UPDATED AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY TEST GUIDE BY PREP4KING

Updated AWS-Certified-Machine-Learning-Specialty Test Guide by Prep4King

Updated AWS-Certified-Machine-Learning-Specialty Test Guide by Prep4King

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The AWS Certified Machine Learning - Specialty Exam covers a wide range of topics related to machine learning, including data preparation and feature engineering, model selection and evaluation, training and tuning models, and deploying and managing machine learning models in production environments. AWS-Certified-Machine-Learning-Specialty exam also focuses on AWS-specific machine learning services, such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

The AWS Certified Machine Learning - Specialty Exam covers a wide range of topics, including data preparation, feature engineering, model selection and evaluation, deep learning, and deployment. It is designed to test an individual's ability to design, implement, deploy, and maintain machine learning solutions using AWS services. AWS-Certified-Machine-Learning-Specialty Exam also covers various AWS services such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend, which are essential tools for machine learning on AWS. By passing AWS-Certified-Machine-Learning-Specialty exam, individuals can demonstrate their ability to design and implement effective machine learning solutions on the AWS platform, which can help them advance their careers in the field.

>> AWS-Certified-Machine-Learning-Specialty Test Guide <<

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q49-Q54):

NEW QUESTION # 49
A term frequency-inverse document frequency (tf-idf) matrix using both unigrams and bigrams is built from a text corpus consisting of the following two sentences:
1. Please call the number below.
2. Please do not call us.
What are the dimensions of the tf-idf matrix?

  • A. (2, 16)
  • B. (8, 10)
  • C. (2, 8)
  • D. (2, 10)

Answer: A

Explanation:
There are 2 sentences, 8 unique unigrams, and 8 unique bigrams, so the result would be (2,16).
The phrases are "Please call the number below" and "Please do not call us." Each word individually (unigram) is "Please," "call," "the," "number," "below," "do," "not," and "us." The unique bigrams are "Please call," "call the," "the number," "number below," "Please do," "do not," "not call," and "call us."


NEW QUESTION # 50
A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on 400 patients randomly selected from the population. The disease is seen in 3% of the population.
Which cross-validation strategy should the Data Scientist adopt?

  • A. A stratified k-fold cross-validation strategy with k=5
  • B. A k-fold cross-validation strategy with k=5 and 3 repeats
  • C. A k-fold cross-validation strategy with k=5
  • D. An 80/20 stratified split between training and validation

Answer: A


NEW QUESTION # 51
A company needs to develop a model that uses a machine learning (ML) model for risk analysis. An ML engineer needs to evaluate the contribution each feature of a training dataset makes to the prediction of the target variable before the ML engineer selects features.
How should the ML engineer predict the contribution of each feature?

  • A. Use an Amazon SageMaker Data Wrangler data flow to create and modify a data preparation pipeline.Manually add the feature scores.
  • B. Use an Amazon SageMaker Data Wrangler quick model visualization to find feature importance scores that are between 0.5 and 1.
  • C. Use the Amazon SageMaker Data Wrangler bias report to identify potential biases in the data related to feature engineering.
  • D. Use the Amazon SageMaker Data Wrangler multicollinearity measurement features and the principal component analysis (PCA) algorithm to calculate the variance of the dataset along multiple directions in the feature space.

Answer: B

Explanation:
To evaluate the contribution of each feature before model training, feature importance scores are a primary tool. In Amazon SageMaker Data Wrangler, a Quick Model can be used to rapidly generate a model on the dataset and return feature importance scores. These scores indicate how much each feature influences the target variable.
"Use Quick Model to generate feature importance scores quickly to help understand which features contribute the most to predictions. These scores range from 0 to 1, with higher values indicating higher contribution." This allows ML engineers to assess feature relevance without full-scale model development, thus saving time and effort in the feature selection phase of the pipeline.


NEW QUESTION # 52
A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset.
The company requires at least 85% accuracy to make use of the model.
After an exhaustive grid search, the optimal hyperparameters produced the following:
68% accuracy on the training set
67% accuracy on the validation set
What can the machine learning specialist do to improve the system's accuracy?

  • A. Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.
  • B. Train a new model using the current neural network architecture.
  • C. Add more data to the training set and retrain the model using transfer learning to reduce the bias.
  • D. Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.

Answer: C

Explanation:
The problem described in the question is a case of underfitting, where the neural network model performs poorly on both the training and validation sets. This means that the model has not learned the features of the data well enough and has high bias. To solve this issue, the machine learning specialist should consider the following change:
Add more data to the training set and retrain the model using transfer learning to reduce the bias: Adding more data to the training set can help the model learn more patterns and variations in the data and improve its performance. Transfer learning can also help the model leverage the knowledge from the pre-trained network and adapt it to the new data. This can reduce the bias and increase the accuracy of the model.
References:
Transfer learning for TensorFlow image classification models in Amazon SageMaker Transfer learning for custom labels using a TensorFlow container and "bring your own algorithm" in Amazon SageMaker Machine Learning Concepts - AWS Training and Certification


NEW QUESTION # 53
A retail company uses a machine learning (ML) model for daily sales forecasting. The company's brand manager reports that the model has provided inaccurate results for the past 3 weeks.
At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company's ML team is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model's inaccuracies.
What should the ML team do on the SageMaker Studio notebook to visualize the model's degradation MOST accurately?

  • A. Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition, create a scatter plot of daily sales versus model error from before that period.
  • B. Create a line chart with the weekly mean absolute error (MAE) of the model.
  • C. Create a histogram of the daily sales over the last 3 weeks. In addition, create a histogram of the daily sales from before that period.
  • D. Create a histogram of the model errors over the last 3 weeks. In addition, create a histogram of the model errors from before that period.

Answer: D

Explanation:
Explanation
The best way to visualize the model's degradation is to create a histogram of the model errors over the last 3 weeks and compare it with a histogram of the model errors from before that period. A histogram is a graphical representation of the distribution of numerical data. It shows how often each value or range of values occurs in the data. A model error is the difference between the actual value and the predicted value. A high model error indicates a poor fit of the model to the data. By comparing the histograms of the model errors, the ML team can see if there is a significant change in the shape, spread, or center of the distribution. This can indicate if the model is underfitting, overfitting, or drifting from the data. A line chart or a scatter plot would not be as effective as a histogram for this purpose, because they do not show the distribution of the errors. A line chart would only show the trend of the errors over time, which may not capture the variability or outliers. A scatter plot would only show the relationship between the errors and another variable, such as daily sales, which may not be relevant or informative for the model's performance. References:
Histogram - Wikipedia
Model error - Wikipedia
SageMaker Model Monitor - visualizing monitoring results


NEW QUESTION # 54
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