Amazon MLS-C01 Online Practice Questions and Exam Preparation

Amazon MLS-C01 Online Questions & Answers

• Question 181:

  A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences and trends to enhance the website for better service and smart recommendations.

  Which solution should the Specialist recommend?

  A. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
  B. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database
  C. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database
  D. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database
  C. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database

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  Explanation

  In natural language processing, the latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.

  Amazon SageMaker Random Cut Forest (RCF) is an unsupervised algorithm for detecting anomalous data points within a data set

  Neural network is used for image detection.

• Question 182:

  A company uses sensors on devices such as motor engines and factory machines to measure parameters, temperature and pressure. The company wants to use the sensor data to predict equipment malfunctions and reduce services outages.

  The Machine learning (ML) specialist needs to gather the sensors data to train a model to predict device malfunctions The ML spoctafst must ensure that the data does not contain outliers before training the ..el. What can the ML specialist meet these requirements with the LEAST operational overhead?

  A. Load the data into an Amazon SagcMaker Studio notebook. Calculate the first and third quartile Use a SageMaker Data Wrangler data (low to remove only values that are outside of those quartiles.
  B. Use an Amazon SageMaker Data Wrangler bias report to find outliers in the dataset Use a Data Wrangler data flow to remove outliers based on the bias report.
  C. Use an Amazon SageMaker Data Wrangler anomaly detection visualization to find outliers in the dataset. Add a transformation to a Data Wrangler data flow to remove outliers.
  D. Use Amazon Lookout for Equipment to find and remove outliers from the dataset.
  C. Use an Amazon SageMaker Data Wrangler anomaly detection visualization to find outliers in the dataset. Add a transformation to a Data Wrangler data flow to remove outliers.

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  Explanation

  Amazon SageMaker Data Wrangler is a tool that helps data scientists and ML developers to prepare data for ML. One of the features of Data Wrangler is the anomaly detection visualization, which uses an unsupervised ML algorithm to identify outliers in the dataset based on statistical properties. The ML specialist can use this feature to quickly explore the sensor data and find any anomalous values that may affect the model performance. The ML specialist can then add a transformation to a Data Wrangler data flow to remove the outliers from the dataset. The data flow can be exported as a script or a pipeline to automate the data preparation process. This option requires the least operational overhead compared to the other options.

  References: Amazon SageMaker Data Wrangler - Amazon Web Services (AWS) Anomaly Detection Visualization - Amazon SageMaker Transform Data - Amazon SageMaker

• Question 183:

  A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences. The aim is to then run Word2Vec to generate embeddings of the sentences and enable

  different types of predictions.

  Here is an example from the dataset:

  "The quck BROWN FOX jumps over the lazy dog."

  Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Choose three.)

  A. Perform part-of-speech tagging and keep the action verb and the nouns only
  B. Normalize all words by making the sentence lowercase
  C. Remove stop words using an English stopword dictionary.
  D. Correct the typography on "quck" to "quick."
  E. One-hot encode all words in the sentence
  F. Tokenize the sentence into words.
  B. Normalize all words by making the sentence lowercase
  C. Remove stop words using an English stopword dictionary.
  F. Tokenize the sentence into words.

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  Explanation

  https://towardsdatascience.com/nlp-extracting-the-main-topics-from-your-dataset-using-lda-in-minutes-21486f5aa925

• Question 184:

  An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models During the model evaluation the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images Which of the following should be used to resolve this issue? (Select TWO)

  A. Add vanishing gradient to the model
  B. Perform data augmentation on the training data
  C. Make the neural network architecture complex.
  D. Use gradient checking in the model
  E. Add L2 regularization to the model
  B. Perform data augmentation on the training data
  E. Add L2 regularization to the model

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  Explanation

  https://www.kdnuggets.com/2019/12/5-techniques-prevent-overfitting-neural-networks.html 5 techniques to prevent overfitting:

  1.Simplifying the model

  2.Early stopping

  3.Use data argumentation

  4.Use regularization

  5.Use dropouts

• Question 185:

  A company wants to use machine learning (ML) to improve its customer churn prediction model. The company stores data in an Amazon Redshift data warehouse.

  A data science team wants to use Amazon Redshift machine learning (Amazon Redshift ML) to build a model and run predictions for new data directly within the data warehouse.

  Which combination of steps should the company take to use Amazon Redshift ML to meet these requirements? (Choose three.)

  A. Define the feature variables and target variable for the churn prediction model.
  B. Use the SOL EXPLAIN_MODEL function to run predictions.
  C. Write a CREATE MODEL SQL statement to create a model.
  D. Use Amazon Redshift Spectrum to train the model.
  E. Manually export the training data to Amazon S3.
  F. Use the SQL prediction function to run predictions.
  A. Define the feature variables and target variable for the churn prediction model.
  C. Write a CREATE MODEL SQL statement to create a model.
  F. Use the SQL prediction function to run predictions.

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  Explanation

• Question 186:

  A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing (NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers.

  The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset.

  Which solution for text extraction and entity detection will require the LEAST amount of effort?

  A. Extract text from receipt images by using Amazon Textract. Use the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities.
  B. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use the NER deep learning model to extract entities.
  C. Extract text from receipt images by using Amazon Textract. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.
  D. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.
  C. Extract text from receipt images by using Amazon Textract. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.

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  Explanation

  https://aws.amazon.com/blogs/machine-learning/building-an-nlp-powered-search-index-with-amazon-textract-and-amazon-comprehend/

• Question 187:

  A company stores its documents in Amazon S3 with no predefined product categories. A data scientist needs to build a machine learning model to categorize the documents for all the company's products. Which solution will meet these requirements with the MOST operational efficiency?

  A. Build a custom clustering model. Create a Dockerfile and build a Docker image. Register the Docker image in Amazon Elastic Container Registry (Amazon ECR). Use the custom image in Amazon SageMaker to generate a trained model.
  B. Tokenize the data and transform the data into tabular data. Train an Amazon SageMaker k-means model to generate the product categories.
  C. Train an Amazon SageMaker Neural Topic Model (NTM) model to generate the product categories.
  D. Train an Amazon SageMaker Blazing Text model to generate the product categories.
  C. Train an Amazon SageMaker Neural Topic Model (NTM) model to generate the product categories.

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  Explanation

• Question 188:

  The chief editor for a product catalog wants the research and development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand. The team has a set of training data.

  Which machine learning algorithm should the researchers use that BEST meets their requirements?

  A. Latent Dirichlet Allocation (LDA)
  B. Recurrent neural network (RNN)
  C. K-means
  D. Convolutional neural network (CNN)
  D. Convolutional neural network (CNN)

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  Explanation

• Question 189:

  While working on a neural network project, a Machine Learning Specialist discovers thai some features in the data have very high magnitude resulting in this data being weighted more in the cost function. What should the Specialist do to ensure better convergence during backpropagation?

  A. Dimensionality reduction
  B. Data normalization
  C. Model regulanzation
  D. Data augmentation for the minority class
  D. Data augmentation for the minority class

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  Explanation

• Question 190:

  A company's machine learning (ML) specialist is building a computer vision model to classify 10 different traffic signs. The company has stored 100 images of each class in Amazon S3, and the company has another 10,000 unlabeled images.

  Which actions should the ML specialist take to address this problem? (Choose two.)

  A. Use Amazon SageMaker Ground Truth to label the unlabeled images.
  B. Use image preprocessing to transform the images into grayscale images.
  C. Use data augmentation to rotate and translate the labeled images.
  D. Replace the activation of the last layer with a sigmoid.
  E. Use the Amazon SageMaker k-nearest neighbors (k-NN) algorithm to label the unlabeled images.
  D. Replace the activation of the last layer with a sigmoid.
  E. Use the Amazon SageMaker k-nearest neighbors (k-NN) algorithm to label the unlabeled images.

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  Explanation