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Amazon MLS-C01 exam covers a wide range of topics related to machine learning on AWS. These topics include data collection, preprocessing, and storage; model selection and training; deployment and monitoring; and data security and compliance. MLS-C01 Exam also covers AWS services such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q314-Q319):

NEW QUESTION # 314
A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images.
The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.
What changes should the Specialist consider to solve this issue? (Choose three.)

A. Choose a higher number of layers
B. Include all the images from the test set in the training set
C. Choose a lower number of layers
D. Enable early stopping
E. Choose a smaller learning rate
F. Enable dropout

Answer: C,D,F

Explanation:
The problem described in the question is a case of overfitting, where the neural network model performs well on the training data but poorly on the test data. This means that the model has learned the noise and specific patterns of the training data, but cannot generalize to new and unseen data. To solve this issue, the Specialist should consider the following changes:
* Choose a lower number of layers: Reducing the number of layers can reduce the complexity and capacity of the neural network model, making it less prone to overfitting. A model with 50 hidden layers is likely too deep for the given data size and task. A simpler model with fewer layers can learn the essential features of the data without memorizing the noise.
* Enable dropout: Dropout is a regularization technique that randomly drops out some units in the neural network during training. This prevents the units from co-adapting too much and forces the model to learn more robust features. Dropout can improve the generalization and test performance of the model by reducing overfitting.
* Enable early stopping: Early stopping is another regularization technique that monitors the validation error during training and stops the training process when the validation error stops decreasing or starts increasing. This prevents the model from overtraining on the training data and reduces overfitting.
References:
* Deep Learning - Machine Learning Lens
* How to Avoid Overfitting in Deep Learning Neural Networks
* How to Identify Overfitting Machine Learning Models in Scikit-Learn

NEW QUESTION # 315
A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test"?

A. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.
B. Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon OuickSight to visualize logs as they are being produced
C. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the data as it is generated by Amazon SageMaker
D. Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker

Answer: D

Explanation:
Explanation
Amazon CloudWatch is a service that can monitor and collect various metrics and logs from AWS resources, such as Amazon SageMaker. Amazon CloudWatch can also generate dashboards to create a single view for the metrics and logs that are of interest. By using Amazon CloudWatch, the Machine Learning Specialist can review the latency, memory utilization, and CPU utilization during the load test, as these are some of the metrics that are outputted by Amazon SageMaker. The Specialist can create a custom dashboard that displays these metrics in different widgets, such as graphs, tables, or text. The dashboard can also be configured to refresh automatically and show the latest data as the load test is running. This approach will allow the Specialist to monitor the performance and resource utilization of the model variant and adjust the Auto Scaling configuration accordingly.
References:
[Monitoring Amazon SageMaker with Amazon CloudWatch - Amazon SageMaker]
[Using Amazon CloudWatch Dashboards - Amazon CloudWatch]
[Create a CloudWatch Dashboard - Amazon CloudWatch]

NEW QUESTION # 316
A chemical company has developed several machine learning (ML) solutions to identify chemical process abnormalities. The time series values of independent variables and the labels are available for the past 2 years and are sufficient to accurately model the problem.
The regular operation label is marked as 0. The abnormal operation label is marked as 1 . Process abnormalities have a significant negative effect on the companys profits. The company must avoid these abnormalities.
Which metrics will indicate an ML solution that will provide the GREATEST probability of detecting an abnormality?

A. Precision = 0.91 Recall = 0.6
B. Precision = 0.7 Recall = 0.9
C. Precision = 0.98 Recall = 0.8
D. Precision = 0.61 Recall = 0.98

Answer: D

Explanation:
The metrics that will indicate an ML solution that will provide the greatest probability of detecting an abnormality are precision and recall. Precision is the ratio of true positives (TP) to the total number of predicted positives (TP + FP), where FP is false positives. Recall is the ratio of true positives (TP) to the total number of actual positives (TP + FN), where FN is false negatives. A high precision means that the ML solution has a low rate of false alarms, while a high recall means that the ML solution has a high rate of true detections. For the chemical company, the goal is to avoid process abnormalities, which are marked as 1 in the labels. Therefore, the company needs an ML solution that has a high recall for the positive class, meaning that it can detect most of the abnormalities and minimize the false negatives. Among the four options, option B has the highest recall for the positive class, which is 0.98. This means that the ML solution can detect 98% of the abnormalities and miss only 2%. Option B also has a reasonable precision for the positive class, which is 0.61. This means that the ML solution has a false alarm rate of 39%, which may be acceptable for the company, depending on the cost and benefit analysis. The other options have lower recall for the positive class, which means that they have higher false negative rates, which can be more detrimental for the company than false positive rates.
1: AWS Certified Machine Learning - Specialty Exam Guide
2: AWS Training - Machine Learning on AWS
3: AWS Whitepaper - An Overview of Machine Learning on AWS
4: Precision and recall

NEW QUESTION # 317
A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates.
Which solution will meet these requirements?

A. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.
B. Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.
C. Use an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.
D. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly.

Answer: B

NEW QUESTION # 318
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

A. The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
B. The validation channel identifying the location of validation data on an Amazon S3 bucket.
C. The output path specifying where on an Amazon S3 bucket the trained model will persist.
D. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
E. Hyperparameters in a JSON array as documented for the algorithm used.
F. The training channel identifying the location of training data on an Amazon S3 bucket.

Answer: A,B,F

NEW QUESTION # 319
......

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