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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q180-Q185):

NEW QUESTION # 180
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable What should be done to reduce the impact of having such a large number of features?

  • A. Perform one-hot encoding on highly correlated features
  • B. Use matrix multiplication on highly correlated features.
  • C. Create a new feature space using principal component analysis (PCA)
  • D. Apply the Pearson correlation coefficient

Answer: A


NEW QUESTION # 181
The Chief Editor for a product catalog wants the Research and Development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand The team has a set of training data Which machine learning algorithm should the researchers use that BEST meets their requirements?

  • A. Recurrent neural network (RNN)
  • B. Latent Dirichlet Allocation (LDA)
  • C. K-means
  • D. Convolutional neural network (CNN)

Answer: C


NEW QUESTION # 182
A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues.
The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset.
Which feature engineering technique should the Data Scientist use to meet the objectives?

  • A. Run self-correlation on all features and remove highly correlated features
  • B. Use an autoencoder or principal component analysis (PCA) to replace original features with new features
  • C. Normalize all numerical values to be between 0 and 1
  • D. Cluster raw data using k-means and use sample data from each cluster to build a new dataset

Answer: B

Explanation:
The best feature engineering technique to speed up the model training time without losing a lot of information from the original dataset is to use an autoencoder or principal component analysis (PCA) to replace original features with new features. An autoencoder is a type of neural network that learns a compressed representation of the input data, called the latent space, by minimizing the reconstruction error between the input and the output. PCA is a statistical technique that reduces the dimensionality of the data by finding a set of orthogonal axes, called the principal components, that capture the maximum variance of the data. Both techniques can help reduce the number of features and remove the noise and redundancy in the data, which can improve the model performance and speed up the training process. References:
* AWS Machine Learning Specialty Exam Guide
* AWS Machine Learning Training - Dimensionality Reduction for Machine Learning
* AWS Machine Learning Training - Deep Learning with Amazon SageMaker


NEW QUESTION # 183
This graph shows the training and validation loss against the epochs for a neural network The network being trained is as follows
* Two dense layers one output neuron
* 100 neurons in each layer
* 100 epochs
* Random initialization of weights

Which technique can be used to improve model performance in terms of accuracy in the validation set?

  • A. Adding another layer with the 100 neurons
  • B. Increasing the number of epochs
  • C. Random initialization of weights with appropriate seed
  • D. Early stopping

Answer: A


NEW QUESTION # 184
A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long.
Which action will resolve the problem?

  • A. Choose a different weight initialization type.
  • B. Add more nodes to the recurrent neural network (RNN) than the largest sentence's word count.
  • C. Adjust hyperparameters related to the attention mechanism.
  • D. Change preprocessing to use n-grams.

Answer: C

Explanation:
The data scientist should adjust hyperparameters related to the attention mechanism to resolve the problem.
The attention mechanism is a technique that allows the decoder to focus on different parts of the input sequence when generating the output sequence. It helps the model cope with long input sequences and improve the translation quality. The Amazon SageMaker seq2seq algorithm supports different types of attention mechanisms, such as dot, general, concat, and mlp. The data scientist can use the hyperparameter attention_type to choose the type of attention mechanism. The data scientist can also use the hyperparameter attention_coverage_type to enable coverage, which is a mechanism that penalizes the model for attending to the same input positions repeatedly. By adjusting these hyperparameters, the data scientist can fine-tune the attention mechanism and reduce the number of false negative predictions by the model.
References:
* Sequence-to-Sequence Algorithm - Amazon SageMaker
* Attention Mechanism - Sockeye Documentation


NEW QUESTION # 185
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