AIF-C01 practice questions 1–10

1. Question 1 · 417 in bank

   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?

   • ACode for model training
   • BPartial dependence plots (PDPs)
   • CSample data for training
   • DModel convergence tables

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   Answer: B

   Explanation

   B. Partial Dependence Plots (PDPs) Explanation: Partial Dependence Plots (PDPs) are useful tools for understanding the relationship between specific features and the model's predictions, making it easier to see how changes in input variables affect the forecast. PDPs are particularly helpful for stakeholders because they visually show the impact of individual features on predictions without requiring a deep understanding of the model's inner workings.

   Source: Adapted from community discussion — jove

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2. Question 2 · 417 in bank

   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?

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

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   Answer: A

   Explanation

   A: Deploy optimized small language models (SLMs) on edge devices. Explanation: Deploying optimized small language models (SLMs) on edge devices ensures low latency because the inference happens directly on the device without relying on cloud communication. Small language models are lightweight and designed to run efficiently on devices with limited resources, making them ideal for edge computing.

   Source: Adapted from community discussion — Moon

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3. Question 3 · 417 in bank

   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?

   • AFairness
   • BExplainability
   • CGovernance
   • DTransparency

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   Answer: A

   Explanation

   In this scenario, the company is demonstrating the principle of Fairness by ensuring their training data includes a diverse range of demographics, preventing potential biases based on gender, ethnicity, or geographic location, which aligns with the core idea of responsible AI.

   Source: Adapted from community discussion — Jessiii

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4. Question 4 · 417 in bank

   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?

   • AAmazon SageMaker Clarify
   • BAmazon SageMaker Data Wrangler
   • CAmazon SageMaker Model Cards
   • DAWS AI Service Cards

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   Answer: A

   Explanation

   Amazon SageMaker Clarify provides both bias detection and model explainability features, making it the most suitable choice for detecting bias in a loan approval model and explaining its predictions.

   Source: Adapted from community discussion — aws_Tamilan

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5. Question 5 · 417 in bank

   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?

   • AArea Under the ROC Curve (AUC) score
   • BF1 score
   • CBERTScore
   • DReal world knowledge (RWK) score

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   Answer: C

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6. Question 6 · 417 in bank

   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?

   • AK-nearest neighbors (k-NN)
   • BK-mean
   • CAutoregressive Integrated Moving Average (ARIMA)
   • DLinear regression

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   Answer: A

   Explanation

   The practitioner wants to classify flowers based on measurements. This indicates a classification problem. A. K-nearest neighbors (k-NN): This is a classification algorithm that classifies data points based on the majority class among their k-nearest neighbors. It's suitable for this scenario. B. K-means: This is a clustering algorithm used for unsupervised learning. It groups data points into clusters based on similarity, but it doesn't perform classification with predefined labels. C. Autoregressive Integrated Moving Average (ARIMA): This is a time series forecasting model used for predicting future values based on past data trends. It's not suitable for classification based on static measurements like flower dimensions. D. Linear regression: This is a regression algorithm used for predicting continuous values. It's not suitable for classification into discrete categories like flower types. Therefore, A. K-nearest neighbors (k-NN) is the appropriate algorithm for this classification task

   Source: Adapted from community discussion — Moon

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7. Question 7 · 417 in bank

   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?

   • AAWS Key Management Service (AWS KMS)
   • BAmazon Inspector
   • CAmazon Macie
   • DAWS Secrets Manager

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   Answer: A

   Explanation

   The company needs to use a company-managed encryption key to encrypt model artifacts. This points directly to key management. A. AWS Key Management Service (AWS KMS): This is the correct answer. AWS KMS allows you to create and manage encryption keys, including customer-managed keys (CMKs), which give you control over the key lifecycle and usage. B. Amazon Inspector: Inspector is a vulnerability management service that scans for security vulnerabilities in your AWS resources. C. Amazon Macie: Macie is a data security and privacy service that uses machine learning to discover and protect sensitive data in AWS. D. AWS Secrets Manager: Secrets Manager helps you manage secrets such as passwords, API keys, and database credentials. While it can store encrypted secrets, it's not the primary service for managing encryption keys used to protect model artifacts at rest.

   Source: Adapted from community discussion — Moon

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8. Question 8 · 417 in bank

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

   • AText summarization
   • BText generation
   • CText completion
   • DText classification

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   Answer: B

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9. Question 9 · 417 in bank

   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?

   • AValue of the loss function
   • BSemantic robustness
   • CRecall-Oriented Understudy for Gisting Evaluation (ROUGE) score
   • DLatency of the text generation

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   Answer: C

   Explanation

   The ROUGE (Recall-Oriented Understudy for Gisting Evaluation) score is widely used to measure the similarity between generated text and a set of reference texts. Since the company wants the LLM's output to resemble the provided readable examples, ROUGE is the most appropriate metric. ROUGE compares the LLM-generated text with the human-provided reference texts by evaluating n-gram overlap, precision, recall, and F1 score, making it a great choice for text coherence and readability assessment.

   Source: Adapted from community discussion — Jessiii

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10. Question 10 · 417 in bank

    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?

    • AUse zero-shot prompts.
    • BUse negative prompts.
    • CUse positive prompts.
    • DUse ambiguous prompts.

    Show answer

    Answer: B

    Explanation

    B. Use negative prompts. Negative prompts help the model understand what to avoid in the generated images. By providing explicit instructions on what should not be included in the output, the model can better align its results with the intended themes and contexts of the prompts.

    Source: Adapted from community discussion — 26b8fe1

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AIF-C01 practice, question 1. 417 questions available.