AWS Certified AI Practitioner AIF-C01 practice test

1. Question 1 · 1

   A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these forecasts. An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders. What should the AI practitioner include in the report to meet the transparency and explainability requirements?

   Choose one answer.

   • Code for model training
   • Partial dependence plots (PDPs)
   • Sample data for training
   • Model convergence tables

2. Question 2 · 1

   A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible. Which solution will meet these requirements?

   Choose one answer.

   • Deploy optimized small language models (SLMs) on edge devices.
   • Deploy optimized large language models (LLMs) on edge devices.
   • Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.
   • Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.

3. Question 3 · 1

   A company is developing a mobile ML app that uses a phone's camera to diagnose and treat insect bites. The company wants to train an image classification model by using a diverse dataset of insect bite photos from different genders, ethnicities, and geographic locations around the world. Which principle of responsible AI does the company demonstrate in this scenario?

   Choose one answer.

   • Fairness
   • Explainability
   • Governance
   • Transparency

4. Question 4 · 1

   A company is developing an ML model to make loan approvals. The company must implement a solution to detect bias in the model. The company must also be able to explain the model's predictions. Which solution will meet these requirements?

   Choose one answer.

   • Amazon SageMaker Clarify
   • Amazon SageMaker Data Wrangler
   • Amazon SageMaker Model Cards
   • AWS AI Service Cards

5. Question 5 · 1

   A company has developed a generative text summarization model by using Amazon Bedrock. The company will use Amazon Bedrock automatic model evaluation capabilities. Which metric should the company use to evaluate the accuracy of the model?

   Choose one answer.

   • Area Under the ROC Curve (AUC) score
   • F1 score
   • BERTScore
   • Real world knowledge (RWK) score

6. Question 6 · 1

   An AI practitioner wants to predict the classification of flowers based on petal length, petal width, sepal length, and sepal width. Which algorithm meets these requirements?

   Choose one answer.

   • K-nearest neighbors (k-NN)
   • K-mean
   • Autoregressive Integrated Moving Average (ARIMA)
   • Linear regression

7. Question 7 · 1

   A company is using custom models in Amazon Bedrock for a generative AI application. The company wants to use a company managed encryption key to encrypt the model artifacts that the model customization jobs create. Which AWS service meets these requirements?

   Choose one answer.

   • AWS Key Management Service (AWS KMS)
   • Amazon Inspector
   • Amazon Macie
   • AWS Secrets Manager

8. Question 8 · 1

   A company wants to use large language models (LLMs) to produce code from natural language code comments. Which LLM feature meets these requirements?

   Choose one answer.

   • Text summarization
   • Text generation
   • Text completion
   • Text classification

9. Question 9 · 1

   A company is introducing a mobile app that helps users learn foreign languages. The app makes text more coherent by calling a large language model (LLM). The company collected a diverse dataset of text and supplemented the dataset with examples of more readable versions. The company wants the LLM output to resemble the provided examples. Which metric should the company use to assess whether the LLM meets these requirements?

   Choose one answer.

   • Value of the loss function
   • Semantic robustness
   • Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score
   • Latency of the text generation

10. Question 10 · 1

    A company notices that its foundation model (FM) generates images that are unrelated to the prompts. The company wants to modify the prompt techniques to decrease unrelated images. Which solution meets these requirements?

    Choose one answer.

    • Use zero-shot prompts.
    • Use negative prompts.
    • Use positive prompts.
    • Use ambiguous prompts.

11. Question 11 · 1

    A company wants to use a large language model (LLM) to generate concise, feature-specific descriptions for the company’s products. Which prompt engineering technique meets these requirements?

    Choose one answer.

    • Create one prompt that covers all products. Edit the responses to make the responses more specific, concise, and tailored to each product.
    • Create prompts for each product category that highlight the key features. Include the desired output format and length for each prompt response.
    • Include a diverse range of product features in each prompt to generate creative and unique descriptions.
    • Provide detailed, product-specific prompts to ensure precise and customized descriptions.

12. Question 12 · 1

    A company is developing an ML model to predict customer churn. The model performs well on the training dataset but does not accurately predict churn for new data. Which solution will resolve this issue?

    Choose one answer.

    • Decrease the regularization parameter to increase model complexity.
    • Increase the regularization parameter to decrease model complexity.
    • Add more features to the input data.
    • Train the model for more epochs.

13. Question 13 · 1

    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?

    Choose one answer.

    • Amazon SageMaker Feature Store
    • Amazon SageMaker Data Wrangler
    • Amazon SageMaker Clarify
    • Amazon SageMaker Model Cards

14. Question 14 · 1

    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?

    Choose one answer.

    • Amazon Athena
    • Amazon Aurora PostgreSQL
    • Amazon Redshift
    • Amazon EMR

15. Question 15 · 1

    A financial institution is building an AI solution to make loan approval decisions by using a foundation model (FM). For security and audit purposes, the company needs the AI solution's decisions to be explainable. Which factor relates to the explainability of the AI solution's decisions?

    Choose one answer.

    • Model complexity
    • Training time
    • Number of hyperparameters
    • Deployment time

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