Amazon MLS-C01 Online Practice Questions and Exam Preparation

Amazon MLS-C01 Online Questions & Answers

• Question 1:

  An Machine Learning Specialist discover the following statistics while experimenting on a model.

  

  What can the Specialist from the experiments?

  A. The model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1
  B. The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1
  C. The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model
  D. The model in Experiment 1 had a high random noise error that was reduced in Expenment 3 by regularization Expenment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model
  C. The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model

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  Explanation

• Question 2:

  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.
  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.

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  Explanation

• Question 3:

  A company wants to use automatic speech recognition (ASR) to transcribe messages that are less than 60 seconds long from a voicemail-style application. The company requires the correct identification of 200 unique product names, some of which have unique spellings or pronunciations.

  The company has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts it can use to customize the chosen ASR model. The company needs to ensure that everyone can update their customizations multiple times each hour.

  Which approach will maximize transcription accuracy during the development phase?

  A. Use a voice-driven Amazon Lex bot to perform the ASR customization. Create customer slots within the bot that specifically identify each of the required product names. Use the Amazon Lex synonym mechanism to provide additional variations of each product name as mis-transcriptions are identified in development.
  B. Use Amazon Transcribe to perform the ASR customization. Analyze the word confidence scores in the transcript, and automatically create or update a custom vocabulary file with any word that has a confidence score below an acceptable threshold value. Use this updated custom vocabulary file in all future transcription tasks.
  C. Create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customization. Analyze the transcripts and manually update the custom vocabulary file to include updated or additional entries for those names that are not being correctly identified.
  D. Use the audio transcripts to create a training dataset and build an Amazon Transcribe custom language model. Analyze the transcripts and update the training dataset with a manually corrected version of transcripts where product names are not being transcribed correctly. Create an updated custom language model.
  C. Create a custom vocabulary file containing each product name with phonetic pronunciations, and use it with Amazon Transcribe to perform the ASR customization. Analyze the transcripts and manually update the custom vocabulary file to include updated or additional entries for those names that are not being correctly identified.

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  Explanation

  https://aws.amazon.com/blogs/machine-learning/build-a-custom-vocabulary-to-enhance-speech-to-text-transcription-accuracy-with-amazon-transcribe/

• Question 4:

  An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.

  What combination of services is the MOST efficient to accomplish the task?

  A. Amazon Transcribe, Amazon Translate, and Amazon Comprehend
  B. Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq
  C. Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)
  D. Amazon Transcribe, Amazon Translate, and Amazon SageMaker BlazingText
  A. Amazon Transcribe, Amazon Translate, and Amazon Comprehend

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  Explanation

• Question 5:

  A company operates large cranes at a busy port. The company plans to use machine learning (ML) for predictive maintenance of the cranes to avoid unexpected breakdowns and to improve productivity.

  The company already uses sensor data from each crane to monitor the health of the cranes in real time. The sensor data includes rotation speed, tension, energy consumption, vibration, pressure, and ...perature for each crane. The company contracts AWS ML experts to implement an ML solution.

  Which potential findings would indicate that an ML-based solution is suitable for this scenario? (Select TWO.)

  A. The historical sensor data does not include a significant number of data points and attributes for certain time periods.
  B. The historical sensor data shows that simple rule-based thresholds can predict crane failures.
  C. The historical sensor data contains failure data for only one type of crane model that is in operation and lacks failure data of most other types of crane that are in operation.
  D. The historical sensor data from the cranes are available with high granularity for the last 3 years.
  E. The historical sensor data contains most common types of crane failures that the company wants to predict.
  D. The historical sensor data from the cranes are available with high granularity for the last 3 years.
  E. The historical sensor data contains most common types of crane failures that the company wants to predict.

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  Explanation

  The best indicators that an ML-based solution is suitable for this scenario are D and E, because they imply that the historical sensor data is sufficient and relevant for building a predictive maintenance model. This model can use machine learning techniques such as regression, classification, or anomaly detection to learn from the past data and forecast future failures or issues12. Having high granularity and diversity of data can improve the accuracy and generalization of the model, as well as enable the detection of complex patterns and relationships that are not captured by simple rule-based thresholds3. The other options are not good indicators that an ML-based solution is suitable, because they suggest that the historical sensor data is incomplete, inconsistent, or inadequate for building a predictive maintenance model. These options would require additional data collection, preprocessing, or augmentation to overcome the data quality issues and ensure that the model can handle different scenarios and types of cranes4 .

  References:

  1: Machine Learning Techniques for Predictive Maintenance

  2: A Guide to Predictive Maintenance and Machine Learning

  3: Machine Learning for Predictive Maintenance: Reinventing Asset Upkeep

  4: Predictive Maintenance with Machine Learning: A Complete Guide : [Machine Learning for Predictive Maintenance - AWS Online Tech Talks]

• Question 6:

  A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The Specialist is asked to score a model that was run against the dataset The Specialist has been advised that identifying valid transactions is equally as important as identifying fraudulent transactions What metric is BEST suited to score the model?

  A. Precision
  B. Recall
  C. Area Under the ROC Curve (AUC)
  D. Root Mean Square Error (RMSE)
  A. Precision

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  Explanation

• Question 7:

  A machine learning (ML) engineer is creating a binary classification model. The ML engineer will use the model in a highly sensitive environment.

  There is no cost associated with missing a positive label. However, the cost of making a false positive inference is extremely high.

  What is the most important metric to optimize the model for in this scenario?

  A. Accuracy
  B. Precision
  C. Recall
  D. F1
  B. Precision

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  Explanation

• Question 8:

  A company sells thousands of products on a public website and wants to automatically identify products with potential durability problems. The company has 1.000 reviews with date, star rating, review text, review summary, and customer email fields, but many reviews are incomplete and have empty fields. Each review has already been labeled with the correct durability result.

  A machine learning specialist must train a model to identify reviews expressing concerns over product durability. The first model needs to be trained and ready to review in 2 days.

  What is the MOST direct approach to solve this problem within 2 days?

  A. Train a custom classifier by using Amazon Comprehend.
  B. Build a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet.
  C. Train a built-in BlazingText model using Word2Vec mode in Amazon SageMaker.
  D. Use a built-in seq2seq model in Amazon SageMaker.
  A. Train a custom classifier by using Amazon Comprehend.

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  Explanation

• Question 9:

  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.
  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.

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  Explanation

• Question 10:

  A company is building a predictive maintenance model based on machine learning (ML). The data is stored in a fully private Amazon S3 bucket that is encrypted at rest with AWS Key Management Service (AWS KMS) CMKs. An ML specialist must run data preprocessing by using an Amazon SageMaker Processing job that is triggered from code in an Amazon SageMaker notebook. The job should read data from Amazon S3, process it, and upload it back to the same S3 bucket. The preprocessing code is stored in a container image in Amazon Elastic Container Registry (Amazon ECR). The ML specialist needs to grant permissions to ensure a smooth data preprocessing workflow.

  Which set of actions should the ML specialist take to meet these requirements?

  A. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs, S3 read and write access to the relevant S3 bucket, and appropriate KMS and ECR permissions. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job from the notebook.
  B. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions.
  C. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs and to access Amazon ECR. Attach the role to the SageMaker notebook instance. Set up both an S3 endpoint and a KMS endpoint in the default VPC. Create Amazon SageMaker Processing jobs from the notebook.
  D. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Set up an S3 endpoint in the default VPC. Create Amazon SageMaker Processing jobs with the access key and secret key of the IAM user with appropriate KMS and ECR permissions.
  A. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs, S3 read and write access to the relevant S3 bucket, and appropriate KMS and ECR permissions. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job from the notebook.

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  Explanation