AWS Certified Machine Learning – Specialty Dump 02

An insurance company is developing a new device for vehicles that uses a camera to observe drivers’ behavior and alert them when they appear distracted. The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models.
During the model evaluation, the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images.
Which of the following should be used to resolve this issue? (Choose two.)

  • A. Add vanishing gradient to the model.
  • B. Perform data augmentation on the training data.
  • C. Make the neural network architecture complex.
  • D. Use gradient checking in the model.
  • E. Add L2 regularization to the model.

A = wrong, nonsense. C = wrong, will not solve overfitting. D = wrong, check for errors in network implementation, will not solve overfitting.

General rule: see overfitting, go for regularization.

When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

  • A. The training channel identifying the location of training data on an Amazon S3 bucket.
  • B. The validation channel identifying the location of validation data on an Amazon S3 bucket.
  • C. The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
  • D. Hyperparameters in a JSON array as documented for the algorithm used.
  • E. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
  • F. The output path specifying where on an Amazon S3 bucket the trained model will persist.

A and B = wrong, interestingly, input data is not compulsory for a training job, which means the docker may have other means to acquire the data, also data location could be on EFS so only stating S3 is not correct. D = wrong, not compulsory.

Whether the algorithm is built-in or user-defined, does not matter, all goes down to the following API call.

A monitoring service generates 1 TB of scale metrics record data every minute. A Research team performs queries on this data using Amazon Athena. The queries run slowly due to the large volume of data, and the team requires better performance.
How should the records be stored in Amazon S3 to improve query performance?

  • A. CSV files
  • B. Parquet files
  • C. Compressed JSON
  • D. RecordlO

If you need explanation on this question you need to see a doctor.

Machine Learning Specialist is working with a media company to perform classification on popular articles from the company’s website. The company is using random forests to classify how popular an article will be before it is published. A sample of the data being used is below.

Given the dataset, the Specialist wants to convert the Day_Of_Week column to binary values.
What technique should be used to convert this column to binary values?

A. Binarization
B. One-hot encoding
C. Tokenization
D. Normalization transformation

Note: one-hot encoding is very much like enumeration.

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.)

  • A. Add more deep trees to the random forest to enable the model to learn more features.
  • B. Include a copy of the samples in the test dataset 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.

A = wrong, will not help. B = wrong, nonsense, don’t do that. E = need to turn down negatives in general.

This is a problem of imbalanced data sheet (very little portion of positives). So increase the number of positives and take better care of false negatives by increasing its impact, thus increasing its error.

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?

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

A = wrong, dropping 10% data of a small data set is not okay. B = wrong, could work, but will introduce strong bias, ignoring the fact patients all have disease known to worsen with age. C = wrong, age is an important feature which should not be dropped.

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?

  • A. Store datasets as files in Amazon S3.
  • B. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
  • C. Store datasets as tables in a multi-node Amazon Redshift cluster.
  • D. Store datasets as global tables in Amazon DynamoDB.

B, C and D = not cost-effective for large amount of training data.

Athena can be used to query S3 data with SQL.

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?

  • A. The model needs to be completely re-engineered because it is unable to handle product inventory changes.
  • B. The model’s hyperparameters should be periodically updated to prevent drift.
  • C. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
  • D. The model should be periodically retrained using the original training data plus new data as product inventory changes.

A and B = wrong, ineffectiveness is not related to the model itself. C = wrong, add new training data, not regularization.

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?

  • A. 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
  • B. 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
  • C. 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
  • D. 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

B = wrong, Athena is not a data catalog. C = wrong, Data Streams is for real-time. D = wrong, Athena is not a data catalog, Glue cannot generate recommendations.

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.)

  • A. Customize the built-in image classification algorithm to use Inception and use this for model training.
  • B. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.
  • C. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.
  • D. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network, and use this for model training.
  • E. 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.

A = wrong, you can’t change built-in image classification algorithm. B = wrong, nonsense. E = wrong, nonsense, SageMaker instances are managed.

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