Free Amazon Web Services MLS-C01 Practice Exam with Questions & Answers | Set: 3

 Binary classification is a type of supervised learning problem where the goal is to predict a categorical label that has only two possible values, such as Yes or No, True or False, Positive or Negative. In this case, the label is whether a transaction is fraudulent or not, which is a binary outcome. Binary classification can be used to estimate the probability of an observation belonging to a certain class, such as the probability of a transaction being fraudulent. This can help the business to make decisions based on the risk level of each transaction. References:

Binary Classification - Amazon Machine Learning

AWS Certified Machine Learning - Specialty Sample Questions

The problem involves assigning multiple related, non-mutually exclusive categories (like "yogurt", "snack", and "dairy product") to customer comments referencing specific products. Amazon Comprehend provides custom classification capabilities, and in this case, the appropriate mode is multi-label, not multi-class.

“Use multi-label mode when each document can belong to more than one class. This is appropriate for situations where the categories are not mutually exclusive.”

This allows a single document to be tagged with several relevant labels simultaneously, which is precisely the use case described.

The factors that will adversely impact the performance of the forecast model are:

Sales data is aggregated by week. This will reduce the granularity and resolution of the data, and make it harder to capture the daily patterns and variations in sales volume. The data scientist should request daily sales data from the source database to enable building a daily model, which will be more accurate and useful for the prediction task.

Sales data is missing zero entries for item sales. This will introduce bias and incompleteness in the data, and make it difficult to account for the items that have no demand or are out of stock. The data scientist should request that item sales data from the source database include zero entries to enable building the model, which will be more robust and realistic.

The other options are not valid because:

Detecting seasonality for the majority of stores will not be an issue, as sales data is highly consistent from week to week. Requesting categorical data to relate new stores with similar stores that have more historical data may not improve the model performance significantly, and may introduce unnecessary complexity and noise.

The sales data does not need to have more variance, as it reflects the actual demand and behavior of the customers. Requesting external sales data from other industries will not improve the model’s ability to generalize, but may introduce irrelevant and misleading information.

Only 100 MB of sales data is not a problem, as it is sufficient to train a forecast model with Amazon S3 and Amazon Forecast. Requesting 10 years of sales data will not provide much benefit, as it may contain outdated and obsolete information that does not reflect the current market trends and customer preferences.

Amazon Forecast

Forecasting: Principles and Practice

 The best way to securely access data stored in a specific Amazon S3 bucket from an Amazon SageMaker notebook instance is to attach a policy to the IAM role associated with the notebook that allows GetObject, PutObject, and ListBucket operations to the specific S3 bucket. This way, the notebook can use the AWS SDK or CLI to access the S3 bucket without exposing any credentials or requiring any additional configuration. This is also the recommended approach by AWS for granting access to S3 from SageMaker. References:

Amazon SageMaker Roles

Accessing Amazon S3 from a SageMaker Notebook Instance

A scatter plot allows the first two dimensions to be represented by coordinates, while color and size represent the third and fourth dimensions, respectively.

From AWS documentation:

“Scatter plots in Amazon SageMaker Canvas and Data Wrangler can visualize relationships between two numeric variables. Additional dimensions can be represented using point size and color.”

— AWS SageMaker Canvas documentation

Residuals are the differences between the actual and predicted values of the target variable in a regression model. A histogram of residuals is a graphical tool that can help evaluate the performance and assumptions of the model. Ideally, the histogram of residuals should have a zero-centered bell shape, which indicates that the residuals are normally distributed with a mean of zero and a constant variance. This means that the model has captured the true relationship between the input and output variables, and that the errors are random and unbiased. However, if the histogram of residuals does not have a zero-centered bell shape, as shown in the image, this means that the model might have prediction errors over a range of target values. This is because the residuals do not form a symmetrical and homogeneous distribution around zero, which implies that the model has some systematic bias or heteroscedasticity. This can affect the accuracy and validity of the model, and indicate that the model needs to be improved or modified.

Residual Analysis in Regression - Statistics By Jim

How to Check Residual Plots for Regression Analysis - dummies

Histogram of Residuals - Statistics How To

 The business problem of predicting whether a new credit card applicant will default on a credit card payment can be framed as a binary classification problem. Binary classification is the task of predicting a discrete class label output for an example, where the class label can only take one of two possible values. In this case, the class label can be either “default” or “no default”, indicating whether the applicant will or will not default on a credit card payment. A binary classification model can return the probability that a given applicant belongs to each class, and then assign the applicant to the class with the highest probability. For example, if the model predicts that an applicant has a 0.8 probability of defaulting and a 0.2 probability of not defaulting, then the model will classify the applicant as “default”. Binary classification is suitable for this problem because the outcome of interest is categorical and binary, and the model needs to return the probability of each outcome.

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Classification vs Regression in Machine Learning

A pronunciation lexicon is a file that defines how words or phrases should be pronounced by Amazon Polly. A lexicon can help customize the speech output for words that are uncommon, foreign, or have multiple pronunciations. A lexicon must conform to the Pronunciation Lexicon Specification (PLS) standard and can be stored in an AWS region using the Amazon Polly API. To use a lexicon for synthesizing speech, the lexicon name must be specified in the SSML tag. For example, the following lexicon defines how to pronounce the acronym W3C:

W3C World Wide Web Consortium</alias>

To use this lexicon, the text input must include the following SSML tag:

xmlns=“http://www.w3.org/2001/10/synthesis” xml:lang=“en-US” > The W3C is an international community that develops open standards to ensure the long-term growth of the Web.

Customize pronunciation using lexicons in Amazon Polly: A blog post that explains how to use lexicons for creating custom pronunciations.

Managing Lexicons: A documentation page that describes how to store and retrieve lexicons using the Amazon Polly API.

Stratified sampling is a technique that preserves the class distribution of the original dataset when creating a smaller or split dataset. This means that the proportion of examples from each class in the original dataset is maintained in the smaller or split dataset. Stratified sampling can help improve the validation accuracy of the model by ensuring that the validation dataset is representative of the original dataset and not biased towards any class. This can reduce the variance and overfitting of the model and increase its generalization ability. Stratified sampling can be applied to both oversampling and undersampling methods, depending on whether the goal is to increase or decrease the size of the dataset.

The other options are not effective ways to improve the validation accuracy of the model. Acquiring additional data about the majority classes in the original dataset will only increase the imbalance and make the model more biased towards the majority classes. Using a smaller, randomly sampled version of the training dataset will not guarantee that the class distribution is preserved and may result in losing important information from the minority classes. Performing systematic sampling on the original dataset will also not ensure that the class distribution is preserved and may introduce sampling bias if the original dataset is ordered or grouped by class.

A hyperparameter tuning job is a feature of Amazon SageMaker that allows automatically finding the best combination of hyperparameters for a machine learning model. Hyperparameters are high-level parameters that influence the learning process and the performance of the model, such as the learning rate, the number of layers, the regularization factor, etc. A hyperparameter tuning job works by launching multiple training jobs with different hyperparameters, evaluating the results using an objective metric, and choosing the next set of hyperparameters to try based on a search strategy. The objective metric is a measure of the quality of the model, such as accuracy, precision, recall, etc. The search strategy is a method of exploring the hyperparameter space, such as random search, grid search, or Bayesian optimization.

Among the four options, option C is the most repeatable and requires the least amount of effort to use hyperparameter optimization to increase the model’s accuracy. This option involves the following steps:

Create a hyperparameter tuning job: Amazon SageMaker provides an easy-to-use interface for creating a hyperparameter tuning job, either through the AWS Management Console, the AWS CLI, or the AWS SDKs. To create a hyperparameter tuning job, the Machine Learning Specialist needs to specify the following information:

The name and type of the algorithm to use, either a built-in algorithm or a custom algorithm.

The ranges and types of the hyperparameters to tune, such as categorical, continuous, or integer.

The name and type of the objective metric to optimize, such as accuracy, and whether to maximize or minimize it.

The resource limits for the tuning job, such as the maximum number of training jobs and the maximum parallel training jobs.

The input data channels and the output data location for the training jobs.

The configuration of the training instances, such as the instance type, the instance count, the volume size, etc.

Set the accuracy as an objective metric: To use accuracy as an objective metric, the Machine Learning Specialist needs to ensure that the training algorithm writes the accuracy value to a file called metric_definitions in JSON format and prints it to stdout or stderr. For example, the file can contain the following content:

This means that the training algorithm prints a line like this:

Amazon SageMaker reads the accuracy value from the line and uses it to evaluate and compare the training jobs.

The other options are not as repeatable and require more effort than option C for the following reasons:

Option A: This option requires manually launching multiple training jobs in parallel with different hyperparameters, which can be tedious and error-prone. It also requires manually monitoring and comparing the results of the training jobs, which can be time-consuming and subjective.

Option B: This option requires writing code to create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches the training job with a defined list of hyperparameters, which can be complex and challenging. It also requires maintaining and updating the list of hyperparameters, which can be inefficient and suboptimal.

Option D: This option requires writing code to create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter, which can be unreliable and unpredictable. It also requires defining and implementing a stopping criterion, which can be arbitrary and inconsistent.

Automatic Model Tuning - Amazon SageMaker

Define Metrics to Monitor Model Performance