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Amazon AWS Certified Machine Learning - Specialty Exam (page: 5)
Amazon AWS Certified Machine Learning - Specialty (MLS-C01)
Updated on: 09-Feb-2026

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AWS Certified Machine Learning - Specialty PDF 370 Questions

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? (Choose two.)

  1. Add more deep trees to the random forest to enable the model to learn more features.
  2. Include a copy of the samples in the test dataset in the training dataset.
  3. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
  4. Change the cost function so that false negatives have a higher impact on the cost value than false positives.
  5. Change the cost function so that false positives have a higher impact on the cost value than false negatives.

Answer(s): C,D



A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.

Initial models have performed poorly.
While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population

How should the Data Scientist correct this issue?

  1. Drop all records from the dataset where age has been set to 0.
  2. Replace the age field value for records with a value of 0 with the mean or median value from the dataset
  3. Drop the age feature from the dataset and train the model using the rest of the features.
  4. Use k-means clustering to handle missing features

Answer(s): D

Explanation:

Dropping the Age feature is a NOT ATOLL a good idea - as age plays a critical role in this disease as per the question
Dropping 10% of data is NOT a good idea considering the fact that the number of observations is already low.
The Mean or Median are a potential solutions
But the question says that "Disease worsens after age 65 so there is a correlation between age and other symptoms related feature" So that means that using Unsupervised Learning we can make pretty good prediction of "Age"


Reference:

https://medium.com/jungle-book/missing-data-filling-with-unsupervised-learning-b448964030d



A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day, the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.

Which storage scheme is MOST adapted to this scenario?

  1. Store datasets as files in Amazon S3.
  2. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
  3. Store datasets as tables in a multi-node Amazon Redshift cluster.
  4. Store datasets as global tables in Amazon DynamoDB.

Answer(s): A



A Machine Learning Specialist deployed a model that provides product recommendations on a company's website. Initially, the model was performing very well and resulted in customers buying more products on average. However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less. The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago.

Which method should the Specialist try to improve model performance?

  1. The model needs to be completely re-engineered because it is unable to handle product inventory changes.
  2. The model's hyperparameters should be periodically updated to prevent drift.
  3. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
  4. The model should be periodically retrained using the original training data plus new data as product inventory changes.

Answer(s): D



A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:

•Real-time analytics
•Interactive analytics of historical data
•Clickstream analytics
•Product recommendations

Which services should the Specialist use?

  1. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real- time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
  2. Amazon Athena as the data catalog: Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-real-time data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
  3. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
  4. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations

Answer(s): A



A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.

Which of the following will accomplish this? (Choose two.)

  1. Customize the built-in image classification algorithm to use Inception and use this for model training.
  2. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.
  3. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.
  4. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network, and use this for model training.
  5. Download and apt-get install the inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.

Answer(s): C,D



A Machine Learning Specialist built an image classification deep learning model. However, the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%, respectively.

How should the Specialist address this issue and what is the reason behind it?

  1. The learning rate should be increased because the optimization process was trapped at a local minimum.
  2. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
  3. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
  4. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

Answer(s): B

Explanation:

Overfitting occurs when a model is too complex and memorizes the training data instead of learning the underlying pattern. As a result, the model performs well on the training data but poorly on new, unseen data.

Increasing the dropout rate, a regularization technique, can help combat overfitting by randomly dropping out some neurons during training, which prevents the model from relying too heavily on any single feature.



A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.

What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?

  1. Implement an AWS Lambda function to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
  2. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
  3. Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
  4. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting

Answer(s): B



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Share your comments for Amazon AWS Certified Machine Learning - Specialty exam with other users:

Reddy 12/14/2023 2:42:00 AM

these are pretty useful
Anonymous