding
experience.