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AWS-Certified-Machine-Learning-Specialty Formal Test, AWS-Certified-Machine-Learning-Specialty Certification Cost

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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) certification exam is a valuable credential for professionals who want to specialize in machine learning. AWS-Certified-Machine-Learning-Specialty Exam Tests the candidate's ability to design, implement, and maintain machine learning solutions on the AWS platform. To prepare for the exam, candidates should have a solid understanding of machine learning algorithms, data modeling, and cloud computing concepts, and should take advantage of the training and certification resources available through AWS.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q248-Q253):

NEW QUESTION # 248
A company needs to deploy a chatbot to answer common questions from customers. The chatbot must base its answers on company documentation.
Which solution will meet these requirements with the LEAST development effort?

A. Train an Amazon SageMaker BlazingText model based on past customer questions and company documents. Deploy the model as a real-time SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.
B. Train a Bidirectional Attention Flow (BiDAF) network based on past customer questions and company documents. Deploy the model as a real-time Amazon SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.
C. Index company documents by using Amazon Kendra. Integrate the chatbot with Amazon Kendra by using the Amazon Kendra Query API operation to answer customer questions.
D. Index company documents by using Amazon OpenSearch Service. Integrate the chatbot with OpenSearch Service by using the OpenSearch Service k-nearest neighbors (k-NN) Query API operation to answer customer questions.

Answer: C

Explanation:
The solution A will meet the requirements with the least development effort because it uses Amazon Kendra, which is a highly accurate and easy to use intelligent search service powered by machine learning. Amazon Kendra can index company documents from various sources and formats, such as PDF, HTML, Word, and more. Amazon Kendra can also integrate with chatbots by using the Amazon Kendra Query API operation, which can understand natural language questions and provide relevant answers from the indexed documents. Amazon Kendra can also provide additional information, such as document excerpts, links, and FAQs, to enhance the chatbot experience1.
The other options are not suitable because:
Option B: Training a Bidirectional Attention Flow (BiDAF) network based on past customer questions and company documents, deploying the model as a real-time Amazon SageMaker endpoint, and integrating the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation will incur more development effort than using Amazon Kendra. The company will have to write the code for the BiDAF network, which is a complex deep learning model for question answering. The company will also have to manage the SageMaker endpoint, the model artifact, and the inference logic2.
Option C: Training an Amazon SageMaker BlazingText model based on past customer questions and company documents, deploying the model as a real-time SageMaker endpoint, and integrating the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation will incur more development effort than using Amazon Kendra. The company will have to write the code for the BlazingText model, which is a fast and scalable text classification and word embedding algorithm. The company will also have to manage the SageMaker endpoint, the model artifact, and the inference logic3.
Option D: Indexing company documents by using Amazon OpenSearch Service and integrating the chatbot with OpenSearch Service by using the OpenSearch Service k-nearest neighbors (k-NN) Query API operation will not meet the requirements effectively. Amazon OpenSearch Service is a fully managed service that provides fast and scalable search and analytics capabilities. However, it is not designed for natural language question answering, and it may not provide accurate or relevant answers for the chatbot. Moreover, the k-NN Query API operation is used to find the most similar documents or vectors based on a distance function, not to find the best answers based on a natural language query4.
References:
1: Amazon Kendra
2: Bidirectional Attention Flow for Machine Comprehension
3: Amazon SageMaker BlazingText
4: Amazon OpenSearch Service

NEW QUESTION # 249
A global financial company is using machine learning to automate its loan approval process. The company has a dataset of customer information. The dataset contains some categorical fields, such as customer location by city and housing status. The dataset also includes financial fields in different units, such as account balances in US dollars and monthly interest in US cents.
The company's data scientists are using a gradient boosting regression model to infer the credit score for each customer. The model has a training accuracy of 99% and a testing accuracy of 75%. The data scientists want to improve the model's testing accuracy.
Which process will improve the testing accuracy the MOST?

A. Use a label encoder for the categorical fields in the dataset. Perform L1 regularization on the financial fields in the dataset. Apply L2 regularization to the data.
B. Use a one-hot encoder for the categorical fields in the dataset. Perform standardization on the financial fields in the dataset. Apply L1 regularization to the data.
C. Use a logarithm transformation on the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Use imputation to populate missing values in the dataset.
D. Use tokenization of the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Remove the outliers in the data by using the z-score.

Answer: B

Explanation:
Explanation
The question is about improving the testing accuracy of a gradient boosting regression model. The testing accuracy is much lower than the training accuracy, which indicates that the model is overfitting the training data. To reduce overfitting, the following steps are recommended:
Use a one-hot encoder for the categorical fields in the dataset. This will create binary features for each category and avoid imposing an ordinal relationship among them. This can help the model learn the patterns better and generalize to unseen data.
Perform standardization on the financial fields in the dataset. This will scale the features to have zero mean and unit variance, which can improve the convergence and performance of the model. This can also help the model handle features with different units and ranges.
Apply L1 regularization to the data. This will add a penalty term to the loss function that is proportional to the absolute value of the coefficients. This can help the model reduce the complexity and select the most relevant features by shrinking the coefficients of less important features to zero.
References:
1: AWS Machine Learning Specialty Exam Guide
2: AWS Machine Learning Specialty Course
3: AWS Machine Learning Blog

NEW QUESTION # 250
A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements. However, company acronyms are being mispronounced in the current documents.
How should a Machine Learning Specialist address this issue for future documents?

A. Create an appropriate pronunciation lexicon.
B. Convert current documents to SSML with pronunciation tags.
C. Use Amazon Lex to preprocess the text files for pronunciation
D. Output speech marks to guide in pronunciation.

Answer: B

Explanation:
Explanation/Reference: https://docs.aws.amazon.com/polly/latest/dg/ssml.html

NEW QUESTION # 251
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. Apply the Pearson correlation coefficient
B. Use matrix multiplication on highly correlated features.
C. Perform one-hot encoding on highly correlated features
D. Create a new feature space using principal component analysis (PCA)

Answer: D

Explanation:
Explanation
Principal component analysis (PCA) is an unsupervised machine learning algorithm that attempts to reduce the dimensionality (number of features) within a dataset while still retaining as much information as possible. This is done by finding a new set of features called components, which are composites of the original features that are uncorrelated with one another. They are also constrained so that the first component accounts for the largest possible variability in the data, the second component the second most variability, and so on. By using PCA, the impact of having a large number of features that are highly correlated with each other can be reduced, as the new feature space will have fewer dimensions and less redundancy. This can make the linear models more stable and less prone to overfitting. References:
Principal Component Analysis (PCA) Algorithm - Amazon SageMaker
Perform a large-scale principal component analysis faster using Amazon SageMaker | AWS Machine Learning Blog Machine Learning- Prinicipal Component Analysis | i2tutorials

NEW QUESTION # 252
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%r respectively.
How should the Specialist address this issue and what is the reason behind it?

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

Answer: D

Explanation:
The best way to address the overfitting problem in image classification is to increase the dropout rate at the flatten layer because the model is not generalized enough. Dropout is a regularization technique that randomly drops out some units from the neural network during training, reducing the co-adaptation of features and preventing overfitting. The flatten layer is the layer that converts the output of the convolutional layers into a one-dimensional vector that can be fed into the dense layers. Increasing the dropout rate at the flatten layer means that more features from the convolutional layers will be ignored, forcing the model to learn more robust and generalizable representations from the remaining features.
The other options are not correct for this scenario because:
* Increasing the learning rate would not help with the overfitting problem, as it would make the optimization process more unstable and prone to overshooting the global minimum. A high learning rate can also cause the model to diverge or oscillate around the optimal solution, resulting in poor performance and accuracy.
* Increasing the dimensionality of the dense layer next to the flatten layer would not help with the overfitting problem, as it would make the model more complex and increase the number of parameters to be learned. A more complex model can fit the training data better, but it can also memorize the noise and irrelevant details in the data, leading to overfitting and poor generalization.
* Increasing the epoch number would not help with the overfitting problem, as it would make the model train longer and more likely to overfit the training data. A high epoch number can cause the model to converge to the global minimum, but it can also cause the model to over-optimize the training data and lose the ability to generalize to new data.
Dropout: A Simple Way to Prevent Neural Networks from Overfitting
How to Reduce Overfitting With Dropout Regularization in Keras
How to Control the Stability of Training Neural Networks With the Learning Rate How to Choose the Number of Hidden Layers and Nodes in a Feedforward Neural Network?
How to decide the optimal number of epochs to train a neural network?

NEW QUESTION # 253
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