INTEREACTIVE MLS-C01 TESTING ENGINE - LATEST MLS-C01 EXAM FORUM

Intereactive MLS-C01 Testing Engine - Latest MLS-C01 Exam Forum

Intereactive MLS-C01 Testing Engine - Latest MLS-C01 Exam Forum

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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) exam is a certification program designed for professionals who want to demonstrate their expertise in the field of machine learning. MLS-C01 Exam is intended to validate the knowledge and skills of candidates in building, training, and deploying machine learning models on the Amazon Web Services (AWS) platform.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q173-Q178):

NEW QUESTION # 173
A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year's worth of credit card transactions data. The model needs to identify the fraudulent transactions (positives) from the regular ones (negatives). The company's goal is to accurately capture as many positives as possible.
Which metrics should the data scientist use to optimize the model? (Choose two.)

  • A. Area under the precision-recall curve
  • B. False positive rate
  • C. True positive rate
  • D. Specificity
  • E. Accuracy

Answer: A,C

Explanation:
Explanation
The data scientist should use the area under the precision-recall curve and the true positive rate to optimize the model. These metrics are suitable for imbalanced classification problems, such as credit card fraud detection, where the positive class (fraudulent transactions) is much rarer than the negative class (non-fraudulent transactions).
The area under the precision-recall curve (AUPRC) is a measure of how well the model can identify the positive class among all the predicted positives. Precision is the fraction of predicted positives that are actually positive, and recall is the fraction of actual positives that are correctly predicted. A higher AUPRC means that the model can achieve a higher precision with a higher recall, which is desirable for fraud detection.
The true positive rate (TPR) is another name for recall. It is also known as sensitivity or hit rate. It measures the proportion of actual positives that are correctly identified by the model. A higher TPR means that the model can capture more positives, which is the company's goal.
References:
Metrics for Imbalanced Classification in Python - Machine Learning Mastery Precision-Recall - scikit-learn


NEW QUESTION # 174
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? (Select TWO.)

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

Answer: D,E

Explanation:
Explanation
The best options to use an Inception neural network architecture instead of a ResNet architecture for image classification in Amazon SageMaker are:
Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training. This option allows users to customize the training environment and use any TensorFlow model they want. Users can create a Docker image that contains the TensorFlow Estimator API and the Inception model from the TensorFlow Hub, and push it to Amazon ECR. Then, users can use the SageMaker Estimator class to train the model using the custom Docker image and the training data from Amazon S3.
Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network and use this for model training. This option allows users to use the built-in TensorFlow container provided by SageMaker and write custom code to load and train the Inception model. Users can use the TensorFlow Estimator class to specify the custom code and the training data from Amazon S3. The custom code can use the TensorFlow Hub module to load the Inception model and fine-tune it on the training data.
The other options are not feasible for this scenario because:
Customize the built-in image classification algorithm to use Inception and use this for model training.
This option is not possible because the built-in image classification algorithm in SageMaker does not support customizing the neural network architecture. The built-in algorithm only supports ResNet models with different depths and widths.
Create a support case with the SageMaker team to change the default image classification algorithm to Inception. This option is not realistic because the SageMaker team does not provide such a service.
Users cannot request the SageMaker team to change the default algorithm or add new algorithms to the built-in ones.
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. This option is not advisable because it does not leverage the benefits of SageMaker, such as managed training and deployment, distributed training, and automatic model tuning. Users would have to manually install and configure the Inception network code and the TensorFlow framework on the EC2 instance, and run the training and inference code on the same instance, which may not be optimal for performance and scalability.
References:
Use Your Own Algorithms or Models with Amazon SageMaker
Use the SageMaker TensorFlow Serving Container
TensorFlow Hub


NEW QUESTION # 175
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset Which tool should be used to improve the validation accuracy?

  • A. Amazon SageMaker BlazingText allow mode
  • B. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers
  • C. Natural Language Toolkit (NLTK) stemming and stop word removal
  • D. Amazon Comprehend syntax analysts and entity detection

Answer: B

Explanation:
Explanation
Term frequency-inverse document frequency (TF-IDF) is a technique that assigns a weight to each word in a document based on how important it is to the meaning of the document. The term frequency (TF) measures how often a word appears in a document, while the inverse document frequency (IDF) measures how rare a word is across a collection of documents. The TF-IDF weight is the product of the TF and IDF values, and it is high for words that are frequent in a specific document but rare in the overall corpus. TF-IDF can help improve the validation accuracy of a sentiment analysis model by reducing the impact of common words that have little or no sentiment value, such as "the", "a", "and", etc. Scikit-learn is a popular Python library for machine learning that provides a TF-IDF vectorizer class that can transform a collection of text documents into a matrix of TF-IDF features. By using this tool, the Data Scientist can create a more informative and discriminative feature representation for the sentiment analysis task.
References:
TfidfVectorizer - scikit-learn
Text feature extraction - scikit-learn
TF-IDF for Beginners | by Jana Schmidt | Towards Data Science
Sentiment Analysis: Concept, Analysis and Applications | by Susan Li | Towards Data Science


NEW QUESTION # 176
For the given confusion matrix, what is the recall and precision of the model?

  • A. Recall = 0.8 Precision = 0.92
  • B. Recall = 0.92 Precision = 0.84
  • C. Recall = 0.84 Precision = 0.8
  • D. Recall = 0.92 Precision = 0.8

Answer: B


NEW QUESTION # 177
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%, respectively.
How should the Specialist address this issue and what is the reason behind it?

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

Answer: C

Explanation:
https://kharshit.github.io/blog/2018/05/04/dropout-prevent-overfitting


NEW QUESTION # 178
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