Quiz Amazon AWS Certified AI Practitioner

When a model is trained on a dataset containing confidential or sensitive data, the model may
inadvertently learn patterns from this data, which could then be reflected in its inference responses.
To ensure that a model does not generate responses based on confidential data, the most effective
approach is to remove the confidential data from the training dataset and then retrain the model.
Explanation of Each Option:
Option A (Correct): "Delete the custom model. Remove the confidential data from the training
dataset. Retrain the custom model."This option is correct because it directly addresses the core issue:
the model has been trained on confidential data. The only way to ensure that the model does not
produce inferences based on this data is to remove the confidential information from the training
dataset and then retrain the model from scratch. Simply deleting the model and retraining it ensures
that no confidential data is learned or retained by the model. This approach follows the best
practices recommended by AWS for handling sensitive data when using machine learning services
like Amazon Bedrock.
Option B: "Mask the confidential data in the inference responses by using dynamic data
masking."This option is incorrect because dynamic data masking is typically used to mask or
obfuscate sensitive data in a database. It does not address the core problem of the model
beingtrained on confidential data. Masking data in inference responses does not prevent the model
from using confidential data it learned during training.
Option C: "Encrypt the confidential data in the inference responses by using Amazon
SageMaker."This option is incorrect because encrypting the inference responses does not prevent the
model from generating outputs based on confidential data. Encryption only secures the data at rest
or in transit but does not affect the model's underlying knowledge or training process.
Option D: "Encrypt the confidential data in the custom model by using AWS Key Management Service
(AWS KMS)."This option is incorrect as well because encrypting the data within the model does not
prevent the model from generating responses based on the confidential data it learned during
training. AWS KMS can encrypt data, but it does not modify the learning that the model has already
performed.
AWS AI Practitioner Reference:
Data Handling Best Practices in AWS Machine Learning: AWS advises practitioners to carefully handle
training data, especially when it involves sensitive or confidential information. This includes
preprocessing steps like data anonymization or removal of sensitive data before using it to train
machine learning models.
Amazon Bedrock and Model Training Security: Amazon Bedrock provides foundational models and
customization capabilities, but any training involving sensitive data should follow best practices, such
as removing or anonymizing confidential data to prevent unintended data leakage.

Amazon OpenSearch Service (formerly Amazon Elasticsearch Service) has introduced capabilities to
support vector search, which allows companies to build vector database applications. This is
particularly useful in machine learning, where vector representations (embeddings) of data are often
used to capture semantic meaning.
Scalable index management and nearest neighbor search capability are the core features enabling
vector database functionalities in OpenSearch. The service allows users to index high-dimensional
vectors and perform efficient nearest neighbor searches, which are crucial for tasks such as
recommendation systems, anomaly detection, and semantic search.
Here is why option C is the correct answer:
Scalable Index Management: OpenSearch Service supports scalable indexing of vector data. This
means you can index a large volume of high-dimensional vectors and manage these indexes in a cost-
effective and performance-optimized way. The service leverages underlying AWS infrastructure to
ensure that indexing scales seamlessly with data size.
Nearest Neighbor Search Capability: OpenSearch Service's nearest neighbor search capability allows
for fast and efficient searches over vector data. This is essential for applications like product
recommendation engines, where the system needs to quickly find the most similar items based on a
user's query or behavior.
AWS AI Practitioner Reference:
According to AWS documentation, OpenSearch Service's support for nearest neighbor search using
vector embeddings is a key feature for companies building machine learning applications that
require similarity search.
The service uses Approximate Nearest Neighbors (ANN) algorithms to speed up searches over large
datasets, ensuring high performance even with large-scale vector data.
The other options do not directly relate to building vector database applications:
A . Integration with Amazon S3 for object storage is about storing data objects, not vector-based
searching or indexing.
B . Support for geospatial indexing and queries is related to location-based data, not vectors used in
machine learning.
D . Ability to perform real-time analysis on streaming data relates to analyzing incoming data
streams, which is different from the vector search capabilities.

Amazon QuickSight is a fully managed business intelligence (BI) service that allows users to create
and publish interactive dashboards that include visualizations like graphs, charts, and tables.
"Amazon Q" is the natural language query feature within Amazon QuickSight. It enables users to ask
questions about their data in natural language and receive visual responses such as graphs.
Option C (Correct): "Amazon Q in Amazon QuickSight": This is the correct answer because Amazon
QuickSight Q is specifically designed to allow users to explore their data through natural language
queries, and it can automatically generate graphs to display sales data and other metrics. This makes
it an ideal choice for the company to automate the generation of graphs showing total sales for its
top-selling products across various retail locations.
Option A, B, and D: These options are incorrect:
A . Amazon Q in Amazon EC2: Amazon EC2 is a compute service that provides virtual servers, but it is
not directly related to generating graphs or providing natural language querying features.
B . Amazon Q Developer: This is not an existing AWS service or feature.
D . Amazon Q in AWS Chatbot: AWS Chatbot is a service that integrates with Amazon Chime and
Slack for monitoring and managing AWS resources, but it is not used for generating graphs based on
sales data.
AWS AI Practitioner Reference:
Amazon QuickSight Q is designed to provide insights from data by using natural language queries,
making it a powerful tool for generating automated graphs and visualizations directly from queried
data.
Business Intelligence (BI) on AWS: AWS services such as Amazon QuickSight provide business
intelligence capabilities, including automated reporting and visualization features, which are ideal
for companies seeking to visualize data like sales trends over time.

Amazon Bedrock is a service that provides foundational models for building generative AI
applications. When creating an application for children, it is crucial to ensure that the generated
content is appropriate for the target audience. "Guardrails" in Amazon Bedrock provide mechanisms
to control the outputs and topics of generated content to align with desired safety standards and
appropriateness levels.
Option C (Correct): "Guardrails for Amazon Bedrock": This is the correct answer because guardrails
are specifically designed to help users enforce content moderation, filtering, and safety checks on
the outputs generated by models in Amazon Bedrock. For a children’s application, guardrails ensure
that all content generated is suitable and appropriate for the intended audience.
Option A: "Amazon Rekognition" is incorrect. Amazon Rekognition is an image and video analysis
service that can detect inappropriate content in images or videos, but it does not handle text or story
generation.
Option B: "Amazon Bedrock playgrounds" is incorrect because playgrounds are environments for
experimenting and testing model outputs, but they do not inherently provide safeguards to ensure
content appropriateness for specific audiences, such as children.
Option D: "Agents for Amazon Bedrock" is incorrect. Agents in Amazon Bedrock facilitate building AI
applications with more interactive capabilities, but they do not provide specific guardrails for
ensuring content appropriateness for children.
AWS AI Practitioner Reference:
Guardrails in Amazon Bedrock: Designed to help implement controls that ensure generated content
is safe and suitable for specific use cases or audiences, such as children, by moderating and filtering
inappropriate or undesired content.
Building Safe AI Applications: AWS provides guidance on implementing ethical AI practices, including
using guardrails to protect against generating inappropriate or biased content.

Amazon SageMaker Serverless Inference is the correct solution for deploying an ML model to
production in a way that allows a web application to use the model without the need to manage the
underlying infrastructure.
Amazon SageMaker Serverless Inference provides a fully managed environment for deploying
machine learning models. It automatically provisions, scales, and manages the infrastructure
required to host the model, removing the need for the company to manage servers or other
underlying infrastructure.
Why Option A is Correct:
No Infrastructure Management: SageMaker Serverless Inference handles the infrastructure
management for deploying and serving ML models. The company can simply provide the model and
specify the required compute capacity, and SageMaker will handle the rest.
Cost-Effectiveness: The serverless inference option is ideal for applications with intermittent or
unpredictable traffic, as the company only pays for the compute time consumed while handling
requests.
Integration with Web Applications: This solution allows the model to be easily accessed by web
applications via RESTful APIs, making it an ideal choice for hosting the model and serving predictions.
Why Other Options are Incorrect:
B . Use Amazon CloudFront to deploy the model: CloudFront is a content delivery network (CDN)
service for distributing content, not for deploying ML models or serving predictions.
C . Use Amazon API Gateway to host the model and serve predictions: API Gateway is used for
creating, deploying, and managing APIs, but it does not provide the infrastructure or the required
environment to host and run ML models.
D . Use AWS Batch to host the model and serve predictions: AWS Batch is designed for running batch
computing workloads and is not optimized for real-time inference or hosting machine learning
models.
Thus, A is the correct answer, as it aligns with the requirement of deploying an ML model without
managing any underlying infrastructure.

Unsupervised learning is optimal for classifying data without predefined labels, allowing for natural grouping, which is ideal for tier classification in this case.

Partial dependence plots help visualize the model's decision-making, thereby increasing transparency and explainability.

Generative AI models can produce images from text descriptions, making them essential for marketing and similar domains.

Hallucination occurs when an AI model generates incorrect or misleading content, which is a common issue when content is plausible but inaccurate.

To build an AI model responsibly and minimize bias, it is essential to ensure fairness and
transparency throughout the model development and deployment process. This involves detecting
and mitigating data imbalances and thoroughly evaluating the model's behavior to understand its
impact on different groups.
Option A (Correct): "Detect imbalances or disparities in the data": This is correct because identifying
and addressing data imbalances or disparities is a critical step in reducing bias. AWS provides tools
like Amazon SageMaker Clarify to detect bias during data preprocessing and model training.
Option C (Correct): "Evaluate the model's behavior so that the company can provide transparency to
stakeholders": This is correct because evaluating the model's behavior for fairness and accuracy is
key to ensuring that stakeholders understand how the model makes decisions. Transparency is a
crucial aspect of responsible AI.
Option B: "Ensure that the model runs frequently" is incorrect because the frequency of model runs
does not address bias.
Option D: "Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure
that the model is 100% accurate" is incorrect because ROUGE is a metric for evaluating the quality of
text summarization models, not for minimizing bias.
Option E: "Ensure that the model's inference time is within the accepted limits" is incorrect as it
relates to performance, not bias reduction.
AWS AI Practitioner Reference:
Amazon SageMaker Clarify: AWS offers tools such as SageMaker Clarify for detecting bias in datasets
and models, and for understanding model behavior to ensure fairness and transparency.
Responsible AI Practices: AWS promotes responsible AI by advocating for fairness, transparency, and
inclusivity in model development and deployment.