DISCOUNT MLS-C01 CODE, MLS-C01 TEST QUESTIONS PDF

Discount MLS-C01 Code, MLS-C01 Test Questions Pdf

Discount MLS-C01 Code, MLS-C01 Test Questions Pdf

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Understanding functional and technical aspects of AWS Certified Machine Learning - Specialty Exploratory Data Analysis

The following will be discussed in AMAZON MLS-C01 Exam Dumps:

  • Perform feature engineering
  • Sanitize and prepare data for modeling
  • Analyze and visualize data for machine learning

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q143-Q148):

NEW QUESTION # 143
An ecommerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible.
Which steps would improve the accuracy of the solution? (Choose three.)

  • A. Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.
  • B. Augment the images in the dataset. Use open-source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.
  • C. Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.
  • D. Use Amazon Rekognition Custom Labels to train a new model.
  • E. Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as required. Store the new dataset in Amazon S3.
  • F. Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3.

Answer: B,D,E

Explanation:
* Option C is correct because augmenting the images in the dataset can help the model learn more features and generalize better to new products. Image augmentation is a common technique to increase the diversity and size of the training data.
* Option E is correct because Amazon Rekognition Custom Labels can train a custom model to detect specific objects and scenes that are relevant to the business use case. It can also leverage the existing models from Amazon Rekognition that are trained on tens of millions of images across many categories.
* Option F is correct because class imbalance can affect the performance and accuracy of the model, as it can cause the model to be biased towards the majority class and ignore the minority class. Applying oversampling or undersampling can help balance the classes and improve the model's ability to learn from the data.
* Option A is incorrect because the semantic segmentation algorithm is used to assign a label to every pixel in an image, not to classify the whole image into a category. Semantic segmentation is useful for applications such as autonomous driving, medical imaging, and satellite imagery analysis.
* Option B is incorrect because the DetectLabels API is a general-purpose image analysis service that can detect objects, scenes, and concepts in an image, but it cannot be customized to the specific product lines of the ecommerce company. The DetectLabels API is based on the pre-trained models from Amazon Rekognition, which may not cover all the categories that the company needs.
* Option D is incorrect because normalizing the pixels and scaling the images are preprocessing steps that should be done before training the model, not after. These steps can help improve the model's convergence and performance, but they are not sufficient to increase the accuracy of the model on new products.
References:
* : Image Augmentation - Amazon SageMaker
* : Amazon Rekognition Custom Labels Features
* : [Handling Imbalanced Datasets in Machine Learning]
* : [Semantic Segmentation - Amazon SageMaker]
* : [DetectLabels - Amazon Rekognition]
* : [Image Classification - MXNet - Amazon SageMaker]
* : [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]
* : [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]
* : [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]
* : [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]
* : [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]
* : [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]
* : [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]
* : [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]
* : [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]
* : [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]
* : [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]
* : [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]


NEW QUESTION # 144
A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point contains credit history from a third-party data source and demographic information about the customer. Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan. The company will use Amazon SageMaker to build the model.
Which solution will meet these requirements with the LEAST development effort?

  • A. Use SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.
  • B. Use AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report.
  • C. Use SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.
  • D. Use custom Amazon Cloud Watch metrics to generate the explanation report. Attach the report to the predicted results.

Answer: A

Explanation:
The best solution for this scenario is to use SageMaker Clarify to generate the explanation report and attach it to the predicted results. SageMaker Clarify provides tools to help explain how machine learning (ML) models make predictions using a model-agnostic feature attribution approach based on SHAP values. It can also detect and measure potential bias in the data and the model. SageMaker Clarify can generate explanation reports during data preparation, model training, and model deployment. The reports include metrics, graphs, and examples that help understand the model behavior and predictions. The reports can be attached to the predicted results using the SageMaker SDK or the SageMaker API.
The other solutions are less optimal because they require more development effort and additional services. Using SageMaker Model Debugger would require modifying the training script to save the model output tensors and writing custom rules to debug and explain the predictions. Using AWS Lambda would require writing code to invoke the ML model, compute the feature importance and partial dependence plots, and generate and attach the explanation report. Using custom Amazon CloudWatch metrics would require writing code to publish the metrics, create dashboards, and generate and attach the explanation report.
References:
Bias Detection and Model Explainability - Amazon SageMaker Clarify - AWS Amazon SageMaker Clarify Model Explainability Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability GitHub - aws/amazon-sagemaker-clarify: Fairness Aware Machine Learning


NEW QUESTION # 145
A company wants to detect credit card fraud. The company has observed that an average of 2% of credit card transactions are fraudulent. A data scientist trains a classifier on a year's worth of credit card transaction data.
The classifier needs to identify the fraudulent transactions. The company wants to accurately capture as many fraudulent transactions as possible.
Which metrics should the data scientist use to optimize the classifier? (Select TWO.)

  • A. Specificity
  • B. Fl score
  • C. False positive rate
  • D. True positive rate
  • E. Accuracy

Answer: B,D

Explanation:
Explanation
The F1 score is a measure of the harmonic mean of precision and recall, which are both important for fraud detection. Precision is the ratio of true positives to all predicted positives, and recall is the ratio of true positives to all actual positives. A high F1 score indicates that the classifier can correctly identify fraudulent transactions and avoid false negatives. The true positive rate is another name for recall, and it measures the proportion of fraudulent transactions that are correctly detected by the classifier. A high true positive rate means that the classifier can capture as many fraudulent transactions as possible.
References:
Fraud Detection Using Machine Learning | Implementations | AWS Solutions Detect fraudulent transactions using machine learning with Amazon SageMaker | AWS Machine Learning Blog
1. Introduction - Reproducible Machine Learning for Credit Card Fraud Detection


NEW QUESTION # 146
A company deployed a machine learning (ML) model on the company website to predict real estate prices.
Several months after deployment, an ML engineer notices that the accuracy of the model has gradually decreased.
The ML engineer needs to improve the accuracy of the model. The engineer also needs to receive notifications for any future performance issues.
Which solution will meet these requirements?

  • A. Use only data from the previous several months to perform incremental training to update the model.Use Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.
  • B. Use Amazon SageMaker Debugger with appropriate thresholds. Configure Debugger to send Amazon CloudWatch alarms to alert the team Retrain the model by using only data from the previous several months.
  • C. Use Amazon SageMaker Model Governance. Configure Model Governance to automatically adjust model hyper para meters. Create a performance threshold alarm in Amazon CloudWatch to send notifications.
  • D. Perform incremental training to update the model. Activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.

Answer: D

Explanation:
The best solution to improve the accuracy of the model and receive notifications for any future performance issues is to perform incremental training to update the model and activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications. Incremental training is a technique that allows you to update an existing model with new data without retraining the entire model from scratch. This can save time and resources, and help the model adapt to changing data patterns. Amazon SageMaker Model Monitor is a feature that continuously monitors the quality of machine learning models in production and notifies you when there are deviations in the model quality, such as data drift and anomalies. You can set up alerts that trigger actions, such as sending notifications to Amazon Simple Notification Service (Amazon SNS) topics, when certain conditions are met.
Option B is incorrect because Amazon SageMaker Model Governance is a set of tools that help you implement ML responsibly by simplifying access control and enhancing transparency. It does not provide a mechanism to automatically adjust model hyperparameters or improve model accuracy.
Option C is incorrect because Amazon SageMaker Debugger is a feature that helps you debug and optimize your model training process by capturing relevant data and providing real-time analysis. However, using Debugger alone does not update the model or monitor its performance in production. Also, retraining the model by using only data from the previous several months may not capture the full range of data variability and may introduce bias or overfitting.
Option D is incorrect because using only data from the previous several months to perform incremental training may not be sufficient to improve the model accuracy, as explained above. Moreover, this option does not specify how to activate Amazon SageMaker Model Monitor or configure the alerts and notifications.
References:
* Incremental training
* Amazon SageMaker Model Monitor
* Amazon SageMaker Model Governance
* Amazon SageMaker Debugger


NEW QUESTION # 147
A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

Considering the graph, what is a reasonable selection for the optimal choice of k?

  • A. 0
  • B. 1
  • C. 2
  • D. 3

Answer: A


NEW QUESTION # 148
......

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