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AWS-Certified-Machine-Learning-Specialty Latest Demo, Reliable AWS-Certified-Machine-Learning-Specialty Exam Cost

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q83-Q88):

NEW QUESTION # 83
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable What should be done to reduce the impact of having such a large number of features?

A. Perform one-hot encoding on highly correlated features
B. Create a new feature space using principal component analysis (PCA)
C. Apply the Pearson correlation coefficient
D. Use matrix multiplication on highly correlated features.

Answer: B

Explanation:
Principal component analysis (PCA) is an unsupervised machine learning algorithm that attempts to reduce the dimensionality (number of features) within a dataset while still retaining as much information as possible. This is done by finding a new set of features called components, which are composites of the original features that are uncorrelated with one another. They are also constrained so that the first component accounts for the largest possible variability in the data, the second component the second most variability, and so on. By using PCA, the impact of having a large number of features that are highly correlated with each other can be reduced, as the new feature space will have fewer dimensions and less redundancy. This can make the linear models more stable and less prone to overfitting. References:
Principal Component Analysis (PCA) Algorithm - Amazon SageMaker
Perform a large-scale principal component analysis faster using Amazon SageMaker | AWS Machine Learning Blog Machine Learning- Prinicipal Component Analysis | i2tutorials

NEW QUESTION # 84
A machine learning (ML) specialist is administering a production Amazon SageMaker endpoint with model monitoring configured. Amazon SageMaker Model Monitor detects violations on the SageMaker endpoint, so the ML specialist retrains the model with the latest dataset. This dataset is statistically representative of the current production traffic. The ML specialist notices that even after deploying the new SageMaker model and running the first monitoring job, the SageMaker endpoint still has violations.
What should the ML specialist do to resolve the violations?

A. Manually trigger the monitoring job to re-evaluate the SageMaker endpoint traffic sample.
B. Run the Model Monitor baseline job again on the new training set. Configure Model Monitor to use the new baseline.
C. Delete the endpoint and recreate it with the original configuration.
D. Retrain the model again by using a combination of the original training set and the new training set.

Answer: B

Explanation:
The ML specialist should run the Model Monitor baseline job again on the new training set and configure Model Monitor to use the new baseline. This is because the baseline job computes the statistics and constraints for the data quality and model quality metrics, which are used to detect violations. If the training set changes, the baseline job should be updated accordingly to reflect the new distribution of the data and the model performance. Otherwise, the old baseline may not be representative of the current production traffic and may cause false alarms or miss violations. References:
Monitor data and model quality - Amazon SageMaker
Detecting and analyzing incorrect model predictions with Amazon SageMaker Model Monitor and Debugger | AWS Machine Learning Blog

NEW QUESTION # 85
A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords.
Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?

A. Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.
B. Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.
C. Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.
D. Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.

Answer: D

Explanation:
Explanation
Amazon SageMaker script mode is a feature that allows users to use training scripts similar to those they would use outside SageMaker with SageMaker's prebuilt containers for various frameworks such as TensorFlow. Script mode supports reading data from Amazon S3 buckets without requiring any changes to the training script. Therefore, option B is the best method of providing training data to Amazon SageMaker that would meet the business requirements with the least development overhead.
Option A is incorrect because using a local path of the data would not be scalable or reliable, as it would depend on the availability and capacity of the local storage. Moreover, using a local path of the data would not leverage the benefits of Amazon S3, such as durability, security, and performance. Option C is incorrect because rewriting the train.py script to convert TFRecords to protobuf would require additional development effort and complexity, as well as introduce potential errors and inconsistencies in the data format. Option D is incorrect because preparing the data in the format accepted by Amazon SageMaker would also require additional development effort and complexity, as well as involve using additional services such as AWS Glue or AWS Lambda, which would increase the cost and maintenance of the solution.
References:
Bring your own model with Amazon SageMaker script mode
GitHub - aws-samples/amazon-sagemaker-script-mode
Deep Dive on TensorFlow training with Amazon SageMaker and Amazon S3
amazon-sagemaker-script-mode/generate_cifar10_tfrecords.py at master

NEW QUESTION # 86
A car company is developing a machine learning solution to detect whether a car is present in an image. The image dataset consists of one million images. Each image in the dataset is 200 pixels in height by 200 pixels in width. Each image is labeled as either having a car or not having a car.
Which architecture is MOST likely to produce a model that detects whether a car is present in an image with the highest accuracy?

A. Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.
B. Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.
C. Use a deep convolutional neural network (CNN) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.
D. Use a deep convolutional neural network (CNN) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.

Answer: D

Explanation:
Explanation
A deep convolutional neural network (CNN) classifier is a suitable architecture for image classification tasks, as it can learn features from the images and reduce the dimensionality of the input. A linear output layer that outputs the probability that an image contains a car is appropriate for a binary classification problem, as it can produce a single scalar value between 0 and 1. A softmax output layer is more suitable for a multi-class classification problem, as it can produce a vector of probabilities that sum up to 1. A deep multilayer perceptron (MLP) classifier is not as effective as a CNN for image classification, as it does not exploit the spatial structure of the images and requires a large number of parameters to process the high-dimensional input. References:
AWS Certified Machine Learning - Specialty Exam Guide
AWS Training - Machine Learning on AWS
AWS Whitepaper - An Overview of Machine Learning on AWS

NEW QUESTION # 87
A data scientist is training a large PyTorch model by using Amazon SageMaker. It takes 10 hours on average to train the model on GPU instances. The data scientist suspects that training is not converging and that resource utilization is not optimal.
What should the data scientist do to identify and address training issues with the LEAST development effort?

A. Use the SageMaker Debugger confusion and feature_importance_overweight built-in rules to detect issues and to launch the StopTrainingJob action if issues are detected.
B. Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect issues and to launch the StopTrainingJob action if issues are detected.
C. Use CPU utilization metrics that are captured in Amazon CloudWatch. Configure a CloudWatch alarm to stop the training job early if low CPU utilization occurs.
D. Use high-resolution custom metrics that are captured in Amazon CloudWatch. Configure an AWS Lambda function to analyze the metrics and to stop the training job early if issues are detected.

Answer: B

Explanation:
The solution C is the best option to identify and address training issues with the least development effort. The solution C involves the following steps:
* Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect issues. SageMaker Debugger is a feature of Amazon SageMaker that allows data scientists to monitor, analyze, and debug machine learning models during training. SageMaker Debugger provides a set of built-in rules that can automatically detect common issues and anomalies in model training, such as vanishing or exploding gradients, overfitting, underfitting, low GPU utilization, and more1. The data scientist can use the vanishing_gradient rule to check if the gradients are becoming too small and causing the training to not converge. The data scientist can also use the LowGPUUtilization rule to check if the GPU resources are underutilized and causing the training to be inefficient2.
* Launch the StopTrainingJob action if issues are detected. SageMaker Debugger can also take actions based on the status of the rules. One of the actions is StopTrainingJob, which can terminate the training job if a rule is in an error state. This can help the data scientist to save time and money by stopping the training early if issues are detected3.
The other options are not suitable because:
* Option A: Using CPU utilization metrics that are captured in Amazon CloudWatch and configuring a CloudWatch alarm to stop the training job early if low CPU utilization occurs will not identify and address training issues effectively. CPU utilization is not a good indicator of model training performance, especially for GPU instances. Moreover, CloudWatch alarms can only trigger actions based on simple thresholds, not complex rules or conditions4.
* Option B: Using high-resolution custom metrics that are captured in Amazon CloudWatch and configuring an AWS Lambda function to analyze the metrics and to stop the training job early if issues are detected will incur more development effort than using SageMaker Debugger. The data scientist will have to write the code for capturing, sending, and analyzing the custom metrics, as well as for invoking the Lambda function and stopping the training job. Moreover, this solution may not be able to detect all the issues that SageMaker Debugger can5.
* Option D: Using the SageMaker Debugger confusion and feature_importance_overweight built-in rules and launching the StopTrainingJob action if issues are detected will not identify and address training issues effectively. The confusion rule is used to monitor the confusion matrix of a classification model, which is not relevant for a regression model that predicts prices. The feature_importance_overweight rule is used to check if some features have too much weight in the model, which may not be related to the convergence or resource utilization issues2.
1: Amazon SageMaker Debugger
2: Built-in Rules for Amazon SageMaker Debugger
3: Actions for Amazon SageMaker Debugger
4: Amazon CloudWatch Alarms
5: Amazon CloudWatch Custom Metrics

NEW QUESTION # 88
......

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