Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 51:

  A company plans to build a custom natural language processing (NLP) model to classify and prioritize user feedback. The company hosts the data and all machine learning (ML) infrastructure in the AWS Cloud. The ML team works from the company's office, which has an IPsec VPN connection to one VPC in the AWS Cloud.

  The company has set both the enableDnsHostnames attribute and the enableDnsSupport attribute of the VPC to true. The company's DNS resolvers point to the VPC DNS. The company does not allow the ML team to access Amazon SageMaker notebooks through connections that use the public internet. The connection must stay within a private network and within the AWS internal network.

  Which solution will meet these requirements with the LEAST development effort?

  A. Create a VPC interface endpoint for the SageMaker notebook in the VPC. Access the notebook through a VPN connection and the VPC endpoint.

  B. Create a bastion host by using Amazon EC2 in a public subnet within the VPC. Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.

  C. Create a bastion host by using Amazon EC2 in a private subnet within the VPC with a NAT gateway. Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.

  D. Create a NAT gateway in the VPC. Access the SageMaker notebook HTTPS endpoint through a VPN connection and the NAT gateway.

  Correct Answer: B

• Question 52:

  A data scientist is using Amazon Comprehend to perform sentiment analysis on a dataset of one million social media posts.

  Which approach will process the dataset in the LEAST time?

  A. Use a combination of AWS Step Functions and an AWS Lambda function to call the DetectSentiment API operation for each post synchronously.

  B. Use a combination of AWS Step Functions and an AWS Lambda function to call the BatchDetectSentiment API operation with batches of up to 25 posts at a time.

  C. Upload the posts to Amazon S3. Pass the S3 storage path to an AWS Lambda function that calls the StartSentimentDetectionJob API operation.

  D. Use an AWS Lambda function to call the BatchDetectSentiment API operation with the whole dataset.

  Correct Answer: D

• Question 53:

  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. Aggregate sales from stores in the same geographic area.

  B. Apply smoothing to correct for seasonal variation.

  C. Change the forecast frequency from daily to weekly.

  D. Replace missing values in the dataset by using linear interpolation.

  Correct Answer: A

• Question 54:

  A mining company wants to use machine learning (ML) models to identify mineral images in real time. A data science team built an image recognition model that is based on convolutional neural network (CNN). The team trained the model on Amazon SageMaker by using GPU instances. The team will deploy the model to a SageMaker endpoint.

  The data science team already knows the workload traffic patterns. The team must determine instance type and configuration for the workloads.

  Which solution will meet these requirements with the LEAST development effort?

  A. Register the model artifact and container to the SageMaker Model Registry. Use the SageMaker Inference Recommender Default job type. Provide the known traffic pattern for load testing to select the best instance type and configuration based on the workloads.

  B. Register the model artifact and container to the SageMaker Model Registry. Use the SageMaker Inference Recommender Advanced job type. Provide the known traffic pattern for load testing to select the best instance type and configuration based on the workloads.

  C. Deploy the model to an endpoint by using GPU instances. Use AWS Lambda and Amazon API Gateway to handle invocations from the web. Use open-source tools to perform load testing against the endpoint and to select the best instance type and configuration.

  D. Deploy the model to an endpoint by using CPU instances. Use AWS Lambda and Amazon API Gateway to handle invocations from the web. Use open-source tools to perform load testing against the endpoint and to select the best instance type and configuration.

  Correct Answer: B

• Question 55:

  A company is building custom deep learning models in Amazon SageMaker by using training and inference containers that run on Amazon EC2 instances. The company wants to reduce training costs but does not want to change the current architecture. The SageMaker training job can finish after interruptions. The company can wait days for the results.

  Which combination of resources should the company use to meet these requirements MOST cost-effectively? (Choose two.)

  A. On-Demand Instances

  B. Checkpoints

  C. Reserved Instances

  D. Incremental training

  E. Spot instances

  Correct Answer: CE

• Question 56:

  A company hosts a public web application on AWS. The application provides a user feedback feature that consists of free-text fields where users can submit text to provide feedback. The company receives a large amount of free-text user feedback from the online web application. The product managers at the company classify the feedback into a set of fixed categories including user interface issues, performance issues, new feature request, and chat issues for further actions by the company's engineering teams.

  A machine learning (ML) engineer at the company must automate the classification of new user feedback into these fixed categories by using Amazon SageMaker. A large set of accurate data is available from the historical user feedback that the product managers previously classified.

  Which solution should the ML engineer apply to perform multi-class text classification of the user feedback?

  A. Use the SageMaker Latent Dirichlet Allocation (LDA) algorithm.

  B. Use the SageMaker BlazingText algorithm.

  C. Use the SageMaker Neural Topic Model (NTM) algorithm.

  D. Use the SageMaker CatBoost algorithm.

  Correct Answer: B

• Question 57:

  A digital media company wants to build a customer churn prediction model by using tabular data. The model should clearly indicate whether a customer will stop using the company's services. The company wants to clean the data because the data contains some empty fields, duplicate values, and rare values.

  Which solution will meet these requirements with the LEAST development effort?

  A. Use SageMaker Canvas to automatically clean the data and to prepare a categorical model.

  B. Use SageMaker Data Wrangler to clean the data. Use the built-in SageMaker XGBoost algorithm to train a classification model.

  C. Use SageMaker Canvas automatic data cleaning and preparation tools. Use the built-in SageMaker XGBoost algorithm to train a regression model.

  D. Use SageMaker Data Wrangler to clean the data. Use the SageMaker Autopilot to train a regression model

  Correct Answer: A

• Question 58:

  An exercise analytics company wants to predict running speeds for its customers by using a dataset that contains multiple health-related features for each customer. Some of the features originate from sensors that provide extremely noisy values.

  The company is training a regression model by using the built-in Amazon SageMaker linear learner algorithm to predict the running speeds. While the company is training the model, a data scientist observes that the training loss decreases to almost zero, but validation loss increases.

  Which technique should the data scientist use to optimally fit the model?

  A. Add L1 regularization to the linear learner regression model.

  B. Perform a principal component analysis (PCA) on the dataset. Use the linear learner regression model.

  C. Perform feature engineering by including quadratic and cubic terms. Train the linear learner regression model.

  D. Add L2 regularization to the linear learner regression model.

  Correct Answer: C

• Question 59:

  A company is building a new supervised classification model in an AWS environment. The company's data science team notices that the dataset has a large quantity of variables Ail the variables are numeric. The model accuracy for training and validation is low. The model's processing time is affected by high latency The data science team needs to increase the accuracy of the model and decrease the processing.

  How it should the data science team do to meet these requirements?

  A. Create new features and interaction variables.

  B. Use a principal component analysis (PCA) model.

  C. Apply normalization on the feature set.

  D. Use a multiple correspondence analysis (MCA) model

  Correct Answer: B

  The best way to meet the requirements is to use a principal component analysis (PCA) model, which is a technique that reduces the dimensionality of the dataset by transforming the original variables into a smaller set of new variables, called

  principal components, that capture most of the variance and information in the data1. This technique has the following advantages:

  It can increase the accuracy of the model by removing noise, redundancy, and multicollinearity from the data, and by enhancing the interpretability and generalization of the model23. It can decrease the processing time of the model by

  reducing the number of features and the computational complexity of the model, and by improving the convergence and stability of the model45. It is suitable for numeric variables, as it relies on the covariance or correlation matrix of the data,

  and it can handle a large quantity of variables, as it can extract the most relevant ones16. The other options are not effective or appropriate, because they have the following drawbacks:

  A: Creating new features and interaction variables can increase the accuracy of the model by capturing more complex and nonlinear relationships in the data, but it can also increase the processing time of the model by adding more features

  and increasing the computational complexity of the model7. Moreover, it can introduce more noise, redundancy, and multicollinearity in the data, which can degrade the performance and interpretability of the model8.

  C: Applying normalization on the feature set can increase the accuracy of the model by scaling the features to a common range and avoiding the dominance of some features over others, but it can also decrease the processing time of the

  model by reducing the numerical instability and improving the convergence of the model . However, normalization alone is not enough to address the high dimensionality and high latency issues of the dataset, as it does not reduce the

  number of features or the variance in the data.

  D: Using a multiple correspondence analysis (MCA) model is not suitable for numeric variables, as it is a technique that reduces the dimensionality of the dataset by transforming the original categorical variables into a smaller set of new

  variables, called factors, that capture most of the inertia and information in the data. MCA is similar to PCA, but it is designed for nominal or ordinal variables, not for continuous or interval variables.

  References:

  1: Principal Component Analysis - Amazon SageMaker

  2: How to Use PCA for Data Visualization and Improved Performance in Machine Learning | by Pratik Shukla | Towards Data Science

  3: Principal Component Analysis (PCA) for Feature Selection and some of its Pitfalls | by Nagesh Singh Chauhan | Towards Data Science

  4: How to Reduce Dimensionality with PCA and Train a Support Vector Machine in Python | by James Briggs | Towards Data Science

  5: Dimensionality Reduction and Its Applications | by Aniruddha Bhandari | Towards Data Science

  6: Principal Component Analysis (PCA) in Python | by Susan Li | Towards Data Science

  7: Feature Engineering for Machine Learning | by Dipanjan (DJ) Sarkar | Towards Data Science

  8: Feature Engineering -- How to Engineer Features and How to Get Good at It | by Parul Pandey | Towards Data Science

  : [Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs. Standardization | by Benjamin Obi Tayo Ph.D. | Towards Data Science] : [Why, How and When to Scale your Features | by George Seif |

  Towards Data Science] : [Normalization vs Dimensionality Reduction | by Saurabh Annadate | Towards Data Science] : [Multiple Correspondence Analysis - Amazon SageMaker] : [Multiple Correspondence Analysis (MCA) | by Raul Eulogio |

  Towards Data Science]

• Question 60:

  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. Spell check the column. Use Amazon SageMaker one-hot encoding on the column to transform a categorical feature to a numerical feature.

  B. 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.

  C. Use Amazon SageMaker Data Wrangler similarity encoding on the column to create embeddings Of vectors Of real numbers.

  D. 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.

  Correct Answer: C

  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:Amazon SageMaker Data Wrangler -Amazon Web Services