2025 PROFESSIONAL EXAM MLS-C01 DURATION | 100% FREE MLS-C01 EXAM COST

2025 Professional Exam MLS-C01 Duration | 100% Free MLS-C01 Exam Cost

2025 Professional Exam MLS-C01 Duration | 100% Free MLS-C01 Exam Cost

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The Amazon MLS-C01 exam covers a wide range of topics, including data pre-processing, feature engineering, model selection and evaluation, model training and optimization, deployment and monitoring, and ethics and fairness in machine learning. MLS-C01 Exam also tests the candidate's ability to use AWS tools and services such as Amazon SageMaker, Amazon S3, Amazon EC2, and Amazon EMR.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q152-Q157):

NEW QUESTION # 152
A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices. The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML) models. There are more than 1,000 features, and the company wants to determine the primary features that contribute to the sales price.
Which techniques should the company use for feature selection? (Choose three.)

  • A. Data augmentation
  • B. Data binning
  • C. Correlation plot with heat maps
  • D. Univariate selection
  • E. Feature importance with a tree-based classifier
  • F. Data scaling with standardization and normalization

Answer: C,D,E

Explanation:
Feature selection is the process of selecting a subset of extracted features that are relevant and contribute to minimizing the error rate of a trained model. Some techniques for feature selection are:
Correlation plot with heat maps: This technique visualizes the correlation between features using a color- coded matrix. Features that are highly correlated with each other or with the target variable can be identified and removed to reduce redundancy and noise.
Univariate selection: This technique evaluates each feature individually based on a statistical test, such as chi- square, ANOVA, or mutual information, and selects the features that have the highest scores or p-values. This technique is simple and fast, but it does not consider the interactions between features.
Feature importance with a tree-based classifier: This technique uses a tree-based classifier, such as random forest or gradient boosting, to rank the features based on their importance in splitting the nodes. Features that have low importance scores can be dropped from the model. This technique can capture the non-linear relationships and interactions between features.
The other options are not techniques for feature selection, but rather for feature engineering, which is the process of creating, transforming, or extracting features from the original data. Feature engineering can improve the performance and interpretability of the model, but it does not reduce the number of features.
Data scaling with standardization and normalization: This technique transforms the features to have a common scale, such as zero mean and unit variance, or a range between 0 and 1. This technique can help some algorithms, such as k-means or logistic regression, to converge faster and avoid numerical instability, but it does not change the number of features.
Data binning: This technique groups the continuous features into discrete bins or categories based on some criteria, such as equal width, equal frequency, or clustering. This technique can reduce the noise and outliers in the data, and also create ordinal or nominal features that can be used for some algorithms, such as decision trees or naive Bayes, but it does not reduce the number of features.
Data augmentation: This technique generates new data from the existing data by applying some transformations, such as rotation, flipping, cropping, or noise addition. This technique can increase the size and diversity of the data, and help prevent overfitting, but it does not reduce the number of features.
Feature engineering - Machine Learning Lens
Amazon SageMaker Autopilot now provides feature selection and the ability to change data types while creating an AutoML experiment Feature Selection in Machine Learning | Baeldung on Computer Science Feature Selection in Machine Learning: An easy Introduction


NEW QUESTION # 153
A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?

  • A. Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
  • B. Use the event tracker in Amazon Personalize to include real-time user interactions.
  • C. Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
  • D. Add event type and event value fields to the interactions dataset in Amazon Personalize.

Answer: B

Explanation:
The best option is to use the event tracker in Amazon Personalize to include real-time user interactions. This will allow the model to learn from the feedback of the customers during the marketing campaign and adjust the recommendations accordingly. The event tracker can capture click-through, add-to-cart, purchase, and other types of events that indicate the user's preferences. By using the event tracker, the company can improve the relevance and freshness of the recommendations and avoid the decrease in sales.
The other options are not as effective as using the event tracker. Adding user metadata and using the HRNN-Metadata recipe in Amazon Personalize can help capture the user's attributes and preferences, but it will not reflect the changes in user behavior during the marketing campaign. Implementing a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker can also provide personalized recommendations, but it will require more time and effort to train and deploy the model. Adding event type and event value fields to the interactions dataset in Amazon Personalize can help capture the importance and context of each interaction, but it will not update the model with the latest user feedback.
References:
Recording events - Amazon Personalize
Using real-time events - Amazon Personalize


NEW QUESTION # 154
A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.
Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

  • A. Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.
  • B. Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.
  • C. Concatenate the features with high correlation scores by using a Jupyter notebook.
  • D. Drop the features with low correlation scores by using a Jupyter notebook.

Answer: B


NEW QUESTION # 155
A data scientist receives a collection of insurance claim records. Each record includes a claim ID. the final outcome of the insurance claim, and the date of the final outcome.
The final outcome of each claim is a selection from among 200 outcome categories. Some claim records include only partial information. However, incomplete claim records include only 3 or 4 outcome ...gones from among the 200 available outcome categories. The collection includes hundreds of records for each outcome category. The records are from the previous 3 years.
The data scientist must create a solution to predict the number of claims that will be in each outcome category every month, several months in advance.
Which solution will meet these requirements?

  • A. Perform classification every month by using supervised learning of the 20X3 outcome categories based on claim contents.
  • B. Perform classification by using supervised learning of the outcome categories for which partial information on claim contents is provided. Perform forecasting by using claim IDs and dates for all other outcome categories.
  • C. Perform forecasting by using claim IDs and dates to identify the expected number ot claims in each outcome category every month.
  • D. Perform reinforcement learning by using claim IDs and dates Instruct the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month

Answer: C

Explanation:
The best solution for this scenario is to perform forecasting by using claim IDs and dates to identify the expected number of claims in each outcome category every month. This solution has the following advantages:
* It leverages the historical data of claim outcomes and dates to capture the temporal patterns and trends of the claims in each category1.
* It does not require the claim contents or any other features to make predictions, which simplifies the data preparation and reduces the impact of missing or incomplete data2.
* It can handle the high cardinality of the outcome categories, as forecasting models can output multiple values for each time point3.
* It can provide predictions for several months in advance, which is useful for planning and budgeting purposes4.
The other solutions have the following drawbacks:
* A: Performing classification every month by using supervised learning of the 200 outcome categories based on claim contents is not suitable, because it assumes that the claim contents are available and complete for all the records, which is not the case in this scenario2. Moreover, classification models usually output a single label for each input, which is not adequate for predicting the number of claims in each category3. Additionally, classification models do not account for the temporal aspect of the data, which is important for forecasting1.
* B: Performing reinforcement learning by using claim IDs and dates and instructing the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month is not feasible, because it requires a feedback loop between the model and the agents, which might not be available or reliable in this scenario5. Furthermore, reinforcement learning is more suitable for sequential decision making problems, where the model learns from its actions and rewards, rather than forecasting problems, where the model learns from historical data and outputs future values6.
* D: Performing classification by using supervised learning of the outcome categories for which partial information on claim contents is provided and performing forecasting by using claim IDs and dates for all other outcome categories is not optimal, because it combines two different methods that might not be consistent or compatible with each other7. Also, this solution suffers from the same limitations as solution A, such as the dependency on claim contents, the inability to handle multiple outputs, and the ignorance of temporal patterns123.
1: Time Series Forecasting - Amazon SageMaker
2: Handling Missing Data for Machine Learning | AWS Machine Learning Blog
3: Forecasting vs Classification: What's the Difference? | DataRobot
4: Amazon Forecast - Time Series Forecasting Made Easy | AWS News Blog
5: Reinforcement Learning - Amazon SageMaker
6: What is Reinforcement Learning? The Complete Guide | Edureka
7: Combining Machine Learning Models | by Will Koehrsen | Towards Data Science


NEW QUESTION # 156
A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The Specialist is asked to score a model that was run against the dataset The Specialist has been advised that identifying valid transactions is equally as important as identifying fraudulent transactions What metric is BEST suited to score the model?

  • A. Area Under the ROC Curve (AUC)
  • B. Recall
  • C. Root Mean Square Error (RMSE)
  • D. Precision

Answer: A

Explanation:
Area Under the ROC Curve (AUC) is a metric that is best suited to score the model for the given scenario. AUC is a measure of the performance of a binary classifier, such as a model that predicts whether a credit card transaction is valid or fraudulent. AUC is calculated based on the Receiver Operating Characteristic (ROC) curve, which is a plot that shows the trade-off between the true positive rate (TPR) and the false positive rate (FPR) of the classifier as the decision threshold is varied. The TPR, also known as recall or sensitivity, is the proportion of actual positive cases (fraudulent transactions) that are correctly predicted as positive by the classifier. The FPR, also known as the fall-out, is the proportion of actual negative cases (valid transactions) that are incorrectly predicted as positive by the classifier. The ROC curve illustrates how well the classifier can distinguish between the two classes, regardless of the class distribution or the error costs. A perfect classifier would have a TPR of 1 and an FPR of 0 for all thresholds, resulting in a ROC curve that goes from the bottom left to the top left and then to the top right of the plot. A random classifier would have a TPR and an FPR that are equal for all thresholds, resulting in a ROC curve that goes from the bottom left to the top right of the plot along the diagonal line. AUC is the area under the ROC curve, and it ranges from 0 to 1. A higher AUC indicates a better classifier, as it means that the classifier has a higher TPR and a lower FPR for all thresholds. AUC is a useful metric for imbalanced classification problems, such as the credit card transaction dataset, because it is insensitive to the class imbalance and the error costs. AUC can capture the overall performance of the classifier across all possible scenarios, and it can be used to compare different classifiers based on their ROC curves.
The other options are not as suitable as AUC for the given scenario for the following reasons:
Precision: Precision is the proportion of predicted positive cases (fraudulent transactions) that are actually positive. Precision is a useful metric when the cost of a false positive is high, such as in spam detection or medical diagnosis. However, precision is not a good metric for imbalanced classification problems, because it can be misleadingly high when the positive class is rare. For example, a classifier that predicts all transactions as valid would have a precision of 0, but a very high accuracy of 99%. Precision is also dependent on the decision threshold and the error costs, which may vary for different scenarios.
Recall: Recall is the same as the TPR, and it is the proportion of actual positive cases (fraudulent transactions) that are correctly predicted as positive by the classifier. Recall is a useful metric when the cost of a false negative is high, such as in fraud detection or cancer diagnosis. However, recall is not a good metric for imbalanced classification problems, because it can be misleadingly low when the positive class is rare. For example, a classifier that predicts all transactions as fraudulent would have a recall of 1, but a very low accuracy of 1%. Recall is also dependent on the decision threshold and the error costs, which may vary for different scenarios.
Root Mean Square Error (RMSE): RMSE is a metric that measures the average difference between the predicted and the actual values. RMSE is a useful metric for regression problems, where the goal is to predict a continuous value, such as the price of a house or the temperature of a city. However, RMSE is not a good metric for classification problems, where the goal is to predict a discrete value, such as the class label of a transaction. RMSE is not meaningful for classification problems, because it does not capture the accuracy or the error costs of the predictions.
References:
ROC Curve and AUC
How and When to Use ROC Curves and Precision-Recall Curves for Classification in Python Precision-Recall Root Mean Squared Error


NEW QUESTION # 157
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