Practice Free MLS-C01 Exam Online Questions
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen
Which combination of algorithms would provide the appropriate insights? (Select TWO)
- A . The factorization machines (FM) algorithm
- B . The Latent Dirichlet Allocation (LDA) algorithm
- C . The principal component analysis (PCA) algorithm
- D . The k-means algorithm
- E . The Random Cut Forest (RCF) algorithm
A company uses camera images of the tops of items displayed on store shelves to determine which items were removed and which ones still remain. After several hours of data labeling, the company has a total of 1,000 hand-labeled images covering 10 distinct items. The training results were poor.
Which machine learning approach fulfills the company’s long-term needs?
- A . Convert the images to grayscale and retrain the model
- B . Reduce the number of distinct items from 10 to 2, build the model, and iterate
- C . Attach different colored labels to each item, take the images again, and build the model
- D . Augment training data for each item using image variants like inversions and translations, build the model, and iterate.
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers.
Currently, the company has the following data in Amazon Aurora
• Profiles for all past and existing customers
• Profiles for all past and existing insured pets
• Policy-level information
• Premiums received
• Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?
- A . Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
- B . Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
- C . Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
- D . Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
A machine learning specialist is preparing data for training on Amazon SageMaker. The specialist is using one of the SageMaker built-in algorithms for the training. The dataset is stored in .CSV format and is transformed into a numpy.array, which appears to be negatively affecting the speed of the training.
What should the specialist do to optimize the data for training on SageMaker?
- A . Use the SageMaker batch transform feature to transform the training data into a DataFrame.
- B . Use AWS Glue to compress the data into the Apache Parquet format.
- C . Transform the dataset into the RecordIO protobuf format.
- D . Use the SageMaker hyperparameter optimization feature to automatically optimize the data.
A machine learning (ML) specialist needs to solve a binary classification problem for a marketing dataset. The ML specialist must maximize the Area Under the ROC Curve (AUC) of the algorithm by training an XGBoost algorithm. The ML specialist must find values for the eta, alpha, min_child_weight, and max_depth hyperparameter that will generate the most accurate model.
Which approach will meet these requirements with the LEAST operational overhead?
- A . Use a bootstrap script to install scikit-learn on an Amazon EMR cluster. Deploy the EMR cluster.
Apply k-fold cross-validation methods to the algorithm. - B . Deploy Amazon SageMaker prebuilt Docker images that have scikit-learn installed. Apply k-fold cross-validation methods to the algorithm.
- C . Use Amazon SageMaker automatic model tuning (AMT). Specify a range of values for each hyperparameter.
- D . Subscribe to an AUC algorithm that is on AWS Marketplace. Specify a range of values for each hyperparameter.
A company is building a line-counting application for use in a quick-service restaurant. The company wants to use video cameras pointed at the line of customers at a given register to measure how many people are in line and deliver notifications to managers if the line grows too long. The restaurant locations have limited bandwidth for connections to external services and cannot accommodate multiple video streams without impacting other operations.
Which solution should a machine learning specialist implement to meet these requirements?
- A . Install cameras compatible with Amazon Kinesis Video Streams to stream the data to AWS over the restaurant’s existing internet connection. Write an AWS Lambda function to take an image and send it to Amazon Rekognition to count the number of faces in the image. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.
- B . Deploy AWS DeepLens cameras in the restaurant to capture video. Enable Amazon Rekognition on the AWS DeepLens device, and use it to trigger a local AWS Lambda function when a person is recognized. Use the Lambda function to send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.
- C . Build a custom model in Amazon SageMaker to recognize the number of people in an image. Install cameras compatible with Amazon Kinesis Video Streams in the restaurant. Write an AWS Lambda function to take an image. Use the SageMaker endpoint to call the model to count people. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.
- D . Build a custom model in Amazon SageMaker to recognize the number of people in an image. Deploy AWS DeepLens cameras in the restaurant. Deploy the model to the cameras. Deploy an AWS Lambda function to the cameras to use the model to count people and send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.
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 . Use the event tracker in Amazon Personalize to include real-time user interactions.
- B . Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
- 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.
A machine learning (ML) specialist is training a linear regression model. The specialist notices that the model is overfitting. The specialist applies an L1 regularization parameter and runs the model again. This change results in all features having zero weights.
What should the ML specialist do to improve the model results?
- A . Increase the L1 regularization parameter. Do not change any other training parameters.
- B . Decrease the L1 regularization parameter. Do not change any other training parameters.
- C . Introduce a large L2 regularization parameter. Do not change the current L1 regularization value.
- D . Introduce a small L2 regularization parameter. Do not change the current L1 regularization value.
A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images. The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.
What changes should the Specialist consider to solve this issue? (Choose three.)
- A . Choose a higher number of layers
- B . Choose a lower number of layers
- C . Choose a smaller learning rate
- D . Enable dropout
- E . Include all the images from the test set in the training set
- F . Enable early stopping
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 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.