ONLY THE MOST POPULAR MLS-C01 STUDY TOOL CAN MAKE MANY PEOPLE PASS THE AWS CERTIFIED MACHINE LEARNING - SPECIALTY

Only The Most Popular MLS-C01 Study Tool Can Make Many People Pass The AWS Certified Machine Learning - Specialty

Only The Most Popular MLS-C01 Study Tool Can Make Many People Pass The AWS Certified Machine Learning - Specialty

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AWS Certified Machine Learning – Specialty is a standard exam for candidates who want to excel in the development field and data science and verify their competence by earning certification. This test, coded MLS-C01, helps the individuals to measure their knowledge of the design, deployment, implementation, and maintenance of machine learning solutions.

This exam allows candidates to validate their skills related to choosing the right AWS services for Machine learning implementation and resolving business problems. Also, they prove their ability to create reliable, cost effective, and secure ML solutions.

To become an AWS Certified Machine Learning Specialist, candidates must demonstrate their ability to design, implement, and maintain machine learning solutions on AWS. This includes understanding the various tools and services offered by AWS for machine learning, such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend. Candidates must also have experience working with data, such as data preprocessing, data visualization, and data analysis.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q74-Q79):

NEW QUESTION # 74
A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users.
The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company's business goals. To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models.
Which solution satisfies these requirements with MINIMAL effort?

  • A. Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.
  • B. Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.
  • C. Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices. Programmatically control which model is invoked for inference based on the medical device type.
  • D. Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer.

Answer: B

Explanation:
Explanation
Amazon SageMaker is a service that allows users to build, train, and deploy ML models on AWS. Amazon SageMaker endpoints are scalable and secure web services that can be used to perform real-time inference on ML models. An endpoint configuration defines the models that are deployed and the resources that are used by the endpoint. An endpoint configuration can have multiple production variants, each representing a different version or variant of a model. Users can specify the portion of the inferences served by each production variant using the initialVariantWeight parameter. Users can also programmatically update the endpoint configuration to change the portion of the inferences served by each production variant using the UpdateEndpointWeightsAndCapacities API. Therefore, option B is the best solution to satisfy the requirements with minimal effort.
Option A is incorrect because creating multiple endpoints for each model would incur more cost and complexity than using a single endpoint with multiple production variants. Moreover, controlling the invocation of different models at the application layer would require more custom logic and coordination than using the UpdateEndpointWeightsAndCapacities API. Option C is incorrect because Amazon SageMaker Neo is a service that allows users to optimize ML models for different hardware platforms, such as edge devices. It is not relevant to the problem of running multiple versions of a model in parallel for long periods of time.
Option D is incorrect because Amazon SageMaker batch transform is a service that allows users to perform asynchronous inference on large datasets. It is not suitable for the problem of performing real-time inference on streaming data from device users.
References:
Deploying models to Amazon SageMaker hosting services - Amazon SageMaker Update an Amazon SageMaker endpoint to accommodate new models - Amazon SageMaker UpdateEndpointWeightsAndCapacities - Amazon SageMaker


NEW QUESTION # 75
A Machine Learning Specialist wants to determine the appropriate SageMaker Variant Invocations Per Instance setting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS As this is the first deployment, the Specialist intends to set the invocation safety factor to 0 5 Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the sageMaker variant invocations Per instance setting?

  • A. 0
  • B. 2,400
  • C. 1
  • D. 2

Answer: C

Explanation:
Explanation
The SageMaker Variant Invocations Per Instance setting is the target value for the average number of invocations per instance per minute for the model variant. It is used by the automatic scaling policy to add or remove instances to keep the metric close to the specified value. To determine this value, the following equation can be used in combination with load testing:
SageMakerVariantInvocationsPerInstance = (MAX_RPS * SAFETY_FACTOR) * 60 Where MAX_RPS is the maximum requests per second that the model variant can handle without service degradation, SAFETY_FACTOR is a factor that ensures that the clients do not exceed the maximum RPS, and
60 is the conversion factor from seconds to minutes. In this case, the given parameters are:
MAX_RPS = 20 SAFETY_FACTOR = 0.5
Plugging these values into the equation, we get:
SageMakerVariantInvocationsPerInstance = (20 * 0.5) * 60 SageMakerVariantInvocationsPerInstance = 600 Therefore, the Specialist should set the SageMaker Variant Invocations Per Instance setting to 600.
References:
Load testing your auto scaling configuration - Amazon SageMaker
Configure model auto scaling with the console - Amazon SageMaker


NEW QUESTION # 76
A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.
What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

  • A. Residual plots
  • B. Root Mean Square Error (RMSE)
  • C. Confusion matrix
  • D. Area under the curve

Answer: A


NEW QUESTION # 77
A credit card company wants to identify fraudulent transactions in real time. A data scientist builds a machine learning model for this purpose. The transactional data is captured and stored in Amazon S3. The historic data is already labeled with two classes: fraud (positive) and fair transactions (negative). The data scientist removes all the missing data and builds a classifier by using the XGBoost algorithm in Amazon SageMaker. The model produces the following results:
* True positive rate (TPR): 0.700
* False negative rate (FNR): 0.300
* True negative rate (TNR): 0.977
* False positive rate (FPR): 0.023
* Overall accuracy: 0.949
Which solution should the data scientist use to improve the performance of the model?

  • A. Undersample the minority class.
  • B. Oversample the majority class.
  • C. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the majority class in the training dataset. Retrain the model with the updated training data.
  • D. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset. Retrain the model with the updated training data.

Answer: D

Explanation:
Explanation
The solution that the data scientist should use to improve the performance of the model is to apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset, and retrain the model with the updated training data. This solution can address the problem of class imbalance in the dataset, which can affect the model's ability to learn from the rare but important positive class (fraud).
Class imbalance is a common issue in machine learning, especially for classification tasks. It occurs when one class (usually the positive or target class) is significantly underrepresented in the dataset compared to the other class (usually the negative or non-target class). For example, in the credit card fraud detection problem, the positive class (fraud) is much less frequent than the negative class (fair transactions). This can cause the model to be biased towards the majority class, and fail to capture the characteristics and patterns of the minority class. As a result, the model may have a high overall accuracy, but a low recall or true positive rate for the minority class, which means it misses many fraudulent transactions.
SMOTE is a technique that can help mitigate the class imbalance problem by generating synthetic samples for the minority class. SMOTE works by finding the k-nearest neighbors of each minority class instance, and randomly creating new instances along the line segments connecting them. This way, SMOTE can increase the number and diversity of the minority class instances, without duplicating or losing any information. By applying SMOTE on the minority class in the training dataset, the data scientist can balance the classes and improve the model's performance on the positive class1.
The other options are either ineffective or counterproductive. Applying SMOTE on the majority class would not balance the classes, but increase the imbalance and the size of the dataset. Undersampling the minority class would reduce the number of instances available for the model to learn from, and potentially lose some important information. Oversampling the majority class would also increase the imbalance and the size of the dataset, and introduce redundancy and overfitting.
References:
1: SMOTE for Imbalanced Classification with Python - Machine Learning Mastery


NEW QUESTION # 78
A gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million users The training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and
999.000 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user age, device, location, and play patterns Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set However, the prediction results on a test dataset were not satisfactory.
Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

  • A. Add more deep trees to the random forest to enable the model to learn more features.
  • B. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
  • C. Change the cost function so that false positives have a higher impact on the cost value than false negatives
  • D. indicate a copy of the samples in the test database in the training dataset
  • E. Change the cost function so that false negatives have a higher impact on the cost value than false positives

Answer: B,D


NEW QUESTION # 79
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