Amazon AIF-C01 Online Practice Questions and Exam Preparation

Amazon AIF-C01 Online Questions & Answers

• Question 1:

  HOTSPOT

  A company is building an AI solution by using Amazon SageMaker AI. The company wants to use SageMaker AI features to facilitate application development.

  Select the correct SageMaker AI feature from the following list for each use case. Select each feature one time.

  

  

  Explanation:

  Determine the most suitable model to use for a business case -> Model Cards Prepare data through a low-code or no-code interface -> Data Wrangler Identify biases or imbalances in the data -> Clarify

  Model Cards help summarize and communicate model characteristics for selection.

  Data Wrangler provides a no-code/low-code interface for data preparation.

  Clarify is used to detect bias and imbalances in datasets and models.

• Question 2:

  HOTSPOT

  A company uses ML techniques to build applications.

  Select the correct ML technique from the following list for each task. Select each ML technique one time.

  

  

  Explanation:

  Analyze a text question to determine if the answer is correct -> Binary classification Analyze ecological factors to determine the number of species in a certain area -> Regression Analyze car attributes to determine the car model -> Multiclass classification

  Determining if an answer is correct is a yes/no (binary) decision.

  Predicting the number of species (a numeric value) is a regression task.

  Identifying a car model from attributes involves choosing from multiple possible classes, which is multiclass classification.

• Question 3:

  HOTSPOT

  Select the correct AWS service or tool from the following list for each use case. Select each AWS service or tool one time or not at all.

  

  

  Explanation:

  Apply human feedback across the ML lifecycle to improve the accuracy and relevancy of models. -> Amazon SageMaker Ground Truth

  Implement safeguards that align with responsible AI policies. -> Amazon Bedrock Guardrails Detect potential bias during data preparation and model training. -> Amazon SageMaker Clarify

  Amazon SageMaker Ground Truth is used for human-in-the-loop data labeling and model improvement.

  Amazon Bedrock Guardrails are designed to enforce responsible AI safeguards. Amazon SageMaker Clarify detects and helps mitigate bias during data preparation and model training.

• Question 4:

  HOTSPOT

  A company is developing an AI application to help the company approve or deny personal loans. The application must follow the principles of responsible AI.

  Select the correct responsible AI principle from the following list for each action. Select each responsible AI principle one time or not at all.

  

  

  Explanation:

  Encrypt the application data, and isolate the application on a private network -> Privacy and security Evaluate how different population groups will be impacted -> Fairness Test the application with unexpected data to ensure the application will work in unique situations -> Robustness

  Protecting data and networks is part of privacy and security.

  Assessing impacts on different groups ensures fairness.

  Testing with unexpected data checks the application's robustness.

• Question 5:

  HOTSPOT

  A company wants to use Amazon SageMaker features for various use cases.

  Select the correct SageMaker feature from the following list for each use case. Each SageMaker feature should be selected one time or not at all.

  

  

  Explanation:

• Question 6:

  HOTSPOT

  A company is designing a customer service chatbot by using a fine-tuned large language model (LLM). The company wants to ensure that the chatbot uses responsible AI characteristics.

  Select the correct responsible AI characteristic from the following list for each application design action. Each responsible AI characteristic should be selected one time or not at all.

  

  

  Explanation:

• Question 7:

  HOTSPOT

  A company needs to customize a base model that is hosted on Amazon Bedrock.

  Select the correct model customization method from the following list of company requirements. Each model customization method should be selected one or more times.

  

  

  Explanation:

• Question 8:

  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 9:

  HOTSPOT

  A company periodically updates its product database by manually uploading digital product guides. The product guides contain text and images. The company wants to automate this task by using generative AI.

  Select and order the steps from the following list to automate the database update task by using generative AI. Select each step one time.

  

  

  Explanation:

• Question 10:

  HOTSPOT

  Select the correct prompt engineering technique from the following list for each description. Select each prompt engineering technique one time or not at all.

  

  

  Explanation:

  Provide a small number of examples to the model to understand the desired task before generating outputs -> Few-shot prompting Prompt a model to break down the step-by-step process that the model took to arrive at a final answer -> Chain-of-thought prompting Prompt a model to perform a task without providing examples -> Zero-shot prompting

  Few-shot prompting uses a few examples to guide the model's outputs. Chain-of-thought prompting asks the model to explain its reasoning step-by-step. Zero-shot prompting asks the model to perform a task without any examples.