Amazon MLS-C01 Online Practice Questions and Exam Preparation

Amazon MLS-C01 Online Questions & Answers

• Question 161:

  A company wants to conduct targeted marketing to sell solar panels to homeowners. The company wants to use machine learning (ML) technologies to identify which houses already have solar panels. The company has collected 8,000

  satellite images as training data and will use Amazon SageMaker Ground Truth to label the data.

  The company has a small internal team that is working on the project. The internal team has no ML expertise and no ML experience.

  Which solution will meet these requirements with the LEAST amount of effort from the internal team?

  A. Set up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Use Amazon Rekognition Custom Labels for model training and hosting.
  B. Set up a private workforce that consists of the internal team. Use the private workforce to label the data. Use Amazon Rekognition Custom Labels for model training and hosting.
  C. Set up a private workforce that consists of the internal team. Use the private workforce and the SageMaker Ground Truth active learning feature to label the data. Use the SageMaker Object Detection algorithm to train a model. Use SageMaker batch transform for inference.
  D. Set up a public workforce. Use the public workforce to label the data. Use the SageMaker Object Detection algorithm to train a model. Use SageMaker batch transform for inference.
  B. Set up a private workforce that consists of the internal team. Use the private workforce to label the data. Use Amazon Rekognition Custom Labels for model training and hosting.

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  Explanation

• Question 162:

  A machine learning (ML) specialist wants to bring a custom training algorithm to Amazon SageMaker. The ML specialist implements the algorithm in a Docker container that is supported by SageMaker. How should the ML specialist package the Docker container so that SageMaker can launch the training correctly?

  A. Specify the server argument in the ENTRYPOINT instruction in the Dockerfile.
  B. Specify the training program in the ENTRYPOINT instruction in the Dockerfile.
  C. Include the path to the training data in the docker build command when packaging the container.
  D. Use a COPY instruction in the Dockerfile to copy the training program to the /opt/ml/train directory.
  B. Specify the training program in the ENTRYPOINT instruction in the Dockerfile.

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  Explanation

  https://docs.aws.amazon.com/sagemaker/latest/dg/adapt-training-container.html In Step 2, it is mentioned to use this instruction on dockerfile: # Defines train.py as script entrypoint

  ENV SAGEMAKER_PROGRAM train.py

• Question 163:

  An automotive company is using computer vision in its autonomous cars. The company has trained its models successfully by using transfer learning from a convolutional neural network (CNN). The models are trained with PyTorch through the use of the Amazon SageMaker SDK. The company wants to reduce the time that is required for performing inferences, given the low latency that is required for self-driving.

  Which solution should the company use to evaluate and improve the performance of the models?

  A. Use Amazon CloudWatch algorithm metrics for visibility into the SageMaker training weights, gradients, biases, and activation outputs. Compute the filter ranks based on this information. Apply pruning to remove the low-ranking filters. Set the new weights. Run a new training job with the pruned model.
  B. Use SageMaker Debugger for visibility into the training weights, gradients, biases, and activation outputs. Adjust the model hyperparameters, and look for lower inference times. Run a new training job.
  C. Use SageMaker Debugger for visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on this information. Apply pruning to remove the low-ranking filters. Set the new weights. Run a new training job with the pruned model.
  D. Use SageMaker Model Monitor for visibility into the ModelLatency metric and OverheadLatency metric of the model after the model is deployed. Adjust the model hyperparameters, and look for lower inference times. Run a new training job.
  C. Use SageMaker Debugger for visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on this information. Apply pruning to remove the low-ranking filters. Set the new weights. Run a new training job with the pruned model.

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  Explanation

  https://aws.amazon.com/blogs/machine-learning/pruning-machine-learning-models-with-amazon-sagemaker-debugger-and-amazon-sagemaker-experiments/

• Question 164:

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

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  Explanation

• Question 165:

  A company is running an Amazon SageMaker training job that will access data stored in its Amazon S3 bucket A compliance policy requires that the data never be transmitted across the internet How should the company set up the job?

  A. Launch the notebook instances in a public subnet and access the data through the public S3 endpoint
  B. Launch the notebook instances in a private subnet and access the data through a NAT gateway
  C. Launch the notebook instances in a public subnet and access the data through a NAT gateway
  D. Launch the notebook instances in a private subnet and access the data through an S3 VPC endpoint.
  D. Launch the notebook instances in a private subnet and access the data through an S3 VPC endpoint.

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  Explanation

• Question 166:

  An obtain relator collects the following data on customer orders: demographics, behaviors, location, shipment progress, and delivery time. A data scientist joins all the collected datasets. The result is a single dataset that includes 980 variables.

  The data scientist must develop a machine learning (ML) model to identify groups of customers who are likely to respond to a marketing campaign.

  Which combination of algorithms should the data scientist use to meet this requirement? (Select TWO.)

  A. Latent Dirichlet Allocation (LDA)
  B. K-means
  C. Se mantic feg mentation
  D. Principal component analysis (PCA)
  E. Factorization machines (FM)
  B. K-means
  D. Principal component analysis (PCA)

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  Explanation

  The data scientist should use K-means and principal component analysis (PCA) to meet this requirement. K-means is a clustering algorithm that can group customers based on their similarity in the feature space. PCA is a dimensionality reduction technique that can transform the original 980 variables into a smaller set of uncorrelated variables that capture most of the variance in the data. This can help reduce the computational cost and noise in the data, and improve the performance of the clustering algorithm.

  References: Clustering - Amazon SageMaker Dimensionality Reduction - Amazon SageMaker

• Question 167:

  A media company wants to create a solution that identifies celebrities in pictures that users upload. The company also wants to identify the IP address and the timestamp details from the users so the company can prevent users from uploading pictures from unauthorized locations.

  Which solution will meet these requirements with LEAST development effort?

  A. Use AWS Panorama to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.
  B. Use AWS Panorama to identify celebrities in the pictures. Make calls to the AWS Panorama Device SDK to capture IP address and timestamp details.
  C. Use Amazon Rekognition to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.
  D. Use Amazon Rekognition to identify celebrities in the pictures. Use the text detection feature to capture IP address and timestamp details.
  C. Use Amazon Rekognition to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.

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  Explanation

• Question 168:

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

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  Explanation

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

• Question 169:

  A manufacturing company has a production line with sensors that collect hundreds of quality metrics. The company has stored sensor data and manual inspection results in a data lake for several months. To automate quality control, the

  machine learning team must build an automated mechanism that determines whether the produced goods are good quality, replacement market quality, or scrap quality based on the manual inspection results.

  Which modeling approach will deliver the MOST accurate prediction of product quality?

  A. Amazon SageMaker DeepAR forecasting algorithm
  B. Amazon SageMaker XGBoost algorithm
  C. Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm
  D. A convolutional neural network (CNN) and ResNet
  B. Amazon SageMaker XGBoost algorithm

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  Explanation

• Question 170:

  A data scientist at a retail company is forecasting sales for a product over the next 3 months. After preliminary analysis, the data scientist identifies that sales are seasonal and that holidays affect sales. The data scientist also determines that

  sales of the product are correlated with sales of other products in the same category.

  The data scientist needs to train a sales forecasting model that incorporates this information.

  Which solution will meet this requirement with the LEAST development effort?

  A. Use Amazon Forecast with Holidays featurization and the built-in autoregressive integrated moving average (ARIMA) algorithm to train the model.
  B. Use Amazon Forecast with Holidays featurization and the built-in DeepAR+ algorithm to train the model.
  C. Use Amazon SageMaker Processing to enrich the data with holiday information. Train the model by using the SageMaker DeepAR built-in algorithm.
  D. Use Amazon SageMaker Processing to enrich the data with holiday information. Train the model by using the Gluon Time Series (GluonTS) toolkit.
  B. Use Amazon Forecast with Holidays featurization and the built-in DeepAR+ algorithm to train the model.

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  Explanation