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To take the Amazon MLS-C01 Exam, candidates must have a strong background in machine learning concepts, programming languages such as Python, and experience with AWS services. MLS-C01 exam consists of multiple-choice and multiple-answer questions and is administered online. Candidates have 170 minutes to complete the exam and must achieve a passing score of 750 out of 1000 points.

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The AWS-Certified-Machine-Learning-Specialty exam covers various topics, such as data engineering, exploratory data analysis, modeling, machine learning implementation and operations, and ethical and legal considerations. Candidates should be well-versed in these topics and should have hands-on experience using AWS services, such as Amazon SageMaker, Amazon S3, Amazon EC2, and Amazon Comprehend.

The AWS Certified Machine Learning - Specialty Exam covers a wide range of topics related to machine learning, including data preparation, feature engineering, model selection and evaluation, and deployment. It also covers advanced topics such as deep learning, natural language processing, and computer vision. MLS-C01 Exam consists of multiple-choice questions and is available in English, Simplified Chinese, Korean, and Japanese.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q275-Q280):

NEW QUESTION # 275
A company deployed a machine learning (ML) model on the company website to predict real estate prices.
Several months after deployment, an ML engineer notices that the accuracy of the model has gradually decreased.
The ML engineer needs to improve the accuracy of the model. The engineer also needs to receive notifications for any future performance issues.
Which solution will meet these requirements?

A. Perform incremental training to update the model. Activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.
B. Use Amazon SageMaker Model Governance. Configure Model Governance to automatically adjust model hyper para meters. Create a performance threshold alarm in Amazon CloudWatch to send notifications.
C. Use only data from the previous several months to perform incremental training to update the model.Use Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.
D. Use Amazon SageMaker Debugger with appropriate thresholds. Configure Debugger to send Amazon CloudWatch alarms to alert the team Retrain the model by using only data from the previous several months.

Answer: A

Explanation:
Explanation
The best solution to improve the accuracy of the model and receive notifications for any future performance issues is to perform incremental training to update the model and activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications. Incremental training is a technique that allows you to update an existing model with new data without retraining the entire model from scratch. This can save time and resources, and help the model adapt to changing data patterns. Amazon SageMaker Model Monitor is a feature that continuously monitors the quality of machine learning models in production and notifies you when there are deviations in the model quality, such as data drift and anomalies. You can set up alerts that trigger actions, such as sending notifications to Amazon Simple Notification Service (Amazon SNS) topics, when certain conditions are met.
Option B is incorrect because Amazon SageMaker Model Governance is a set of tools that help you implement ML responsibly by simplifying access control and enhancing transparency. It does not provide a mechanism to automatically adjust model hyperparameters or improve model accuracy.
Option C is incorrect because Amazon SageMaker Debugger is a feature that helps you debug and optimize your model training process by capturing relevant data and providing real-time analysis. However, using Debugger alone does not update the model or monitor its performance in production. Also, retraining the model by using only data from the previous several months may not capture the full range of data variability and may introduce bias or overfitting.
Option D is incorrect because using only data from the previous several months to perform incremental training may not be sufficient to improve the model accuracy, as explained above. Moreover, this option does not specify how to activate Amazon SageMaker Model Monitor or configure the alerts and notifications.
References:
Incremental training
Amazon SageMaker Model Monitor
Amazon SageMaker Model Governance
Amazon SageMaker Debugger

NEW QUESTION # 276
A medical device company is building a machine learning (ML) model to predict the likelihood of device recall based on customer data that the company collects from a plain text survey. One of the survey questions asks which medications the customer is taking. The data for this field contains the names of medications that customers enter manually. Customers misspell some of the medication names. The column that contains the medication name data gives a categorical feature with high cardinality but redundancy.
What is the MOST effective way to encode this categorical feature into a numeric feature?

A. Use Amazon SageMaker Data Wrangler similarity encoding on the column to create embeddings Of vectors Of real numbers.
B. Use Amazon SageMaker Data Wrangler ordinal encoding on the column to encode categories into an integer between O and the total number Of categories in the column.
C. Fix the spelling in the column by using char-RNN. Use Amazon SageMaker Data Wrangler one-hot encoding to transform a categorical feature to a numerical feature.
D. Spell check the column. Use Amazon SageMaker one-hot encoding on the column to transform a categorical feature to a numerical feature.

Answer: A

Explanation:
The most effective way to encode this categorical feature into a numeric feature is to use Amazon SageMaker Data Wrangler similarity encoding on the column to create embeddings of vectors of real numbers. Similarity encoding is a technique that transforms categorical features into numerical features by computing the similarity between the categories. Similarity encoding can handle high cardinality and redundancy in categorical features, as it can group similar categories together based on their string similarity. For example, if the column contains the values "aspirin", "asprin", and "ibuprofen", similarity encoding will assign a high similarity score to "aspirin" and "asprin", and a low similarity score to "ibuprofen". Similarity encoding can also create embeddings of vectors of real numbers, which can be used as input for machine learning models.
Amazon SageMaker Data Wrangler is a feature of Amazon SageMaker that enables you to prepare data for machine learning quickly and easily. You can use SageMaker Data Wrangler to apply similarity encoding to a column of categorical data, and generate embeddings of vectors of real numbers that capture the similarity between the categories1. The other options are either less effective or more complex to implement. Spell checking the column and using one-hot encoding would require additional steps and resources, and may not capture all the misspellings or redundancies. One-hot encoding would also create a large number of features, which could increase the dimensionality and sparsity of the data. Ordinal encoding would assign an arbitrary order to the categories, which could introduce bias or noise in the data. References:
* 1: Amazon SageMaker Data Wrangler - Amazon Web Services

NEW QUESTION # 277
A retail company uses a machine learning (ML) model for daily sales forecasting. The company's brand manager reports that the model has provided inaccurate results for the past 3 weeks.
At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company's ML team is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model's inaccuracies.
What should the ML team do on the SageMaker Studio notebook to visualize the model's degradation MOST accurately?

A. Create a histogram of the model errors over the last 3 weeks. In addition, create a histogram of the model errors from before that period.
B. Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition, create a scatter plot of daily sales versus model error from before that period.
C. Create a histogram of the daily sales over the last 3 weeks. In addition, create a histogram of the daily sales from before that period.
D. Create a line chart with the weekly mean absolute error (MAE) of the model.

Answer: A

Explanation:
The best way to visualize the model's degradation is to create a histogram of the model errors over the last 3 weeks and compare it with a histogram of the model errors from before that period. A histogram is a graphical representation of the distribution of numerical data. It shows how often each value or range of values occurs in the data. A model error is the difference between the actual value and the predicted value. A high model error indicates a poor fit of the model to the data. By comparing the histograms of the model errors, the ML team can see if there is a significant change in the shape, spread, or center of the distribution. This can indicate if the model is underfitting, overfitting, or drifting from the data. A line chart or a scatter plot would not be as effective as a histogram for this purpose, because they do not show the distribution of the errors. A line chart would only show the trend of the errors over time, which may not capture the variability or outliers.
A scatter plot would only show the relationship between the errors and another variable, such as daily sales, which may not be relevant or informative for the model's performance. References:
* Histogram - Wikipedia
* Model error - Wikipedia
* SageMaker Model Monitor - visualizing monitoring results

NEW QUESTION # 278
A large consumer goods manufacturer has the following products on sale
* 34 different toothpaste variants
* 48 different toothbrush variants
* 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched Which solution should a Machine Learning Specialist apply?

A. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product
B. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
C. Train a custom ARIMA model to forecast demand for the new product.
D. Train a custom XGBoost model to forecast demand for the new product

Answer: A

Explanation:
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.

NEW QUESTION # 279
A company is building a predictive maintenance model for its warehouse equipment. The model must predict the probability of failure of all machines in the warehouse. The company has collected 10.000 event samples within 3 months. The event samples include 100 failure cases that are evenly distributed across 50 different machine types.
How should the company prepare the data for the model to improve the model's accuracy?

A. Undersample the non-failure events. Stratify the non-failure events by machine type.
B. Adjust the class weight to account for each machine type.
C. Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).
D. Undersample the non-failure events by using the Synthetic Minority Oversampling Technique (SMOTE).

Answer: C

Explanation:
In predictive maintenance, when a dataset is imbalanced (with far fewer failure cases than non-failure cases), oversampling the minority class helps the model learn from the minority class effectively. The Synthetic Minority Oversampling Technique (SMOTE) generates synthetic samples for the minority class by creating data points between existing minority class instances. This can enhance the model's ability to recognize failure patterns, particularly in imbalanced datasets.
SMOTE increases the effective presence of failure cases in the dataset, providing a balanced learning environment for the model. This is more effective than undersampling, which would risk losing important non- failure data.

NEW QUESTION # 280
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Real Amazon MLS-C01 Questions Formats - Prepare Better For Exam

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q275-Q280):

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