Free Amazon Web Services AIF-C01 Practice Exam with Questions & Answers | Set: 4

The requirement is to identify an AWS database service that supports the storage and querying of embeddings (from a generative AI model) as vectors. Embeddings are typically high-dimensional numerical representations of data (e.g., text, images) used in AI applications like conversational search. The database must support vector storage and efficient vector similarity searches. Let’s evaluate each option:

A. Amazon Athena: Amazon Athena is a serverless query service for analyzing data in Amazon S3 using SQL. It is designed for ad-hoc querying of structured data but does not natively support vector storage or vector similarity searches, making it unsuitable for this use case.

B. Amazon Aurora PostgreSQL: Amazon Aurora PostgreSQL is a fully managed relational database compatible with PostgreSQL. With the pgvector extension (available in PostgreSQL and supported by Aurora PostgreSQL), it can store and query vector embeddings efficiently. The pgvector extension enables vector similarity searches (e.g., using cosine similarity or Euclidean distance), which is critical for conversational search applications using embeddings from generative AI models.

C. Amazon Redshift: Amazon Redshift is a data warehousing service optimized for analytical queries on large datasets. While it supports machine learning features and can store numerical data, it does not have native support for vector embeddings or vector similarity searches as of May 17, 2025, making it less suitable for this use case.

D. Amazon EMR: Amazon EMR is a managed big data platform for processing large-scale data using frameworks like Apache Hadoop and Spark. It is not a database service and is not designed for storing or querying vector embeddings in the context of a conversational search application.

Exact Extract Reference: According to the AWS documentation, “Amazon Aurora PostgreSQL-Compatible Edition supports the pgvector extension, which enables efficient storage and similarity searches for vector embeddings. This makes it suitable for AI/ML workloads such as natural language processing and recommendation systems that rely on vector data.” (Source: AWS Aurora Documentation - Using pgvector with Aurora PostgreSQL, https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/PostgreSQLpgvector.html ). Additionally, the pgvector extension supports operations like nearest-neighbor searches, which are essential for querying embeddings in a conversational search system.

Amazon Aurora PostgreSQL with the pgvector extension directly meets the requirement for storing and querying embeddings as vectors, making B the correct answer.

The company is training an image classification model for diagnosing insect bites using a diverse dataset that includes photos from different genders, ethnicities, and geographic locations. This approach demonstrates the principle of fairness in responsible AI, as it aims to reduce bias and ensure the model performs equitably across diverse populations.

Exact Extract from AWS AI Documents:

From the AWS AI Practitioner Learning Path:

"Fairness in AI involves ensuring that models do not exhibit bias against certain groups and perform equitably across diverse populations. This can be achieved by training models on diverse datasets that represent various demographics, such as gender, ethnicity, and geographic location."

(Source: AWS AI Practitioner Learning Path, Module on Responsible AI)

Detailed Explanation:

Option A: FairnessThis is the correct answer. By using a diverse dataset, the company ensures the model is less likely to be biased against specific groups, promoting fairness in its predictions and treatments for insect bites.

Option B: ExplainabilityExplainability refers to making the model’s decisions understandable to users, such as byproviding insights into how predictions are made. The scenario focuses on dataset diversity, not explainability.

Option C: GovernanceGovernance involves establishing policies and processes to manage AI systems, such as compliance and oversight. The scenario does not describe governance mechanisms.

Option D: TransparencyTransparency involves disclosing how a model works, its limitations, and its data sources. While transparency is important, the scenario specifically highlights the diversity of the dataset, which aligns more directly with fairness.

Amazon SageMaker's DeepAR is a supervised learning algorithm designed for forecasting scalar (one-dimensional) time series data. Time series data consists of sequences of data points indexed in time order, typically with consistent intervals between them. In the context of a retail store aiming to predict product demand, relevant time series data might include historical sales figures, inventory levels, or related metrics recorded over regular time intervals (e.g., daily or weekly). By training the DeepAR model on this historical time series data, the store can generate forecasts for future product demand. This capability is particularly useful for inventory management, staffing, and supply chain optimization. Other data types, such as text, image, or binary data, are not suitable for time series forecasting tasks and would not be appropriate inputs for the DeepAR algorithm.

Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.

Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.

Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.

Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.

Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.

AWS AI Practitioner References:

Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.

In generative AI models, such as those built on Amazon Bedrock, inference costs are driven by the number of tokens processed. A token can be as short as one character or as long as one word, and the more tokens consumed during the inference process, the higher the cost.

Option A (Correct): "Number of tokens consumed": This is the correct answer because the inference cost is directly related to the number of tokens processed by the model.

Option B: "Temperature value" is incorrect as it affects the randomness of the model's output but not the cost directly.

Option C: "Amount of data used to train the LLM" is incorrect because training data size affects training costs, not inference costs.

Option D: "Total training time" is incorrect because it relates to the cost of training the model, not the cost of inference.

AWS AI Practitioner References:

Understanding Inference Costs on AWS: AWS documentation highlights that inference costs for generative models are largely based on the number of tokens processed.

Increasing the number of epochs during model training allows the model to learn from the data over more iterations, potentially improving its accuracy up to a certain point. This is a common practice when attempting to reach a specific level of accuracy.

Option B (Correct): "Increase the epochs": This is the correct answer because increasing epochs allows the model to learn more from the data, which can lead to higher accuracy.

Option A: "Decrease the batch size" is incorrect as it mainly affects training speed and may lead to overfitting but does not directly relate to achieving a specific accuracy level.

Option C: "Decrease the epochs" is incorrect as it would reduce the training time, possibly preventing the model from reaching the desired accuracy.

Option D: "Increase the temperature parameter" is incorrect because temperature affects the randomness of predictions, not model accuracy.

AWS AI Practitioner References:

Model Training Best Practices on AWS: AWS suggests adjusting training parameters, like the number of epochs, to improve model performance.

The context window determines how much information can fit into a single prompt when using a large language model (LLM) like those on Amazon Bedrock.

Context Window:

The context window is the maximum amount of text (measured in tokens) that a language model can process in a single pass.

For LLM applications, the size of the context window limits how much input data, such as text for sentiment analysis, can be fed into the model at once.

Why Option B is Correct:

Determines Prompt Size: The context window size directly informs how much information (e.g., words or sentences) can fit in one prompt.

Model Capacity: The larger the context window, the more information the model can consider for generating outputs.

Why Other Options are Incorrect:

A. Temperature: Controls randomness in model outputs but does not affect the prompt size.

C. Batch size: Refers to the number of training samples processed in one iteration, not the amount of information in a prompt.

D. Model size: Refers to the number of parameters in the model, not the input size for a single prompt.

The ecommerce company wants to improve search engine recommendations by customizing results for each user. Amazon Personalize is a machine learning service that enables personalized recommendations, tailoring search results or product suggestions based on individual user behavior and preferences, making it the best fit for this requirement.

Exact Extract from AWS AI Documents:

From the Amazon Personalize Developer Guide:

"Amazon Personalize enables developers to build applications with personalized recommendations, such as customized search results or product suggestions, by analyzing user behavior and preferences to deliver tailored experiences."

(Source: Amazon Personalize Developer Guide, Introduction to Amazon Personalize)

Detailed Explanation:

Option A: Amazon PersonalizeThis is the correct answer. Amazon Personalize specializes in creating personalized recommendations, ideal for customizing search results for each user on an ecommerce platform.

Option B: Amazon KendraAmazon Kendra is an intelligent search service for enterprise data, focusing on retrieving relevant documents or answers, not on personalizing search results for individual users.

Option C: Amazon RekognitionAmazon Rekognition is for image and video analysis, such as object detection or facial recognition, and is unrelated to search engine recommendations.

Option D: Amazon TranscribeAmazon Transcribe converts speech to text, which is not relevant for improving search engine recommendations.

The company needs an ML model for a lead prioritization application where employees can view and adjust the weights assigned to different variables based on domain knowledge. Logistic regression is a linear model that assigns interpretable weights to input features, making it easy for users to understand and modify these weights. This interpretability and adjustability make it suitable for the requirements.

Exact Extract from AWS AI Documents:

From the AWS AI Practitioner Learning Path:

"Logistic regression is a supervised learning algorithm used for classification tasks. It is highly interpretable, as it assigns weights to each feature, allowing users to understand and adjust the importance of different variables based on domain expertise."

(Source: AWS AI Practitioner Learning Path, Module on Machine Learning Algorithms)

Detailed Explanation:

Option A: Logistic regression modelThis is the correct answer. Logistic regression provides interpretable coefficients (weights) for each feature, enabling employees to view and adjust them based on domain knowledge, meeting the application’s requirements.

Option B: Deep learning model built on principal componentsDeep learning models, even when using principal components, are complex and lack interpretability. The weights in such models are not easily adjustable by users, making this option unsuitable.

Option C: K-nearest neighbors (k-NN) modelk-NN is a non-parametric model that does not assign explicit weights to features. It relieson distance metrics, which are not easily adjustable based on domain knowledge, so it does not meet the requirements.

Option D: Neural networkNeural networks are highly complex and lack interpretability, as their weights are not directly tied to input features in a human-understandable way. Adjusting weights based on domain knowledge is impractical, making this option incorrect.

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