Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 131:

  A company has hired a data scientist to create a loan risk model. The dataset contains loan amounts and variables such as loan type, region, and other demographic variables. The data scientist wants to use Amazon SageMaker to test bias regarding the loan amount distribution with respect to some of these categorical variables.

  Which pretraining bias metrics should the data scientist use to check the bias distribution? (Choose three.)

  A. Class imbalance

  B. Conditional demographic disparity

  C. Difference in proportions of labels

  D. Jensen-Shannon divergence

  E. Kullback-Leibler divergence

  F. Total variation distance

  Correct Answer: DEF

  https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-measure-data-bias.html

• Question 132:

  A retail company wants to use Amazon Forecast to predict daily stock levels of inventory. The cost of running out of items in stock is much higher for the company than the cost of having excess inventory. The company has millions of data

  samples for multiple years for thousands of items. The company's purchasing department needs to predict demand for 30-day cycles for each item to ensure that restocking occurs.

  A machine learning (ML) specialist wants to use item-related features such as "category," "brand," and "safety stock count." The ML specialist also wants to use a binary time series feature that has "promotion applied?" as its name. Future

  promotion information is available only for the next 5 days.

  The ML specialist must choose an algorithm and an evaluation metric for a solution to produce prediction results that will maximize company profit. Which solution will meet these requirements?

  A. Train a model by using the Autoregressive Integrated Moving Average (ARIMA) algorithm. Evaluate the model by using the Weighted Quantile Loss (wQL) metric at 0.75 (P75).

  B. Train a model by using the Autoregressive Integrated Moving Average (ARIMA) algorithm. Evaluate the model by using the Weighted Absolute Percentage Error (WAPE) metric.

  C. Train a model by using the Convolutional Neural Network - Quantile Regression (CNN-QR) algorithm. Evaluate the model by using the Weighted Quantile Loss (wQL) metric at 0.75 (P75).

  D. Train a model by using the Convolutional Neural Network - Quantile Regression (CNN-QR) algorithm. Evaluate the model by using the Weighted Absolute Percentage Error (WAPE) metric.

  Correct Answer: C

  https://docs.aws.amazon.com/forecast/latest/dg/aws-forecast-algo-cnnqr.html https://docs.aws.amazon.com/forecast/latest/dg/metrics.html#metrics-wQL

• Question 133:

  A manufacturing company wants to monitor its devices for anomalous behavior. A data scientist has trained an Amazon SageMaker scikit-learn model that classifies a device as normal or anomalous based on its 4-day telemetry. The 4-day telemetry of each device is collected in a separate file and is placed in an Amazon S3 bucket once every hour. The total time to run the model across the telemetry for all devices is 5 minutes.

  What is the MOST cost-effective solution for the company to use to run the model across the telemetry for all the devices?

  A. SageMaker Batch Transform

  B. SageMaker Asynchronous Inference

  C. SageMaker Processing

  D. A SageMaker multi-container endpoint

  Correct Answer: A

  https://docs.aws.amazon.com/sagemaker/latest/dg/inference-cost-optimization.html

  "Use batch inference for workloads for which you need inference for a large set of data for processes that happen offline (that is, you don't need a persistent endpoint). You pay for the instance for the duration of the batch inference job". As you pay for the batch job duration, cost should not be an issue with Batch transform.

  "Use asynchronous inference for asynchronous workloads that process up to 1 GB of data (such as text corpus, image, video, and audio) that are latency insensitive and cost sensitive. With asynchronous inference, you can control costs by specifying a fixed number of instances for the optimal processing rate instead of provisioning for the peak. You can also scale down to zero to save additional costs."

• Question 134:

  A company wants to segment a large group of customers into subgroups based on shared characteristics. The company's data scientist is planning to use the Amazon SageMaker built-in k-means clustering algorithm for this task. The data scientist needs to determine the optimal number of subgroups (k) to use.

  Which data visualization approach will MOST accurately determine the optimal value of k?

  A. Calculate the principal component analysis (PCA) components. Run the k-means clustering algorithm for a range of k by using only the first two PCA components. For each value of k, create a scatter plot with a different color for each cluster. The optimal value of k is the value where the clusters start to look reasonably separated.

  B. Calculate the principal component analysis (PCA) components. Create a line plot of the number of components against the explained variance. The optimal value of k is the number of PCA components after which the curve starts decreasing in a linear fashion.

  C. Create a t-distributed stochastic neighbor embedding (t-SNE) plot for a range of perplexity values. The optimal value of k is the value of perplexity, where the clusters start to look reasonably separated.

  D. Run the k-means clustering algorithm for a range of k. For each value of k, calculate the sum of squared errors (SSE). Plot a line chart of the SSE for each value of k. The optimal value of k is the point after which the curve starts decreasing in a linear fashion.

  Correct Answer: D

• Question 135:

  A data scientist at a financial services company used Amazon SageMaker to train and deploy a model that predicts loan defaults. The model analyzes new loan applications and predicts the risk of loan default. To train the model, the data scientist manually extracted loan data from a database. The data scientist performed the model training and deployment steps in a Jupyter notebook that is hosted on SageMaker Studio notebooks. The model's prediction accuracy is decreasing over time.

  Which combination of steps is the MOST operationally efficient way for the data scientist to maintain the model's accuracy? (Choose two.)

  A. Use SageMaker Pipelines to create an automated workflow that extracts fresh data, trains the model, and deploys a new version of the model.

  B. Configure SageMaker Model Monitor with an accuracy threshold to check for model drift. Initiate an Amazon CloudWatch alarm when the threshold is exceeded. Connect the workflow in SageMaker Pipelines with the CloudWatch alarm to automatically initiate retraining.

  C. Store the model predictions in Amazon S3. Create a daily SageMaker Processing job that reads the predictions from Amazon S3, checks for changes in model prediction accuracy, and sends an email notification if a significant change is detected.

  D. Rerun the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model.

  E. Export the training and deployment code from the SageMaker Studio notebooks into a Python script. Package the script into an Amazon Elastic Container Service (Amazon ECS) task that an AWS Lambda function can initiate.

  Correct Answer: AB

• Question 136:

  A retail company wants to create a system that can predict sales based on the price of an item. A machine learning (ML) engineer built an initial linear model that resulted in the following residual plot:

  

  Which actions should the ML engineer take to improve the accuracy of the predictions in the next phase of model building? (Choose three.)

  A. Downsample the data uniformly to reduce the amount of data.

  B. Create two different models for different sections of the data.

  C. Downsample the data in sections where Price < 50.

  D. Offset the input data by a constant value where Price > 50.

  E. Examine the input data, and apply non-linear data transformations where appropriate.

  F. Use a non-linear model instead of a linear model.

  Correct Answer: BEF

• Question 137:

  A data scientist at a food production company wants to use an Amazon SageMaker built-in model to classify different vegetables. The current dataset has many features. The company wants to save on memory costs when the data scientist trains and deploys the model. The company also wants to be able to find similar data points for each test data point.

  Which algorithm will meet these requirements?

  A. K-nearest neighbors (k-NN) with dimension reduction

  B. Linear learner with early stopping

  C. K-means

  D. Principal component analysis (PCA) with the algorithm mode set to random

  Correct Answer: A

• Question 138:

  A data scientist is training a large PyTorch model by using Amazon SageMaker. It takes 10 hours on average to train the model on GPU instances. The data scientist suspects that training is not converging and that resource utilization is not optimal.

  What should the data scientist do to identify and address training issues with the LEAST development effort?

  A. Use CPU utilization metrics that are captured in Amazon CloudWatch. Configure a CloudWatch alarm to stop the training job early if low CPU utilization occurs.

  B. Use high-resolution custom metrics that are captured in Amazon CloudWatch. Configure an AWS Lambda function to analyze the metrics and to stop the training job early if issues are detected.

  C. Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect issues and to launch the StopTrainingJob action if issues are detected.

  D. Use the SageMaker Debugger confusion and feature_importance_overweight built-in rules to detect issues and to launch the StopTrainingJob action if issues are detected.

  Correct Answer: C

• Question 139:

  A sports broadcasting company is planning to introduce subtitles in multiple languages for a live broadcast. The commentary is in English. The company needs the transcriptions to appear on screen in French or Spanish, depending on the broadcasting country. The transcriptions must be able to capture domain-specific terminology, names, and locations based on the commentary context. The company needs a solution that can support options to provide tuning data.

  Which combination of AWS services and features will meet these requirements with the LEAST operational overhead? (Choose two.)

  A. Amazon Transcribe with custom vocabularies

  B. Amazon Transcribe with custom language models

  C. Amazon SageMaker Seq2Seq

  D. Amazon SageMaker with Hugging Face Speech2Text

  E. Amazon Translate

  Correct Answer: BE

• Question 140:

  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.

  Correct Answer: B