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[General] 100% Pass Quiz 2026 Amazon MLS-C01: AWS Certified Machine Learning - Specialty M

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【General】 100% Pass Quiz 2026 Amazon MLS-C01: AWS Certified Machine Learning - Specialty M

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The AWS Certified Machine Learning - Specialty certification exam is a professional-level certification that validates the candidate's ability to design, implement, deploy, and maintain machine learning solutions on AWS. AWS Certified Machine Learning - Specialty certification exam is intended for data scientists, software developers, and machine learning practitioners who want to demonstrate their expertise in building and deploying ML solutions on AWS. Passing this certification exam is a valuable credential for professionals seeking to advance their careers in the field of machine learning.
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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) exam is a certification program designed for professionals who want to demonstrate their expertise in the field of machine learning. MLS-C01 exam is intended to validate the knowledge and skills of candidates in building, training, and deploying machine learning models on the Amazon Web Services (AWS) platform.
Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) Certification Exam is designed to assess the knowledge and skills of individuals in the field of machine learning. AWS Certified Machine Learning - Specialty certification is intended for professionals who have experience in building, training, and deploying machine learning models on the Amazon Web Services (AWS) platform. MLS-C01 Exam Tests the ability of candidates to design and implement machine learning solutions using AWS services.
Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q238-Q243):NEW QUESTION # 238
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs What does the Specialist need to do1?
  • A. Organize the Docker container's file structure to execute on GPU instances.
  • B. Build the Docker container to be NVIDIA-Docker compatible
  • C. Set the GPU flag in the Amazon SageMaker Create TrainingJob request body
  • D. Bundle the NVIDIA drivers with the Docker image
Answer: D

NEW QUESTION # 239
For the given confusion matrix, what is the recall and precision of the model?

  • A. Recall = 0.84 Precision = 0.8
  • B. Recall = 0.92 Precision = 0.84
  • C. Recall = 0.8 Precision = 0.92
  • D. Recall = 0.92 Precision = 0.8
Answer: B

NEW QUESTION # 240
A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.
Which model will meet the business requirement?
  • A. Principal component analysis (PCA)
  • B. Logistic regression
  • C. Linear regression
  • D. K-means
Answer: C
Explanation:
The best model for predicting housing prices based on a historical dataset with 32 features is linear regression. Linear regression is a supervised learning algorithm that fits a linear relationship between a dependent variable (housing price) and one or more independent variables (features). Linear regression can handle multiple features and output a continuous value for the housing price. Linear regression can also return the coefficients of the features, which indicate how each feature affects the housing price. Linear regression is suitable for this problem because the outcome of interest is numerical and continuous, and the model needs to capture the linear relationship between the features and the outcome.
References:
* AWS Machine Learning Specialty Exam Guide
* AWS Machine Learning Training - Regression vs Classification in Machine Learning
* AWS Machine Learning Training - Linear Regression with Amazon SageMaker

NEW QUESTION # 241
A machine learning (ML) specialist at a retail company must build a system to forecast the daily sales for one of the company's stores. The company provided the ML specialist with sales data for this store from the past
10 years. The historical dataset includes the total amount of sales on each day for the store. Approximately
10% of the days in the historical dataset are missing sales data.
The ML specialist builds a forecasting model based on the historical dataset. The specialist discovers that the model does not meet the performance standards that the company requires.
Which action will MOST likely improve the performance for the forecasting model?
  • A. Apply smoothing to correct for seasonal variation.
  • B. Aggregate sales from stores in the same geographic area.
  • C. Replace missing values in the dataset by using linear interpolation.
  • D. Change the forecast frequency from daily to weekly.
Answer: C
Explanation:
When forecasting sales data, missing values can significantly impact model accuracy, especially for time series models. Approximately 10% of the days in this dataset lack sales data, which may cause gaps in patterns and disrupt seasonal trends. Linear interpolation is an effective technique for estimating and filling in missing data points based on adjacent known values, thus preserving the continuity of the time series.
By interpolating the missing values, the ML specialist can provide the model with a more complete and consistent dataset, potentially enhancing performance. This approach maintains the daily data granularity, which is important for accurately capturing trends at that frequency.

NEW QUESTION # 242
A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model's accuracy. The learning rate parameter is specified in the following HPO configuration:

During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue.
Which solution provides the MOST accurate result?
  • A. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job:
    [0.01, 0.1]
    [0.001, 0.01]
    [0.0001, 0.001]
    Select the most accurate hyperparameter configuration form these three HPO jobs.
  • B. Modify the HPO configuration as follows:

    Select the most accurate hyperparameter configuration form this HPO job.
  • C. Modify the HPO configuration as follows:

    Select the most accurate hyperparameter configuration form this training job.
  • D. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue. Divide the number of training jobs for each HPO job by three:
    [0.01, 0.1]
    [0.001, 0.01]
    [0.0001, 0.001]
    Select the most accurate hyperparameter configuration form these three HPO jobs.
Answer: C
Explanation:
The solution C modifies the HPO configuration to use a logarithmic scale for the learning rate parameter. This means that the values of the learning rate are sampled from a log-uniform distribution, which gives more weight to smaller values. This can help to explore the lower end of the range more evenly and find the optimal learning rate more efficiently. The other solutions either use a linear scale, which may not sample enough values from the lower end, or divide the range into sub-intervals, which may miss some combinations of hyperparameters. References:
How Hyperparameter Tuning Works - Amazon SageMaker
Tuning Hyperparameters - Amazon SageMaker

NEW QUESTION # 243
......
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