Amazon MLS-C01 Online Practice Questions and Exam Preparation

Amazon MLS-C01 Online Questions & Answers

• Question 1:

  A machine learning (ML) specialist is running an Amazon SageMaker hyperparameter optimization job for a model that is based on the XGBoost algorithm. The ML specialist selects Root Mean Square Error (RMSE) as the objective

  evaluation metric.

  The ML specialist discovers that the model is overfitting and cannot generalize well on the validation data. The ML specialist decides to resolve the model overfitting by using SageMaker automatic model tuning (AMT).

  Which solution will meet this requirement?

  A. Configure SageMaker AMT to use a static range of hyperparameter values.
  B. Configure SageMaker AMT to increase the number of parallel training jobs.
  C. Configure SageMaker AMT to stop training jobs early.
  D. Configure SageMaker AMT to run the training jobs with a warm start.
  C. Configure SageMaker AMT to stop training jobs early.

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  explanation:

  Explanation

• Question 2:

  A beauty supply store wants to understand some characteristics of visitors to the store. The store has security video recordings from the past several years. The store wants to generate a report of hourly visitors from the recordings. The report should group visitors by hair style and hair color.

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

  A. Use an object detection algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
  B. Use an object detection algorithm to identify a visitor's hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair color.
  C. Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
  D. Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair.
  C. Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.

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  explanation:

  Explanation

• Question 3:

  A manufacturing company stores production volume data in a PostgreSQL database.

  The company needs an end-to-end solution that will give business analysts the ability to prepare data for processing and to predict future production volume based the previous year's production volume. The solution must not require the company to have coding knowledge.

  Which solution will meet these requirements with the LEAST effort?

  A. Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Create an Amazon EMR duster to read the S3 bucket and perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.
  B. Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.
  C. Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Use AWS Glue to read the data in the S3 bucket and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.
  D. Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.
  B. Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.

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  explanation:

  Explanation

• Question 4:

  A company distributes an online multiple-choice survey to several thousand people. Respondents to the survey can select multiple options for each question.

  A machine learning (ML) engineer needs to comprehensively represent every response from all respondents in a dataset. The ML engineer will use the dataset to train a logistic regression model.

  Which solution will meet these requirements?

  A. Perform one-hot encoding on every possible option for each question of the survey.
  B. Perform binning on all the answers each respondent selected for each question.
  C. Use Amazon Mechanical Turk to create categorical labels for each set of possible responses.
  D. Use Amazon Textract to create numeric features for each set of possible responses.
  A. Perform one-hot encoding on every possible option for each question of the survey.

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  explanation:

  Explanation

• Question 5:

  A machine learning (ML) specialist is developing a model for a company. The model will classify and predict sequences of objects that are displayed in a video. The ML specialist decides to use a hybrid architecture that consists of a convolutional neural network (CNN) followed by a classifier three-layer recurrent neural network (RNN).

  The company developed a similar model previously but trained the model to classify a different set of objects. The ML specialist wants to save time by using the previously trained model and adapting the model for the current use case and set of objects.

  Which combination of steps will accomplish this goal with the LEAST amount of effort? (Choose two.)

  A. Reinitialize the weights of the entire CNN. Retrain the CNN on the classification task by using the new set of objects.
  B. Reinitialize the weights of the entire network. Retrain the entire network on the prediction task by using the new set of objects.
  C. Reinitialize the weights of the entire RNN. Retrain the entire model on the prediction task by using the new set of objects.
  D. Reinitialize the weights of the last fully connected layer of the CNN. Retrain the CNN on the classification task by using the new set of objects.
  E. Reinitialize the weights of the last layer of the RNN. Retrain the entire model on the prediction task by using the new set of objects.
  D. Reinitialize the weights of the last fully connected layer of the CNN. Retrain the CNN on the classification task by using the new set of objects.
  E. Reinitialize the weights of the last layer of the RNN. Retrain the entire model on the prediction task by using the new set of objects.

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  explanation:

  Explanation

• Question 6:

  A data scientist is building a new model for an ecommerce company. The model will predict how many minutes it will take to deliver a package.

  During model training, the data scientist needs to evaluate model performance.

  Which metrics should the data scientist use to meet this requirement? (Choose two.)

  A. InferenceLatency
  B. Mean squared error (MSE)
  C. Root mean squared error (RMSE)
  D. Precision
  E. Accuracy
  B. Mean squared error (MSE)
  C. Root mean squared error (RMSE)

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  explanation:

  Explanation

• Question 7:

  A data scientist is conducting exploratory data analysis (EDA) on a dataset that contains information about product suppliers. The dataset records the country where each product supplier is located as a two-letter text code. For example, the

  code for New Zealand is "NZ."

  The data scientist needs to transform the country codes for model training. The data scientist must choose the solution that will result in the smallest increase in dimensionality. The solution must not result in any information loss.

  Which solution will meet these requirements?

  A. Add a new column of data that includes the full country name.
  B. Encode the country codes into numeric variables by using similarity encoding.
  C. Map the country codes to continent names.
  D. Encode the country codes into numeric variables by using one-hot encoding.
  D. Encode the country codes into numeric variables by using one-hot encoding.

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  explanation:

  Explanation

• Question 8:

  A data scientist uses Amazon SageMaker Data Wrangler to obtain a feature summary from a dataset that the data scientist imported from Amazon S3. The data scientist notices that the prediction power for a dataset feature has a score of 1. What is the cause of the score?

  A. Target leakage occurred in the imported dataset.
  B. The data scientist did not fine-tune the training and validation split.
  C. The SageMaker Data Wrangler algorithm that the data scientist used did not find an optimal model fit for each feature to calculate the prediction power.
  D. The data scientist did not process the features enough to accurately calculate prediction power.
  A. Target leakage occurred in the imported dataset.

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  explanation:

  Explanation

• Question 9:

  A company's machine learning (ML) team needs to build a system that can detect whether people in a collection of images are wearing the company's logo. The company has a set of labeled training data. Which algorithm should the ML team use to meet this requirement?

  A. Principal component analysis (PCA)
  B. Recurrent neural network (RNN)
  C. -nearest neighbors (k-NN)
  D. Convolutional neural network (CNN)
  D. Convolutional neural network (CNN)

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  explanation:

  Explanation

• Question 10:

  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:

  Explanation