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Understanding functional and technical aspects of AWS Certified Machine Learning Specialty Exam Data Engineering

The following will be dicussed here:

• Identify and implement a data-transformation solution
• Identify and implement a data-ingestion solution
• Create data repositories for machine learning

Prerequisites

The potential candidates should have 12-24 months of experience in architecting, developing, or running machine learning or deep learning workloads particularly on AWS Cloud. They must also possess the ability to effectively express the intuition behind basic machine learning algorithms. It is also advisable to have some experience with machine learning and deep learning frameworks, as well as be able to follow operational & deployment best practices. >> Study MLS-C01 Reference <<

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q88-Q93):

NEW QUESTION # 88
A large consumer goods manufacturer has the following products on sale
* 34 different toothpaste variants
* 48 different toothbrush variants
* 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched Which solution should a Machine Learning Specialist apply?

• A. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product
• B. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
• C. Train a custom XGBoost model to forecast demand for the new product
• D. Train a custom ARIMA model to forecast demand for the new product.

Answer: A Explanation:
Explanation
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.
NEW QUESTION # 89
An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data Which reconstruction approach should the Specialist use to preserve the integrity of the dataset1?

• A. Mean substitution
• B. Listwise deletion
• C. Multiple imputation
• D. Last observation carried forward

Answer: B
NEW QUESTION # 90
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?

• A. 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.
• B. 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.
• C. Implement an AWS Lambda function to long 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.
• D. 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: D
NEW QUESTION # 91
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information which model would have the HIGHEST accuracy?

• A. Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))
• B. Logistic regression
• C. Support vector machine (SVM) with non-linear kernel
• D. Single perceptron with tanh activation function

Answer: C
NEW QUESTION # 92
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. Change the cost function so that false negatives have a higher impact on the cost value than false positives
• B. Add more deep trees to the random forest to enable the model to learn more features.
• C. Change the cost function so that false positives have a higher impact on the cost value than false negatives
• D. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
• E. indicate a copy of the samples in the test database in the training dataset

Answer: A,D
NEW QUESTION # 93
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