Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 261:

  This graph shows the training and validation loss against the epochs for a neural network.

  The network being trained is as follows:

  1.

  Two dense layers, one output neuron

  2.

  100 neurons in each layer

  3.

  100 epochs

  4.

  Random initialization of weights

  

  Which technique can be used to improve model performance in terms of accuracy in the validation set?

  A. Early stopping

  B. Random initialization of weights with appropriate seed

  C. Increasing the number of epochs

  D. Adding another layer with the 100 neurons

  Correct Answer: A

  The answer is Early Stopping. Stopp the training before accuracy start do decrease.

• Question 262:

  A Machine Learning Specialist is attempting to build a linear regression model.

  

  Given the displayed residual plot only, what is the MOST likely problem with the model?

  A. Linear regression is inappropriate. The residuals do not have constant variance.

  B. Linear regression is inappropriate. The underlying data has outliers.

  C. Linear regression is appropriate. The residuals have a zero mean.

  D. Linear regression is appropriate. The residuals have constant variance.

  Correct Answer: A

  https://www.itl.nist.gov/div898/handbook/pmd/section4/pmd442.htm https://blog.minitab.com/blog/the-statistics-game/checking-the-assumption-of-constant-variance-in-regression-analyses

• Question 263:

  A large company has developed a BI application that generates reports and dashboards using data collected from various operational metrics. The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports. The company wants the executives to be able ask questions using written and spoken interfaces.

  Which combination of services can be used to build this conversational interface? (Choose three.)

  A. Alexa for Business

  B. Amazon Connect

  C. Amazon Lex

  D. Amazon Polly

  E. Amazon Comprehend

  F. Amazon Transcribe

  Correct Answer: CEF

• Question 264:

  A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied

  transfer learning on a neural network that was pretrained on ImageNet with this dataset.

  The company requires at least 85% accuracy to make use of the model.

  After an exhaustive grid search, the optimal hyperparameters produced the following:

  1.

  68% accuracy on the training set

  2.

  67% accuracy on the validation set

  What can the machine learning specialist do to improve the system's accuracy?

  A. Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.

  B. Add more data to the training set and retrain the model using transfer learning to reduce the bias.

  C. Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.

  D. Train a new model using the current neural network architecture.

  Correct Answer: B

• Question 265:

  A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.

  Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?

  A. Plot a histogram of the features and compute their standard deviation. Remove features with high variance.

  B. Plot a histogram of the features and compute their standard deviation. Remove features with low variance.

  C. Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.

  D. Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.

  Correct Answer: D

  https://deep-r.medium.com/difference-between-variance-co-variance-and-correlation-ea0b7ddbaa1

• Question 266:

  A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a machine learning specialist will build a binary classifier based on two features: age of account, denoted by x, and transaction month, denoted by y. The class distributions are illustrated in the provided figure. The positive class is portrayed in red, while the negative class is portrayed in black.

  

  Which model would have the HIGHEST accuracy?

  A. Linear support vector machine (SVM)

  B. Decision tree

  C. Support vector machine (SVM) with a radial basis function kernel

  D. Single perceptron with a Tanh activation function

  Correct Answer: B

  A Decision tree produces a stepwise boundary that consists of rectilinear splits in the feature space that are perpendicular to the axes. The boundary is determined by the conditions specified in the tree.

  Support Vector Machines (SVM) produce a non-linear boundary in the feature space that separates the classes. The boundary is defined as the maximum margin hyperplane, which is the line that maximally separates the classes while having the greatest margin between the classes. The boundary can be linear, polynomial, radial basis function (RBF) or other types of non-linear functions, depending on the choice of kernel and the configuration of the SVM.

• Question 267:

  A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does not achieve and acceptable recall metric. The Data Scientist has already tried varying the number and size of the MLP's hidden layers, which has not significantly improved the results. A solution to improve recall must be implemented as quickly as possible.

  Which techniques should be used to meet these requirements?

  A. Gather more data using Amazon Mechanical Turk and then retrain

  B. Train an anomaly detection model instead of an MLP

  C. Train an XGBoost model instead of an MLP

  D. Add class weights to the MLP's loss function and then retrain

  Correct Answer: D

  https://androidkt.com/set-class-weight-for-imbalance-dataset-in-keras/

• Question 268:

  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. Logistic regression

  B. Linear regression

  C. K-means

  D. Principal component analysis (PCA)

  Correct Answer: B

• Question 269:

  A Machine Learning Specialist is applying a linear least squares regression model to a dataset with 1,000 records and 50 features. Prior to training, the ML Specialist notices that two features are perfectly linearly dependent. Why could this be an issue for the linear least squares regression model?

  A. It could cause the backpropagation algorithm to fail during training

  B. It could create a singular matrix during optimization, which fails to define a unique solution

  C. It could modify the loss function during optimization, causing it to fail during training

  D. It could introduce non-linear dependencies within the data, which could invalidate the linear assumptions of the model

  Correct Answer: B

• Question 270:

  A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer base. The dataset contains thousands of columns of data and hundreds of numerical columns for each customer. The Specialist wants to identify whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible.

  What approach should the Specialist take to accomplish these tasks?

  A. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot.

  B. Run k-means using the Euclidean distance measure for different values of k and create an elbow plot.

  C. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a line graph.

  D. Run k-means using the Euclidean distance measure for different values of k and create box plots for each numerical column within each cluster.

  Correct Answer: A

  https://towardsdatascience.com/an-introduction-to-t-sne-with-python-example-5a3a293108d1