Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 41:

  A financial company is training a generative AI model to predict outcomes of loan applications. The training dataset is small. The dataset categorizes loan applicants as "younger-aged," "middle-aged," or "olderaged." Most individuals in the dataset are characterized as "middle-aged." The company removes the age range feature from the training dataset.

  Which model behavior will likely happen as a result of this change to the dataset?

  A. The model will inaccurately predict outcomes for younger and older age groups.
  B. The model will require less training data.
  C. The model will predict accurate outcomes for only younger age groups.
  D. The model will accurately predict outcomes for all ages.
  A. The model will inaccurately predict outcomes for younger and older age groups.

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  Explanation

  Removing the age feature eliminates an important variable that differentiates applicant groups. Since the dataset is already imbalanced, the model will generalize based on dominant patterns and lose the ability to account for age-related differences, leading to poorer predictions especially for underrepresented younger and older groups.

• Question 42:

  A company uses Amazon SageMaker AI to generate article summaries in multiple languages. The company needs a metric to evaluate the quality of the summary translations in multiple languages.

  Which evaluation metric will meet these requirements?

  A. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
  B. Bilingual evaluation understudy (BLEU)
  C. Area Under the ROC Curve (AUC)
  D. Precision
  B. Bilingual evaluation understudy (BLEU)

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  Explanation

  Bilingual Evaluation Understudy measures how closely a generated translation matches one or more high-quality reference translations, making it well suited for evaluating the quality of multilingual summary translations.

• Question 43:

  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?

  A. Amazon EC2 C series
  B. Amazon EC2 G series
  C. Amazon EC2 P series
  D. Amazon EC2 Trn series
  D. Amazon EC2 Trn series

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  The Amazon EC2 Trn series (Trainium) instances are designed for high-performance, cost- effective machine learning training while being energy-efficient. AWS Trainium-powered instances are optimized for deep learning models and have been developed to minimize environmental impact by maximizing energy efficiency.

  Option D (Correct): "Amazon EC2 Trn series":This is the correct answer because the Trn series is purpose-built for training deep learning models with lower energy consumption, which aligns with the company's concern about environmental effects.

  Option A:"Amazon EC2 C series" is incorrect because it is intended for compute- intensive tasks but not specifically optimized for ML training with environmental considerations.

  Option B:"Amazon EC2 G series" (Graphics Processing Unit instances) is optimized for graphics-intensive applications but does not focus on minimizing environmental impact for training.

  Option C:"Amazon EC2 P series" is designed for ML training but does not offer the same level of energy efficiency as the Trn series.

  References:

  AWS Trainium Overview:AWS promotes Trainium instances as their most energy- efficient and cost-effective solution for ML model training.

• Question 44:

  A company is implementing intelligent agents to provide conversational search experiences for its customers. The company needs a database service that will support storage and queries of embeddings from a generative AI model as vectors in the database. Which AWS service will meet these requirements?

  A. Amazon Athena
  B. Amazon Aurora PostgreSQL
  C. Amazon Redshift
  D. Amazon EMR
  B. Amazon Aurora PostgreSQL

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  Amazon Aurora PostgreSQL is a relational database service that supports a wide range of applications, including those that require vector-based operations. Specifically, Aurora PostgreSQL can be extended with vector search capabilities using extensions like pgvector. This extension allows you to store, index, and query vector embeddings from generative AI models, making it well-suited for the company's needs to store and query embeddings as vectors. pgvector is an extension for PostgreSQL that provides efficient similarity search for vector data, which is ideal for storing and querying embeddings.

• Question 45:

  A publishing company built a Retrieval Augmented Generation (RAG) based solution to give its users the ability to interact with published content. New content is published daily. The company wants to provide a near real-time experience to users. Which steps in the RAG pipeline should the company implement by using offline batch processing to meet these requirements? (Select TWO.)

  A. Generation of content embeddings
  B. Generation of embeddings for user queries
  C. Creation of the search index
  D. Retrieval of relevant content
  E. Response generation for the user
  A. Generation of content embeddings
  C. Creation of the search index

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  Generation of content embeddings

  New or updated documents can be converted to vector representations in periodic batches (e.g., hourly, nightly). Because a document's embedding remains fixed until the document itself changes, this step does not have to run for every user query, making it ideal for offline processing.

  Creation of the search index

  After new embeddings are produced, the vector (or hybrid) index can be rebuilt or incrementally updated in batch mode. Index construction is computationally intensive but happens infrequently relative to user requests, so running it offline avoids adding latency to real-time interactions.

• Question 46:

  HOTSPOT

  A company wants to build generative AI applications by using Amazon Bedrock. The company wants to minimize development effort.

  Select and order the model development techniques from the following list from the LEAST development effort to the MOST development effort. Each model development technique should be selected one time.

  

  

  Explanation:

  1. Prompt engineering

  2. Retrieval Augmented Generation (RAG)

  3. Fine-tuning

  4. Continued pre-training

  Prompt engineering requires minimal effort as it only involves designing effective prompts. RAG adds moderate effort by integrating retrieval mechanisms with the model. Fine-tuning requires labeled data and retraining, demanding more resources. Continued pre-training is the most effort-intensive because it involves large-scale training with unlabeled domain data.

• Question 47:

  Which option is a benefit of ongoing pre-training when fine-tuning a foundation model (FM)?

  A. Helps decrease the model's complexity
  B. Improves model performance over time
  C. Decreases the training time requirement
  D. Optimizes model inference time
  B. Improves model performance over time

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  Ongoing pre-training when fine-tuning a foundation model (FM) improves model performance over time by continuously learning from new data.

• Question 48:

  A company creates video content. The company wants to use generative AI to generate new creative content and to reduce video creation time. Which solution will meet these requirements in the MOST operationally efficient way?

  A. Use the Amazon Titan Image Generator model on Amazon Bedrock to generate intermediate images. Use video editing software to create videos.
  B. Use the Amazon Nova Canvas model on Amazon Bedrock to generate intermediate images. Use video editing software to create videos.
  C. Use the Amazon Nova Reel model on Amazon Bedrock to generate videos.
  D. Use the Amazon Nova Pro model on Amazon Bedrock to generate videos.
  C. Use the Amazon Nova Reel model on Amazon Bedrock to generate videos.

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  The Amazon Nova Reel model on Amazon Bedrock is specifically designed for generative video creation, enabling the company to efficiently generate new creative content and significantly reduce video creation time in an operationally efficient manner.

• Question 49:

  A company is using supervised learning to train an AI model on a small labeled dataset that is specific to a target task. Which step of the foundation model (FM) lifecycle does this describe?

  A. Fine-tuning
  B. Data selection
  C. Pre-training
  D. Evaluation
  A. Fine-tuning

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  Fine-tuning is the process of training a pre-existing foundation model on a smaller, labeled dataset that is specific to a target task, allowing the model to adapt to the company's requirements.

• Question 50:

  Which statement describes a generative AI use case for multimodal models?

  A. Deploy multiple scalable and cost-effective versions of a model.
  B. Process large amounts of data to train multiple models.
  C. Write code in multiple programming languages.
  D. Process different data types, such as images, audio, and video.
  D. Process different data types, such as images, audio, and video.

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  Explanation

  Multimodal generative AI models are designed to process and reason across multiple data types such as images, audio, video, and text within a single model.