LATEST MLS-C01 MOCK EXAM - SIMULATIONS MLS-C01 PDF

Latest MLS-C01 Mock Exam - Simulations MLS-C01 Pdf

Latest MLS-C01 Mock Exam - Simulations MLS-C01 Pdf

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Tags: Latest MLS-C01 Mock Exam, Simulations MLS-C01 Pdf, MLS-C01 Reliable Test Forum, MLS-C01 Valid Mock Test, Best MLS-C01 Study Material

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To prepare for the Amazon MLS-C01 exam, candidates can take advantage of the AWS training and certification resources. AWS offers various training courses, including instructor-led training, self-paced online courses, and virtual classrooms. The AWS Certified Machine Learning - Specialty preparation course provides candidates with the knowledge and skills required to pass the exam. Additionally, AWS offers practice exams and sample questions to help candidates assess their readiness for the certification exam.

Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) Certification Exam is designed to test the skills and knowledge of professionals who work with machine learning technologies within the Amazon Web Services (AWS) environment. AWS Certified Machine Learning - Specialty certification is ideal for individuals who want to demonstrate their proficiency in designing, implementing, and maintaining machine learning solutions on AWS. MLS-C01 Exam assesses candidates on a range of topics, including data engineering, machine learning algorithms, AWS services for machine learning, and model deployment and maintenance.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q68-Q73):

NEW QUESTION # 68
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. K-means
  • B. Principal component analysis (PCA)
  • C. Logistic regression
  • D. Linear regression

Answer: D

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.
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 # 69
A bank wants to launch a low-rate credit promotion. The bank is located in a town that recently experienced economic hardship. Only some of the bank's customers were affected by the crisis, so the bank's credit team must identify which customers to target with the promotion. However, the credit team wants to make sure that loyal customers' full credit history is considered when the decision is made.
The bank's data science team developed a model that classifies account transactions and understands credit eligibility. The data science team used the XGBoost algorithm to train the model. The team used 7 years of bank transaction historical data for training and hyperparameter tuning over the course of several days.
The accuracy of the model is sufficient, but the credit team is struggling to explain accurately why the model denies credit to some customers. The credit team has almost no skill in data science.
What should the data science team do to address this issue in the MOST operationally efficient manner?

  • A. Use Amazon SageMaker Studio to rebuild the model. Create a notebook that uses the XGBoost training container to perform model training. Activate Amazon SageMaker Debugger, and configure it to calculate and collect Shapley values. Create a chart that shows features and SHapley Additive exPlanations (SHAP) values to explain to the credit team how the features affect the model outcomes.
  • B. Create an Amazon SageMaker notebook instance. Use the notebook instance and the XGBoost library to locally retrain the model. Use the plot_importance() method in the Python XGBoost interface to create a feature importance chart. Use that chart to explain to the credit team how the features affect the model outcomes.
  • C. Use Amazon SageMaker Studio to rebuild the model. Create a notebook that uses the XGBoost training container to perform model training. Deploy the model at an endpoint. Enable Amazon SageMaker Model Monitor to store inferences. Use the inferences to create Shapley values that help explain model behavior. Create a chart that shows features and SHapley Additive exPlanations (SHAP) values to explain to the credit team how the features affect the model outcomes.
  • D. Use Amazon SageMaker Studio to rebuild the model. Create a notebook that uses the XGBoost training container to perform model training. Deploy the model at an endpoint. Use Amazon SageMaker

Answer: B

Explanation:
Processing to post-analyze the model and create a feature importance explainability chart automatically for the credit team.


NEW QUESTION # 70
A manufacturing company stores production volume data in a PostgreSQL database.
The company needs an end-to-end solution that will give business analysts the ability to prepare data for processing and to predict future production volume based the previous year's production volume. The solution must not require the company to have coding knowledge.
Which solution will meet these requirements with the LEAST effort?

  • A. Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Use AWS Glue to read the data in the S3 bucket and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.
  • B. Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.
  • C. Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.
  • D. Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Create an Amazon EMR cluster to read the S3 bucket and perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.

Answer: C

Explanation:
AWS Glue DataBrew provides a no-code data preparation interface that enables business analysts to clean and transform data from various sources, including PostgreSQL databases, without needing programming skills. Amazon SageMaker Canvas offers a no-code interface for machine learning model training and predictions, allowing users to predict future production volume without coding expertise.
This solution meets the requirements efficiently by providing end-to-end data preparation and prediction modeling without requiring coding.


NEW QUESTION # 71
A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?

  • A. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
  • B. The precision of the model is 86%, which is less than the accuracy of the model.
  • C. The precision of the model is 86%, which is greater than the accuracy of the model.
  • D. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.

Answer: D

Explanation:
Based on the model evaluation results, this is a viable model for production because the model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives. The accuracy of the model is the proportion of correct predictions out of the total predictions, which can be calculated by adding the true positives and true negatives and dividing by the total number of observations. In this case, the accuracy of the model is (10 + 76) / 100 = 0.86, which means that the model correctly predicted
86% of the customers' churn status. The cost incurred by the company as a result of false positives and false negatives is the loss or damage that the company suffers when the model makes incorrect predictions. A false positive is when the model predicts that a customer will churn, but the customer actually does not churn. A false negative is when the model predicts that a customer will not churn, but the customer actually churns. In this case, the cost of a false positive is the incentive that the company offers to the customer who is predicted to churn, which is a relatively low cost. The cost of a false negative is the revenue that the company loses when the customer churns, which is a relatively high cost. Therefore, the cost of a false positive is less than the cost of a false negative, and the company would prefer to have more false positives than false negatives.
The model has 10 false positives and 4 false negatives, which means that the company's cost is lower than if the model had more false negatives and fewer false positives.


NEW QUESTION # 72
A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s) Which visualization will accomplish this?

  • A. A scatter plot with points colored by target variable that uses (-Distributed Stochastic Neighbor Embedding (I-SNE) to visualize the large number of input variables in an easier-to-read dimension.
  • B. A histogram showing whether the most important input feature is Gaussian.
  • C. A scatter plot showing the correlation between maximum tree depth and the objective metric.
  • D. A scatter plot showing (he performance of the objective metric over each training iteration

Answer: A


NEW QUESTION # 73
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