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AIF-C01

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AIF-C01 — 20 free practice questions

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Question 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? Options: A. Code for model training B. Partial dependence plots (PDPs) C. Sample data for training D. Model convergence tables
  • A. Code for model training
  • B. Partial dependence plots (PDPs) Correct
  • C. Sample data for training
  • D. Model convergence tables

Explanation

Partial Dependence Plots (PDPs) are a valuable tool for explaining the relationship between features and predictions in machine learning models, providing transparency into how each feature affects the outcome. This aligns with AWS's emphasis on model explainability to ensure stakeholders understand model behavior.

Question 2

A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents. Which solution meets these requirements? Options: A. Build an automatic named entity recognition system. B. Create a recommendation engine. C. Develop a summarization chatbot. D. Develop a multi-language translation system.
  • A. Build an automatic named entity recognition system.
  • B. Create a recommendation engine.
  • C. Develop a summarization chatbot. Correct
  • D. Develop a multi-language translation system.

Explanation

The correct answer is C because developing a summarization chatbot aligns with the requirement of reading legal documents and extracting key points, which is a form of summarization. Options A, B, and D do not directly address the need for summarizing or extracting key information from documents.

Question 3

A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output. Which ML algorithm meets these requirements? Options: A. Decision trees B. Linear regression C. Logistic regression D. Neural networks
  • A. Decision trees Correct
  • B. Linear regression
  • C. Logistic regression
  • D. Neural networks

Explanation

Decision trees are suitable for classification tasks and provide a clear, interpretable model that shows how input features affect the output through a series of decisions. This transparency allows documentation of the inner mechanism of the model, meeting the company's requirements.

Question 4

A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly. Which evaluation metric should the company use to measure the model's performance? Options: A. R-squared score B. Accuracy C. Root mean squared error (RMSE) D. Learning rate
  • A. R-squared score
  • B. Accuracy Correct
  • C. Root mean squared error (RMSE)
  • D. Learning rate

Explanation

Accuracy is the correct metric for evaluating how many images were classified correctly by a model, as it measures the proportion of true results among the total number of cases processed. This makes it suitable for classification tasks like image classification.

Question 5

A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language. Which solution will align the LLM response quality with the company's expectations? Options: A. Adjust the prompt. B. Choose an LLM of a different size. C. Increase the temperature. D. Increase the Top K value.
  • A. Adjust the prompt. Correct
  • B. Choose an LLM of a different size.
  • C. Increase the temperature.
  • D. Increase the Top K value.

Explanation

Adjusting the prompt (Option A) is the correct solution as it allows for specifying the desired length and language of the responses directly within the instructions given to the LLM, aligning its outputs with the company's expectations according to AWS best practices.

Question 6

A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency. Which SageMaker inference option meets these requirements? Options: A. Real-time inference B. Serverless inference C. Asynchronous inference D. Batch transform
  • A. Real-time inference
  • B. Serverless inference
  • C. Asynchronous inference Correct
  • D. Batch transform

Explanation

Asynchronous inference in Amazon SageMaker is suitable for scenarios where the input data sizes are large and processing times are long, while still requiring near real-time responses. This option allows the model to process requests without waiting for a response, which fits the company's need for handling up to 1 GB of input data with processing times up to 1 hour.

Question 7

A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks. Which ML strategy meets these requirements? Options: A. Increase the number of epochs. B. Use transfer learning. C. Decrease the number of epochs. D. Use unsupervised learning.
  • A. Increase the number of epochs.
  • B. Use transfer learning. Correct
  • C. Decrease the number of epochs.
  • D. Use unsupervised learning.

Explanation

According to AWS documentation, transfer learning is a machine learning technique where a model trained on one task is repurposed on a second related task. This strategy allows the company to adapt pre-trained models for new, related tasks without starting from scratch, which aligns with their requirements.

Question 8

A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations. Which solution will meet these requirements? Options: A. Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus B. Data augmentation by using an Amazon Bedrock knowledge base C. Image recognition by using Amazon Rekognition D. Data summarization by using Amazon QuickSight Q
  • A. Human-in-the-loop validation by using Amazon SageMaker Ground Truth Plus Correct
  • B. Data augmentation by using an Amazon Bedrock knowledge base
  • C. Image recognition by using Amazon Rekognition
  • D. Data summarization by using Amazon QuickSight Q

Explanation

Amazon SageMaker Ground Truth Plus provides high-quality annotated datasets with human-in-the-loop validation, ensuring high accuracy and minimizing the risk of incorrect annotations, which meets the requirements for generating images for protective eyewear.

Question 9

A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket. The data is encrypted with Amazon S3 managed keys (SSE-S3). The FM encounters a failure when attempting to access the S3 bucket data. Which solution will meet these requirements? Options: A. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key. B. Set the access permissions for the S3 buckets to allow public access to enable access over the internet. C. Use prompt engineering techniques to tell the model to look for information in Amazon S3. D. Ensure that the S3 data does not contain sensitive information.
  • A. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key. Correct
  • B. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
  • C. Use prompt engineering techniques to tell the model to look for information in Amazon S3.
  • D. Ensure that the S3 data does not contain sensitive information.

Explanation

The correct answer is A because, according to AWS documentation, for a foundation model on Amazon Bedrock to access and decrypt SSE-S3 encrypted data stored in an Amazon S3 bucket, the role assumed by Amazon Bedrock must have the necessary permissions to decrypt the data with the appropriate encryption key. This ensures secure and authorized access to the encrypted data.

Question 10

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? Options: A. Deploy optimized small language models (SLMs) on edge devices. B. Deploy optimized large language models (LLMs) on edge devices. C. Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices. D. Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.
  • A. Deploy optimized small language models (SLMs) on edge devices. Correct
  • B. Deploy optimized large language models (LLMs) on edge devices.
  • C. Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.
  • D. Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.

Explanation

According to AWS documentation, deploying optimized small language models (SLMs) on edge devices (Option A) will meet the requirement of lowest latency because SLMs require fewer computational resources and can process requests more quickly than large language models. Additionally, processing on edge devices reduces dependency on network communication, further minimizing latency.

Question 11

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? Options: A. Amazon SageMaker Feature Store B. Amazon SageMaker Data Wrangler C. Amazon SageMaker Clarify D. Amazon SageMaker Model Cards
  • A. Amazon SageMaker Feature Store Correct
  • B. Amazon SageMaker Data Wrangler
  • C. Amazon SageMaker Clarify
  • D. Amazon SageMaker Model Cards

Explanation

Amazon SageMaker Feature Store is designed to store, share, and manage features for model development across multiple teams, which meets the company's requirements. Other options like Data Wrangler, Clarify, and Model Cards do not provide feature storage and sharing capabilities.

Question 12

A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer. What can Amazon Q Developer do to help the company meet these requirements? Options: A. Create software snippets, reference tracking, and open source license tracking. B. Run an application without provisioning or managing servers. C. Enable voice commands for coding and providing natural language search. D. Convert audio files to text documents by using ML models.
  • A. Create software snippets, reference tracking, and open source license tracking. Correct
  • B. Run an application without provisioning or managing servers.
  • C. Enable voice commands for coding and providing natural language search.
  • D. Convert audio files to text documents by using ML models.

Explanation

According to AWS documentation, Amazon Q Developer is designed to assist developers with generating code snippets, tracking references within the codebase, and managing open source licenses, which directly aligns with option A.

Question 13

A financial institution is using Amazon Bedrock to develop an AI application. The application is hosted in a VPC. To meet regulatory compliance standards, the VPC is not allowed access to any internet traffic. Which AWS service or feature will meet these requirements? Options: A. AWS PrivateLink B. Amazon Macie C. Amazon CloudFront D. Internet gateway
  • A. AWS PrivateLink Correct
  • B. Amazon Macie
  • C. Amazon CloudFront
  • D. Internet gateway

Explanation

AWS PrivateLink allows services to communicate over a private network, without using the internet or an internet gateway. This ensures that traffic remains within the VPC and adheres to regulatory compliance standards by avoiding any exposure to public internet traffic.

Question 14

A company wants to develop an educational game where users answer questions such as the following: "A jar contains six red, four green, and three yellow marbles. What is the probability of choosing a green marble from the jar?" Which solution meets these requirements with the LEAST operational overhead? Options: A. Use supervised learning to create a regression model that will predict probability. B. Use reinforcement learning to train a model to return the probability. C. Use code that will calculate probability by using simple rules and computations. D. Use unsupervised learning to create a model that will estimate probability density.
  • A. Use supervised learning to create a regression model that will predict probability.
  • B. Use reinforcement learning to train a model to return the probability.
  • C. Use code that will calculate probability by using simple rules and computations. Correct
  • D. Use unsupervised learning to create a model that will estimate probability density.

Explanation

The correct answer is C because calculating probability in this scenario requires only basic arithmetic operations, which can be efficiently handled with simple code without the need for complex machine learning models. Options A, B, and D involve machine learning approaches that introduce unnecessary operational overhead for a straightforward calculation.

Question 15

Which metric measures the runtime efficiency of operating AI models? Options: A. Customer satisfaction score (CSAT) B. Training time for each epoch C. Average response time D. Number of training instances
  • A. Customer satisfaction score (CSAT)
  • B. Training time for each epoch
  • C. Average response time Correct
  • D. Number of training instances

Explanation

The average response time is a metric that measures the runtime efficiency of operating AI models, as it indicates how quickly the model can process and respond to incoming requests or data. This directly relates to the operational performance and user experience in real-time applications.

Question 16

A company is building a contact center application and wants to gain insights from customer conversations. The company wants to analyze and extract key information from the audio of the customer calls. Which solution meets these requirements? Options: A. Build a conversational chatbot by using Amazon Lex. B. Transcribe call recordings by using Amazon Transcribe. C. Extract information from call recordings by using Amazon SageMaker Model Monitor. D. Create classification labels by using Amazon Comprehend.
  • A. Build a conversational chatbot by using Amazon Lex.
  • B. Transcribe call recordings by using Amazon Transcribe. Correct
  • C. Extract information from call recordings by using Amazon SageMaker Model Monitor.
  • D. Create classification labels by using Amazon Comprehend.

Explanation

Amazon Transcribe is designed to convert spoken language in audio files into text, which allows the company to transcribe customer calls and gain insights from the conversations. This meets the requirement of analyzing and extracting key information from the audio of customer calls.

Question 17

A company has petabytes of unlabeled customer data to use for an advertisement campaign. The company wants to classify its customers into tiers to advertise and promote the company's products. Which methodology should the company use to meet these requirements? Options: A. Supervised learning B. Unsupervised learning C. Reinforcement learning D. Reinforcement learning from human feedback (RLHF)
  • A. Supervised learning
  • B. Unsupervised learning Correct
  • C. Reinforcement learning
  • D. Reinforcement learning from human feedback (RLHF)

Explanation

The correct answer is B, unsupervised learning. According to AWS documentation, unsupervised learning is used for tasks where the data has no labels and the goal is to find hidden patterns or intrinsic structures in the input data, such as customer segmentation into tiers without predefined labels.

Question 18

An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images. Which type of FM should the AI practitioner use to power the search application? Options: A. Multi-modal embedding model B. Text embedding model C. Multi-modal generation model D. Image generation model
  • A. Multi-modal embedding model Correct
  • B. Text embedding model
  • C. Multi-modal generation model
  • D. Image generation model

Explanation

The correct answer is A, a multi-modal embedding model, because it can handle and integrate both text and image data types simultaneously, which is necessary for the search application that must process queries containing both text and images.

Question 19

A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company's data. Which strategy will successfully fine-tune the model? Options: A. Provide labeled data with the prompt field and the completion field. B. Prepare the training dataset by creating a .txt file that contains multiple lines in .csv format. C. Purchase Provisioned Throughput for Amazon Bedrock. D. Train the model on journals and textbooks.
  • A. Provide labeled data with the prompt field and the completion field. Correct
  • B. Prepare the training dataset by creating a .txt file that contains multiple lines in .csv format.
  • C. Purchase Provisioned Throughput for Amazon Bedrock.
  • D. Train the model on journals and textbooks.

Explanation

According to AWS documentation, fine-tuning a foundation model from Amazon Bedrock involves providing labeled data where the 'prompt' field contains the input text and the 'completion' field contains the desired output or response. This method allows the model to learn from specific examples provided by the company, thereby improving its accuracy for the particular use case.

Question 20

A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source. Which solution meets these requirements? Options: A. Build a speech recognition system. B. Create a natural language processing (NLP) named entity recognition system. C. Develop an anomaly detection system. D. Create a fraud forecasting system.
  • A. Build a speech recognition system.
  • B. Create a natural language processing (NLP) named entity recognition system.
  • C. Develop an anomaly detection system. Correct
  • D. Create a fraud forecasting system.

Explanation

Anomaly detection systems are designed to identify unusual patterns that do not conform to expected behavior, making them suitable for detecting suspicious IP addresses. Options A, B, and D are not directly related to identifying suspicious IP sources as they pertain to speech recognition, NLP named entity recognition, and fraud forecasting respectively.

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