Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 321:

  A gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million users

  The training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and 999.000 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user

  age, device, location, and play patterns

  Using this dataset for training, the Data Science team trained a random forest model that converged with over 99% accuracy on the training set However, the prediction results on a test dataset were not satisfactory.

  Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

  A. Add more deep trees to the random forest to enable the model to learn more features.

  B. indicate a copy of the samples in the test database in the training dataset

  C. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.

  D. Change the cost function so that false negatives have a higher impact on the cost value than false positives

  E. Change the cost function so that false positives have a higher impact on the cost value than false negatives

  Correct Answer: CD

• Question 322:

  A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.

  The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:

  

  Based on the model evaluation results, why is this a viable model for production?

  A. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.

  B. The precision of the model is 86%, which is less than the accuracy of the model.

  C. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.

  D. The precision of the model is 86%, which is greater than the accuracy of the model.

  Correct Answer: C

• Question 323:

  IT leadership wants Jo transition a company's existing machine learning data storage environment to AWS as a temporary ad hoc solution The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learning

  The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts The solution must also support the storage of csv and JSON files, and be able to query over semi-structured data The following are high priorities for the company:

  1.

  Solution simplicity

  2.

  Fast development time

  3.

  Low cost

  4.

  High flexibility

  What technologies meet the company's requirements?

  A. Amazon S3 and Amazon Athena

  B. Amazon Redshift and AWS Glue

  C. Amazon DynamoDB and DynamoDB Accelerator (DAX)

  D. Amazon RDS and Amazon ES

  Correct Answer: B

• Question 324:

  A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the Specialist do to integrate the Spark application with SageMaker? (Select THREE )

  A. Download the AWS SDK for the Spark environment

  B. Install the SageMaker Spark library in the Spark environment.

  C. Use the appropriate estimator from the SageMaker Spark Library to train a model.

  D. Compress the training data into a ZIP file and upload it to a pre-defined Amazon S3 bucket.

  E. Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker

  F. Convert the DataFrame object to a CSV file, and use the CSV file as input for obtaining inferences from SageMaker.

  Correct Answer: DEF

• Question 325:

  The displayed graph is from a forecasting model for testing a time series.

  

  Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

  A. The model predicts both the trend and the seasonality well.

  B. The model predicts the trend well, but not the seasonality.

  C. The model predicts the seasonality well, but not the trend.

  D. The model does not predict the trend or the seasonality well.

  Correct Answer: A

• Question 326:

  A Machine Learning Specialist is training a model to identify the make and model of vehicles in images The Specialist wants to use transfer learning and an existing model trained on images of general objects The Specialist collated a large custom dataset of pictures containing different vehicle makes and models

  A. Initialize the model with random weights in all layers including the last fully connected layer

  B. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

  C. Initialize the model with random weights in all layers and replace the last fully connected layer

  D. Initialize the model with pre-trained weights in all layers including the last fully connected layer

  Correct Answer: B

  In transfer learning, a pre-trained model is used as a starting point to train a new model on a different task, typically using a smaller dataset. The pre-trained model contains weights that have been learned from a large amount of data on a related task, and these weights can be leveraged to train the new model more efficiently.

  To re-train the model with the custom data, the Specialist should initialize the model with pre-trained weights in all layers, as these weights can provide a good starting point for the new task. The Specialist should then replace the last fully connected layer, which is responsible for making the final predictions, as this layer will likely need to be modified to reflect the new task. By keeping the pre-trained weights in the other layers, the Specialist can take advantage of the knowledge learned from the previous task, and potentially speed up the training process.

• Question 327:

  A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.

  Which prior probability distribution should the ML Specialist use for this variable?

  A. Poisson distribution

  B. Uniform distribution

  C. Normal distribution

  D. Binomial distribution

  Correct Answer: A

  https://www.investopedia.com/terms/d/discrete-distribution.asp The Poisson distribution is commonly used for count data, which is the case here as we are interested in the number of minutes New Yorkers wait for a bus. The Poisson distribution is characterized by a single parameter, lambda, which represents the mean and variance of the distribution. In this case, the mean is 3 minutes, so we would set lambda to 3. The Poisson distribution assumes that events occur independently of each other, which is a reasonable assumption in this case since the waiting time for each individual is likely to be independent of the waiting time for others.

• Question 328:

  Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

  

  A. The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20.85%

  B. The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 13.12%

  C. The true class frequency for Romance is 0.78% and the predicted class frequency for Adventure is (0.47-0.32)

  D. The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20.85%*0.32

  Correct Answer: B

  https://vceguide.com/what-is-the-true-class-frequency-for-romance-and-the-predicted-class-frequency-for-adventure/

• Question 329:

  A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined The model needs lo be retrained daily

  Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

  A. Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3 then use AWS Glue to do the transformation

  B. Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3

  C. Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.

  D. Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehouse stream that transforms raw record attributes into simple transformed values using SQL.

  Correct Answer: D

  https://docs.aws.amazon.com/kinesisanalytics/latest/java/examples-s3.html

• Question 330:

  A Machine Learning Specialist is building a supervised model that will evaluate customers' satisfaction with their mobile phone service based on recent usage The model's output should infer whether or not a customer is likely to switch to a competitor in the next 30 days.

  Which of the following modeling techniques should the Specialist use1?

  A. Time-series prediction

  B. Anomaly detection

  C. Binary classification

  D. Regression

  Correct Answer: C