Amazon MLS-C01 Online Practice Questions and Exam Preparation

Amazon MLS-C01 Online Questions & Answers

• Question 131:

  A company is creating an application to identify, count, and classify animal images that are uploaded to the company's website. The company is using the Amazon SageMaker image classification algorithm with an ImageNetV2 convolutional neural network (CNN). The solution works well for most animal images but does not recognize many animal species that are less common.

  The company obtains 10,000 labeled images of less common animal species and stores the images in Amazon S3. A machine learning (ML) engineer needs to incorporate the images into the model by using Pipe mode in SageMaker.

  Which combination of steps should the ML engineer take to train the model? (Choose two.)

  A. Use a ResNet model. Initiate full training mode by initializing the network with random weights.
  B. Use an Inception model that is available with the SageMaker image classification algorithm.
  C. Create a .lst file that contains a list of image files and corresponding class labels. Upload the .lst file to Amazon S3.
  D. Initiate transfer learning. Train the model by using the images of less common species.
  E. Use an augmented manifest file in JSON Lines format.
  B. Use an Inception model that is available with the SageMaker image classification algorithm.
  D. Initiate transfer learning. Train the model by using the images of less common species.

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  Explanation

• Question 132:

  A company wants to create an artificial intelligence (Al) yoga instructor that can lead large classes of students. The company needs to create a feature that can accurately count the number of students who are in a class. The company also needs a feature that can differentiate students who are performing a yoga stretch correctly from students who are performing a stretch incorrectly.

  ...etermine whether students are performing a stretch correctly, the solution needs to measure the location and angle of each student's arms and legs A data scientist must use Amazon SageMaker to ...ss video footage of a yoga class by extracting image frames and applying computer vision models.

  Which combination of models will meet these requirements with the LEAST effort? (Select TWO.)

  A. Image Classification
  B. Optical Character Recognition (OCR)
  C. Object Detection
  D. Pose estimation
  E. Image Generative Adversarial Networks (GANs)
  C. Object Detection
  D. Pose estimation

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  Explanation

  To count the number of students who are in a class, the solution needs to detect and locate each student in the video frame. Object detection is a computer vision model that can identify and locate multiple objects in an image. To differentiate

  students who are performing a stretch correctly from students who are performing a stretch incorrectly, the solution needs to measure the location and angle of each student's arms and legs. Pose estimation is a computer vision model that

  can estimate the pose of a person by detecting the position and orientation of key body parts. Image classification, OCR, and image GANs are not relevant for this use case.

  References:

  Object Detection: A computer vision technique that identifies and locates objects within an image or video. Pose Estimation: A computer vision technique that estimates the pose of a person by detecting the position and orientation of key body

  parts. Amazon SageMaker: A fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly.

• Question 133:

  A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users.

  The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company's business goals. To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models.

  Which solution satisfies these requirements with MINIMAL effort?

  A. Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer.
  B. Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.
  C. Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices. Programmatically control which model is invoked for inference based on the medical device type.
  D. Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.
  B. Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.

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  Explanation

  Amazon SageMaker enables you to test multiple models or model versions behind the 'same endpoint' using 'production variants'.

  Each production variant identifies a machine learning (ML) model and the resources deployed for hosting the model.

  To test multiple models by distributing traffic between them, specify the 'percentage of the traffic' that gets routed to each model by specifying the 'weight' for each 'production variant' in the endpoint configuration.

• Question 134:

  A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw

  attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues.

  The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset.

  Which feature engineering technique should the Data Scientist use to meet the objectives?

  A. Run self-correlation on all features and remove highly correlated features
  B. Normalize all numerical values to be between 0 and 1
  C. Use an autoencoder or principal component analysis (PCA) to replace original features with new features
  D. Cluster raw data using k-means and use sample data from each cluster to build a new dataset
  C. Use an autoencoder or principal component analysis (PCA) to replace original features with new features

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  Explanation

  Using an autoencoder or PCA can help reduce the dimensionality of the dataset by creating new features that capture the most important information in the original dataset while discarding some of the noise and highly correlated features. This can help speed up the training time and reduce overfitting issues without losing a lot of information from the original dataset.

• Question 135:

  A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API. The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users. The VPC is configured with a single public subnet.

  Which combination of steps should the ML specialist take to secure the traffic? (Choose two.)

  A. Add a VPC endpoint policy to allow access to the IAM users.
  B. Modify the users' IAM policy to allow access to Amazon SageMaker Service API calls only.
  C. Modify the security group on the endpoint network interface to restrict access to the instances.
  D. Modify the ACL on the endpoint network interface to restrict access to the instances.
  E. Add a SageMaker Runtime VPC endpoint interface to the VPC.
  A. Add a VPC endpoint policy to allow access to the IAM users.
  C. Modify the security group on the endpoint network interface to restrict access to the instances.

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  Explanation

  https://aws.amazon.com/blogs/machine-learning/securing-all-amazon-sagemaker-api-calls-with-aws-privatelink/

• Question 136:

  A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews. A data scientist building the ML models must identify additional sources of data to increase the size of the dataset.

  Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)

  A. Emails exchanged by customers and the company's customer service agents
  B. Social media posts containing the name of the company or its products
  C. A publicly available collection of news articles
  D. A publicly available collection of customer reviews
  E. Product sales revenue figures for the company
  F. Instruction manuals for the company's products
  A. Emails exchanged by customers and the company's customer service agents
  B. Social media posts containing the name of the company or its products
  D. A publicly available collection of customer reviews

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  Explanation

  Email exchange between customer and customer service would be valuable data source.

• Question 137:

  A developer at a retail company is creating a daily demand forecasting model. The company stores the historical hourly demand data in an Amazon S3 bucket. However, the historical data does not include demand data for some hours.

  The developer wants to verify that an autoregressive integrated moving average (ARIMA) approach will be a suitable model for the use case.

  How should the developer verify the suitability of an ARIMA approach?

  A. Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Impute hourly missing data. Perform a Seasonal Trend decomposition.
  B. Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Choose ARIMA as the machine learning (ML) problem. Check the model performance.
  C. Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Resample data by using the aggregate daily total. Perform a Seasonal Trend decomposition.
  D. Use Amazon SageMaker Autopilot. Create a new experiment that specifies the S3 data location. Impute missing hourly values. Choose ARIMA as the machine learning (ML) problem. Check the model performance.
  C. Use Amazon SageMaker Data Wrangler. Import the data from Amazon S3. Resample data by using the aggregate daily total. Perform a Seasonal Trend decomposition.

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  Explanation

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  Daily aggregation is needed to forecast daily demand and also takes care of missing hourly values.

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  Seasonal Trend decomposition on the daily aggregated data helps in understanding the underlying patterns, trends, and seasonality, which is essential for determining whether an ARIMA model would be appropriate.

• Question 138:

  A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model.

  What should the Specialist do to address the performance issues with the current solution?

  A. Use the SageMaker batch transform feature
  B. Compress the training data into Apache Parquet format.
  C. Ensure that the input mode for the training job is set to Pipe.
  D. Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.
  C. Ensure that the input mode for the training job is set to Pipe.

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  Explanation

• Question 139:

  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
  B. Choose a lower number of layers
  D. Enable dropout
  F. Enable early stopping

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  Explanation

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

  To solve this issue, we will present five techniques prevent overfitting while training neural networks.

  1.Simplifying The Model (reduce number of layers)

  2.Early Stopping

  3.Use Data Augmentation

  4.Use Regularization (L1 + L2)

  5.Use Dropouts

• Question 140:

  A sports analytics company is providing services at a marathon. Each runner in the marathon will have their race ID printed as text on the front of their shirt. The company needs to extract race IDs from images of the runners. Which solution will meet these requirements with the LEAST operational overhead?

  A. Use Amazon Rekognition.
  B. Use a custom convolutional neural network (CNN).
  C. Use the Amazon SageMaker Object Detection algorithm.
  D. Use Amazon Lookout for Vision.
  A. Use Amazon Rekognition.

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  Explanation

  https://docs.aws.amazon.com/rekognition/latest/dg/text-detection.html?pg=lnandsec=ft