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Click on the sample questions for the AWS AI Practitioner below to reveal the correct answers and explanations with reference links.
Question 1: A company is developing a machine learning model using Amazon SageMaker and needs a solution to store and share feature sets across different teams for collaborative model building.
Which Amazon SageMaker feature should the company use?
1. Amazon SageMaker Feature Store
2. Amazon SageMaker Data Wrangler
3. Amazon SageMaker Clarify
4. Amazon SageMaker Model Registry
Item #2
Show Answer
The correct answer is 1. “Amazon SageMaker Feature Store.”
Explanation:
SageMaker Feature Store is designed to allow teams to store, manage, and share features (attributes or variables) in a central repository. This ensures consistency across models and helps teams collaborate by reusing the same features across multiple projects.
Amazon SageMaker Data Wrangler: This is incorrect because Data Wrangler is used for data transformation and preparation, helping users to clean and structure data before training models. It does not provide a mechanism to store and share features across teams.
Amazon SageMaker Clarify: This is incorrect because Clarify is focused on detecting bias in machine learning models and ensuring explainability. It is not used for storing or sharing features between teams.
Amazon SageMaker Model Registry: This is incorrect because the Model Registry is designed for managing and versioning machine learning models, not for storing or sharing feature sets used during model development.
References:
https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store.html
Question 2: A business uses Amazon SageMaker to run its machine learning pipeline in a production environment. The company processes large datasets, sometimes reaching 1 GB in size, with processing times that can take up to an hour. To support its operations, the company requires low-latency predictions.
Which Amazon SageMaker inference option should the company choose?
1. Real-time inference
2. Serverless inference
3. Asynchronous inference
4. Batch transform
Item #2
Show Answer
The correct answer is 1. “Real-time inference.”
Explanation:
Real-time inference is designed for scenarios where low-latency responses are needed. It is ideal when predictions need to be generated immediately upon receiving input data, making it suitable for use cases requiring near real-time results, even with large datasets.
Serverless inference: This is incorrect because serverless inference is optimized for intermittent workloads that don’t require low-latency predictions. While it’s cost-effective for occasional requests, it doesn’t meet the requirement for near real-time performance when processing large datasets.
Asynchronous inference: This is incorrect because asynchronous inference is intended for situations where the input size is large or the processing time is lengthy, but real-time predictions are not required. It’s useful when results can be delayed, but it doesn’t support the company’s need for near real-time latency.
Batch transform: This is incorrect because batch transform is used for processing large datasets in batches without a focus on real-time results. It is better suited for use cases where predictions are processed in bulk and not required immediately after input.
References:
https://docs.aws.amazon.com/sagemaker/latest/dg/deploy-model.html
Question 3: Which method is used to evaluate the accuracy of a foundation model (FM) applied to image classification tasks?
1. Calculate the total cost of resources consumed by the model.
2. Measure the model’s accuracy using a benchmark dataset specifically designed for image classification.
3. Count the total number of neural network layers in the model architecture.
4. Assess the color accuracy of the images that the model processes.
Item #2
Show Answer
The correct answer is 2. “Measure the model’s accuracy using a benchmark dataset specifically designed for image classification.”
Explanation:
Evaluating the accuracy of a foundation model involves comparing its predictions to known labels from a predefined dataset. Benchmark datasets are specifically curated for tasks like image classification to assess how well a model performs against a standardized set of images.
Calculate the total cost of resources consumed by the model: This is incorrect because resource consumption measures efficiency, not model accuracy. Evaluating costs is important for financial considerations, but it doesn’t reflect how well the model classifies images.
Count the total number of neural network layers in the model architecture: This is incorrect because the number of layers in the neural network doesn’t directly measure the model’s performance or accuracy. While deeper networks can often be more powerful, accuracy is evaluated through performance on actual data.
Assess the color accuracy of the images that the model processes: This is incorrect because color accuracy pertains to the visual quality or fidelity of an image. It has no direct relationship to the model’s ability to classify images correctly.
References:
https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html
Questions 4: An AI researcher is developing a model to generate synthetic faces for a facial recognition application. During training, they realize that the dataset contains significantly fewer samples of certain ethnic groups, leading to biased model outputs.
Which technique can the researcher use to address this bias?
1. Data augmentation for imbalanced classes
2. Model monitoring for accuracy drift
3. Retrieval Augmented Generation (RAG)
4. Edge detection for image processing
Item #2
Show Answer
The correct answer is 1. “Data augmentation for imbalanced classes”.
Explanation:
Data augmentation helps balance the dataset by creating new samples from underrepresented groups. By applying transformations such as rotation, cropping, or flipping to the existing images, the researcher can reduce the bias in the dataset and improve the fairness of the model’s outputs.
Model monitoring for accuracy drift: This is incorrect because monitoring for accuracy drift helps track model performance over time but does not correct bias in the training data. It identifies performance issues but doesn’t resolve them.
Retrieval Augmented Generation (RAG): This is incorrect because RAG is a method for improving generative models by retrieving relevant information from external sources, which doesn’t help balance the data or correct the underlying bias in this scenario.
Edge detection for image processing: This is incorrect because edge detection is a technique used to identify boundaries in images, which is unrelated to addressing dataset bias. It focuses on feature extraction, not on balancing data or mitigating bias.
References:
https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-bias-metric-class-imbalance.html
Questions 5: A company is utilizing machine learning models for specialized tasks in a specific domain. To save time and resources, the company prefers to modify existing pre-trained models instead of building new ones from scratch.
Which machine learning approach should the company use?
1. Increase the number of training iterations.
2. Apply transfer learning.
3. Reduce the number of training iterations.
4. Implement unsupervised learning.
Item #2
Show Answer
The correct answer is 2. “Apply transfer learning.”
Explanation:
Transfer learning allows a company to leverage pre-trained models and adapt them to new, related tasks. Instead of training a model from scratch, the pre-trained model’s knowledge is fine-tuned for the new task, significantly reducing the training time and required data.
Increase the number of training iterations: This is incorrect because increasing the number of epochs (iterations) only affects the training of the model, but it doesn’t allow you to reuse knowledge from pre-trained models. This would require starting with a model from scratch, which the company wants to avoid.
Reduce the number of training iterations: This is incorrect because reducing the number of epochs does not address the need for reusing pre-trained models. While this may shorten training time, it doesn’t leverage pre-existing model knowledge, which is the main objective.
Implement unsupervised learning: This is incorrect because unsupervised learning involves training a model without labeled data. The company’s goal is to adapt pre-trained models for new tasks, which typically involves supervised or fine-tuned learning rather than unsupervised techniques.
References:
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Exam coverage
This free AWS practice exam includes a mix of questions on core AWS services covering multiple knowledge areas
This free AWS AI Practitioner practice exam consists of 20 questions with a mix of questions on e.g. Amazon SageMaker, Amazon Bedrock and Amazon Rekognition.
Please note that, unlike our exam simulator for the AWS AI Practitioner, this AWS quiz is not timed – so you can take as much time as required to answer each question. At the end of the test, you get to review your answers and find detailed explanations of why each answer is correct or incorrect along with reference links for each question. This will help you identify your strength and weaknesses.
How to best prepare for your AWS AI practitioner exam
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AWS Certified AI practitioner exam
Below you’ll find the most important facts about the official AWS Certified AI Practitioner Exam (CLF-C02)
| Exam Name | AWS Certified AI Practitioner |
|---|---|
| Exam Code | AIF-C01 |
| Exam Level | Foundational |
| Exam Duration | 90 Minutes |
| Passing Score | 70% |
| Eligibility/Pre-requisite | None |
| Validity | 3 years |
| Exam Format | Multiple choice or multiple response |
| Number of Questions | 65 Questions |
| Exam Fee | $100 |
| Exam Languages | English, French, German, Indonesian, Italian, Japanese, Korean, Portuguese, Simplified Chinese, Spanish |
| Exam Delivery Format | Pearson VUE and PSI (testing center or online proctored exam) |
| Official Exam Guide | Download the official Exam Guide |