Amazon MLS-C01 Online Practice Questions and Exam Preparation

Amazon MLS-C01 Online Questions & Answers

• Question 281:

  A Machine Learning Specialist wants to bring a custom algorithm to Amazon SageMaker. The Specialist implements the algorithm in a Docker container supported by Amazon SageMaker. How should the Specialist package the Docker container so that Amazon SageMaker can launch the training correctly?

  A. Modify the bash_profile file in the container and add a bash command to start the training program
  B. Use CMD config in the Dockerfile to add the training program as a CMD of the image
  C. Configure the training program as an ENTRYPOINT named train
  D. Copy the training program to directory /opt/ml/train
  C. Configure the training program as an ENTRYPOINT named train

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  Explanation

  "To configure a Docker container to run as an executable, use an ENTRYPOINT instruction in a Dockerfile. SageMaker overrides any default CMD statement in a container by specifying the train argument after the image name"

  https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo-dockerfile.html

• Question 282:

  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 hyperparameters 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.
  C. Use Amazon SageMaker automatic model tuning (AMT). Specify a range of values for each hyperparameter.

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  Explanation

• Question 283:

  A company plans to build a custom natural language processing (NLP) model to classify and prioritize user feedback. The company hosts the data and all machine learning (ML) infrastructure in the AWS Cloud. The ML team works from the company's office, which has an IPsec VPN connection to one VPC in the AWS Cloud.

  The company has set both the enableDnsHostnames attribute and the enableDnsSupport attribute of the VPC to true. The company's DNS resolvers point to the VPC DNS. The company does not allow the ML team to access Amazon SageMaker notebooks through connections that use the public internet. The connection must stay within a private network and within the AWS internal network.

  Which solution will meet these requirements with the LEAST development effort?

  A. Create a VPC interface endpoint for the SageMaker notebook in the VPC. Access the notebook through a VPN connection and the VPC endpoint.
  B. Create a bastion host by using Amazon EC2 in a public subnet within the VPC. Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.
  C. Create a bastion host by using Amazon EC2 in a private subnet within the VPC with a NAT gateway. Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.
  D. Create a NAT gateway in the VPC. Access the SageMaker notebook HTTPS endpoint through a VPN connection and the NAT gateway.
  B. Create a bastion host by using Amazon EC2 in a public subnet within the VPC. Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.

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  Explanation

• Question 284:

  A data scientist is using an Amazon SageMaker notebook instance and needs to securely access data stored in a specific Amazon S3 bucket.

  How should the data scientist accomplish this?

  A. Add an S3 bucket policy allowing GetObject, PutObject, and ListBucket permissions to the Amazon SageMaker notebook ARN as principal.
  B. Encrypt the objects in the S3 bucket with a custom AWS Key Management Service (AWS KMS) key that only the notebook owner has access to.
  C. Attach the policy to the IAM role associated with the notebook that allows GetObject, PutObject, and ListBucket operations to the specific S3 bucket.
  D. Use a script in a lifecycle configuration to configure the AWS CLI on the instance with an access key ID and secret.
  C. Attach the policy to the IAM role associated with the notebook that allows GetObject, PutObject, and ListBucket operations to the specific S3 bucket.

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  Explanation

• Question 285:

  A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1..10]:

  

  Considering the graph, what is a reasonable selection for the optimal choice of k?

  A. 1
  B. 4
  C. 7
  D. 10
  B. 4

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  Explanation

• Question 286:

  A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.

  Which architecture changes would ensure that provisioned resources are being utilized effectively?

  A. Redeploy the model as a batch transform job on an M5 instance.
  B. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.
  C. Redeploy the model on a P3dn instance.
  D. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.
  B. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.

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  Explanation

  https://aws.amazon.com/machine-learning/elastic-inference/

• Question 287:

  A manufacturing company asks its machine learning specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100,000 images per defect type for training. During the initial training of the image classification model, the specialist notices that the validation accuracy is 80%, while the training accuracy is 90%. It is known that human-level performance for this type of image classification is around 90%.

  What should the specialist consider to fix this issue?

  A. A longer training time
  B. Making the network larger
  C. Using a different optimizer
  D. Using some form of regularization
  D. Using some form of regularization

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  Explanation

  https://acloud.guru/forums/aws-certified-machine-learning-specialty/discussion/-MGdBUKmQ02zC3uOq4VL/AWS%20Exam%20Machine%20Learning

• Question 288:

  A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable

  What should be done to reduce the impact of having such a large number of features?

  A. Perform one-hot encoding on highly correlated features
  B. Use matrix multiplication on highly correlated features.
  C. Create a new feature space using principal component analysis (PCA)
  D. Apply the Pearson correlation coefficient
  C. Create a new feature space using principal component analysis (PCA)

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  Explanation

• Question 289:

  Example Corp has an annual sale event from October to December. The company has sequential sales data from the past 15 years and wants to use Amazon ML to predict the sales for this year's upcoming event. Which method should Example Corp use to split the data into a training dataset and evaluation dataset?

  A. Pre-split the data before uploading to Amazon S3
  B. Have Amazon ML split the data randomly.
  C. Have Amazon ML split the data sequentially.
  D. Perform custom cross-validation on the data
  C. Have Amazon ML split the data sequentially.

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  Explanation

• Question 290:

  A company is planning a marketing campaign to promote a new product to existing customers. The company has data for past promotions that are similar. The company decides to try an experiment to send a more expensive marketing

  package to a smaller number of customers. The company wants to target the marketing campaign to customers who are most likely to buy the new product. The experiment requires that at least 90% of the customers who are likely to

  purchase the new product receive the marketing materials.

  The company trains a model by using the linear learner algorithm in Amazon SageMaker. The model has a recall score of 80% and a precision of 75%.

  How should the company retrain the model to meet these requirements?

  A. Set the target_recall hyperparameter to 90%. Set the binary_classifier_model_selection_criteria hyperparameter to recall_at_target_precision.
  B. Set the target_precision hyperparameter to 90%. Set the binary_classifier_model_selection_criteria hyperparameter to precision_at_target_recall.
  C. Use 90% of the historical data for training. Set the number of epochs to 20.
  D. Set the normalize_label hyperparameter to true. Set the number of classes to 2.
  B. Set the target_precision hyperparameter to 90%. Set the binary_classifier_model_selection_criteria hyperparameter to precision_at_target_recall.

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  Explanation