Newest Latest MLS-C01 Test Materials and Updated MLS-C01 Valid Exam Questions & Perfect Exam AWS Certified Machine Learning - Specialty Introduction

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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is a certification exam offered by Amazon Web Services for individuals who are interested in demonstrating their proficiency in machine learning on the AWS platform. MLS-C01 exam is designed for professionals who have a strong understanding of machine learning concepts and the ability to design, implement, and maintain machine learning solutions on AWS.

Amazon MLS-C01 Exam consists of 65 multiple-choice and multiple-response questions that must be completed within 170 minutes. MLS-C01 exam is available in English, Japanese, Korean, and Simplified Chinese. Candidates who pass the exam will receive the AWS Certified Machine Learning - Specialty certification, which is valid for three years.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q236-Q241):

NEW QUESTION # 236
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: D

Explanation:
The elbow method is a technique that we use to determine the number of centroids (k) to use in a k-means clustering algorithm. In this method, we plot the within-cluster sum of squares (WCSS) against the number of clusters (k) and look for the point where the curve bends sharply. This point is called the elbow point and it indicates that adding more clusters does not improve the model significantly. The graph in the question shows that the elbow point is at k = 4, which means that 4 is a reasonable choice for the optimal number of clusters. References:
Elbow Method for optimal value of k in KMeans: A tutorial on how to use the elbow method with Amazon SageMaker.
K-Means Clustering: A video that explains the concept and benefits of k-means clustering.

NEW QUESTION # 237
An online reseller has a large, multi-column dataset with one column missing 30% of its data. A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

A. Listwise deletion
B. Multiple imputation
C. Mean substitution
D. Last observation carried forward

Answer: B

Explanation:
https://worldwidescience.org/topicpages/i/imputing+missing+values.html

NEW QUESTION # 238
While reviewing the histogram for residuals on regression evaluation data a Machine Learning Specialist notices that the residuals do not form a zero-centered bell shape as shown What does this mean?

A. The model might have prediction errors over a range of target values.
B. The dataset cannot be accurately represented using the regression model
C. The model is predicting its target values perfectly.
D. There are too many variables in the model

Answer: C

NEW QUESTION # 239
A medical imaging company wants to train a computer vision model to detect areas of concern on patients' CT scans. The company has a large collection of unlabeled CT scans that are linked to each patient and stored in an Amazon S3 bucket. The scans must be accessible to authorized users only. A machine learning engineer needs to build a labeling pipeline.
Which set of steps should the engineer take to build the labeling pipeline with the LEAST effort?

A. Create a workforce with Amazon Cognito. Build a labeling web application with AWS Amplify. Build a labeling workflow backend using AWS Lambda. Write the labeling instructions.
B. Create a workforce with AWS Identity and Access Management (IAM). Build a labeling tool on Amazon EC2 Queue images for labeling by using Amazon Simple Queue Service (Amazon SQS). Write the labeling instructions.
C. Create a private workforce and manifest file. Create a labeling job by using the built-in bounding box task type in Amazon SageMaker Ground Truth. Write the labeling instructions.
D. Create an Amazon Mechanical Turk workforce and manifest file. Create a labeling job by using the built-in image classification task type in Amazon SageMaker Ground Truth. Write the labeling instructions.

Answer: C

Explanation:
https://docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-private.html

NEW QUESTION # 240
A company is planning a marketing campaign to promote a new product to existing customers. The company has data (or past promotions that are similar. The company decides to try an experiment to send a more expensive marketing package to a smaller number of customers. The company wants to target the marketing campaign to customers who are most likely to buy the new product. The experiment requires that at least 90% of the customers who are likely to purchase the new product receive the marketing materials.
...company trains a model by using the linear learner algorithm in Amazon SageMaker. The model has a recall score of 80% and a precision of 75%.
...should the company retrain the model to meet these requirements?

A. Use 90% of the historical data for training Set the number of epochs to 20.
B. Set the normalize_jabel hyperparameter to true. Set the number of classes to 2.
C. Set the targetprecision hyperparameter to 90%. Set the binary classifier model selection criteria hyperparameter to precision at_jarget recall.
D. Set the target_recall hyperparameter to 90% Set the binaryclassrfier model_selection_critena hyperparameter to recall_at_target_precision.

Answer: D

Explanation:
The best way to retrain the model to meet the requirements is to set the target_recall hyperparameter to 90% and set the binary_classifier_model_selection_criteria hyperparameter to recall_at_target_precision. This will instruct the linear learner algorithm to optimize the model for a high recall score, while maintaining a reasonable precision score. Recall is the proportion of actual positives that were identified correctly, which is important for the company's goal of reaching at least 90% of the customers who are likely to buy the new product1. Precision is the proportion of positive identifications that were actually correct, which is also relevant for the company's budget and efficiency2. By setting the target_recall to 90%, the algorithm will try to achieve a recall score of at least 90%, and by setting the binary_classifier_model_selection_criteria to recall_at_target_precision, the algorithm will select the model that has the highest recall score among those that have a precision score equal to or higher than the target precision3. The target precision is automatically set to the median of the precision scores of all the models trained in parallel4.
The other options are not correct or optimal, because they have the following drawbacks:
B: Setting the target_precision hyperparameter to 90% and setting the binary_classifier_model_selection_criteria hyperparameter to precision_at_target_recall will optimize the model for a high precision score, while maintaining a reasonable recall score. However, this is not aligned with the company's goal of reaching at least 90% of the customers who are likely to buy the new product, as precision does not reflect how well the model identifies the actual positives1. Moreover, setting the target_precision to 90% might be too high and unrealistic for the dataset, as the current precision score is only 75%4.
C: Using 90% of the historical data for training and setting the number of epochs to 20 will not necessarily improve the recall score of the model, as it does not change the optimization objective or the model selection criteria. Moreover, using more data for training might reduce the amount of data available for validation, which is needed for selecting the best model among the ones trained in parallel3. The number of epochs is also not a decisive factor for the recall score, as it depends on the learning rate, the optimizer, and the convergence of the algorithm5.
D: Setting the normalize_label hyperparameter to true and setting the number of classes to 2 will not affect the recall score of the model, as these are irrelevant hyperparameters for binary classification problems. The normalize_label hyperparameter is only applicable for regression problems, as it controls whether the label is normalized to have zero mean and unit variance3. The number of classes hyperparameter is only applicable for multiclass classification problems, as it specifies the number of output classes3.
References:
1: Classification: Precision and Recall | Machine Learning | Google for Developers
2: Precision and recall - Wikipedia
3: Linear Learner Algorithm - Amazon SageMaker
4: How linear learner works - Amazon SageMaker
5: Getting hands-on with Amazon SageMaker Linear Learner - Pluralsight

NEW QUESTION # 241
......

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Newest Latest MLS-C01 Test Materials and Updated MLS-C01 Valid Exam Questions & Perfect Exam AWS Certified Machine Learning - Specialty Introduction

Web-Based Amazon MLS-C01 Practice Test - Compatible with All Major

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q236-Q241):

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