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Amazon AWS Certified Machine Learning - Specialty 認定 AWS-Certified-Machine-Learning-Specialty 試験問題 (Q205-Q210):
質問 # 205
A data scientist at a financial services company used Amazon SageMaker to train and deploy a model that predicts loan defaults. The model analyzes new loan applications and predicts the risk of loan default. To train the model, the data scientist manually extracted loan data from a database. The data scientist performed the model training and deployment steps in a Jupyter notebook that is hosted on SageMaker Studio notebooks.
The model's prediction accuracy is decreasing over time. Which combination of slept in the MOST operationally efficient way for the data scientist to maintain the model's accuracy? (Select TWO.)
- A. Rerun the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model.
- B. Export the training and deployment code from the SageMaker Studio notebooks into a Python script.Package the script into an Amazon Elastic Container Service (Amazon ECS) task that an AWS Lambda function can initiate.
- C. Use SageMaker Pipelines to create an automated workflow that extracts fresh data, trains the model, and deploys a new version of the model.
- D. Store the model predictions in Amazon S3 Create a daily SageMaker Processing job that reads the predictions from Amazon S3, checks for changes in model prediction accuracy, and sends an email notification if a significant change is detected.
- E. Configure SageMaker Model Monitor with an accuracy threshold to check for model drift. Initiate an Amazon CloudWatch alarm when the threshold is exceeded. Connect the workflow in SageMaker Pipelines with the CloudWatch alarm to automatically initiate retraining.
正解：C、E
解説：
Explanation
Option A is correct because SageMaker Pipelines is a service that enables you to create and manage automated workflows for your machine learning projects. You can use SageMaker Pipelines to orchestrate the steps of data extraction, model training, and model deployment in a repeatable and scalable way1.
Option B is correct because SageMaker Model Monitor is a service that monitors the quality of your models in production and alerts you when there are deviations in the model quality. You can use SageMaker Model Monitor to set an accuracy threshold for your model and configure a CloudWatch alarm that triggers when the threshold is exceeded. You can then connect the alarm to the workflow in SageMaker Pipelines to automatically initiate retraining and deployment of a new version of the model2.
Option C is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Creating a daily SageMaker Processing job that reads the predictions from Amazon S3 and checks for changes in model prediction accuracy is a manual and time-consuming process. It also requires you to write custom code to perform the data analysis and send the email notification.
Moreover, it does not automatically retrain and deploy the model when the accuracy drops.
Option D is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Rerunning the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model is a manual and error-prone process. It also requires you to monitor the model's performance and initiate the retraining and deployment steps yourself. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.
Option E is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Exporting the training and deployment code from the SageMaker Studio notebooks into a Python script and packaging the script into an Amazon ECS task that an AWS Lambda function can initiate is a complex and cumbersome process. It also requires you to manage the infrastructure and resources for the Amazon ECS task and the AWS Lambda function. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.
References:
1: SageMaker Pipelines - Amazon SageMaker
2: Monitor data and model quality - Amazon SageMaker
質問 # 206
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.
Based on this information which model would have the HIGHEST accuracy?
- A. Support vector machine (SVM) with non-linear kernel
- B. Single perceptron with tanh activation function
- C. Logistic regression
- D. Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))
正解：A
解説：
Based on the figure provided, the data is not linearly separable. Therefore, a non-linear model such as SVM with a non-linear kernel would be the best choice. SVMs are particularly effective in high-dimensional spaces and are versatile in that they can be used for both linear and non-linear data. Additionally, SVMs have a high level of accuracy and are less prone to overfitting1 References: 1: https://docs.aws.amazon.com/sagemaker/latest/dg/svm.html
質問 # 207
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?
- A. Poisson distribution
- B. Binomial distribution
- C. Uniform distribution
- D. Normal distribution
正解：B
質問 # 208
A company builds computer-vision models that use deep learning for the autonomous vehicle industry. A machine learning (ML) specialist uses an Amazon EC2 instance that has a CPU: GPU ratio of 12:1 to train the models.
The ML specialist examines the instance metric logs and notices that the GPU is idle half of the time The ML specialist must reduce training costs without increasing the duration of the training jobs.
Which solution will meet these requirements?
- A. Use memory-optimized EC2 Spot Instances for the training jobs.
- B. Switch to an instance type that has a CPU GPU ratio of 6:1.
- C. Use a heterogeneous cluster that has two different instances groups.
- D. Switch to an instance type that has only CPUs.
正解：B
解説：
Explanation
Switching to an instance type that has a CPU: GPU ratio of 6:1 will reduce the training costs by using fewer CPUs and GPUs, while maintaining the same level of performance. The GPU idle time indicates that the CPU is not able to feed the GPU with enough data, so reducing the CPU: GPU ratio will balance the workload and improve the GPU utilization. A lower CPU: GPU ratio also means less overhead for inter-process communication and synchronization between the CPU and GPU processes. References:
Optimizing GPU utilization for AI/ML workloads on Amazon EC2
Analyze CPU vs. GPU Performance for AWS Machine Learning
質問 # 209
A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes.
What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?
- A. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
- B. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.
- C. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
- D. Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
正解：A
解説：
Explanation
質問 # 210
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