RELIABLE MLS-C01 EXAM PAPERS | MLS-C01 PASS GUARANTEE

Reliable MLS-C01 Exam Papers | MLS-C01 Pass Guarantee

Reliable MLS-C01 Exam Papers | MLS-C01 Pass Guarantee

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q219-Q224):

NEW QUESTION # 219
An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.
What combination of services is the MOST efficient to accomplish the task?

  • A. Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq
  • B. Amazon Transcribe, Amazon Translate, and Amazon Comprehend
  • C. Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)
  • D. Amazon Transcribe, Amazon Translate, and Amazon SageMaker BlazingText

Answer: B

Explanation:
Amazon Transcribe, Amazon Translate, and Amazon Comprehend are the most efficient combination of services to accomplish the task of sentiment analysis on a video clip with audio in Spanish. Amazon Transcribe is a service that can convert speech to text using deep learning. Amazon Transcribe can transcribe audio from various sources, such as video files, audio files, or streaming audio. Amazon Transcribe can also recognize multiple speakers, different languages, accents, dialects, and custom vocabularies. In this case, Amazon Transcribe can transcribe the audio from the video clip in Spanish to text in Spanish1 Amazon Translate is a service that can translate text from one language to another using neural machine translation. Amazon Translate can translate text from various sources, such as documents, web pages, chat messages, etc. Amazon Translate can also support multiple languages, domains, and styles. In this case, Amazon Translate can translate the text from Spanish to English2 Amazon Comprehend is a service that can analyze and derive insights from text using natural language processing. Amazon Comprehend can perform various tasks, such as sentiment analysis, entity recognition, key phrase extraction, topic modeling, etc. Amazon Comprehend can also support multiple languages and domains. In this case, Amazon Comprehend can perform sentiment analysis on the text in English and determine whether the feedback is positive, negative, neutral, or mixed3 The other options are not valid or efficient for accomplishing the task of sentiment analysis on a video clip with audio in Spanish. Amazon Comprehend, Amazon SageMaker seq2seq, and Amazon SageMaker Neural Topic Model (NTM) are not a good combination, as they do not include a service that can transcribe speech to text, which is a necessary step for processing the audio from the video clip. Amazon Comprehend, Amazon Translate, and Amazon SageMaker BlazingText are not a good combination, as they do not include a service that can perform sentiment analysis, which is the main goal of the task. Amazon SageMaker BlazingText is a service that can train and deploy text classification and word embedding models using deep learning. Amazon SageMaker BlazingText can perform tasks such as text classification, named entity recognition, part-of-speech tagging, etc., but not sentiment analysis4


NEW QUESTION # 220
An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.
What combination of services is the MOST efficient to accomplish the task?

  • A. Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq
  • B. Amazon Transcribe, Amazon Translate, and Amazon Comprehend
  • C. Amazon Transcribe, Amazon Translate and Amazon SageMaker BlazingText
  • D. Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)

Answer: D


NEW QUESTION # 221
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. True positive rate
  • B. False positive rate
  • C. Specificity
  • D. Area under the precision-recall curve
  • E. Accuracy

Answer: A,D

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 # 222
A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images.
The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.
What changes should the Specialist consider to solve this issue? (Choose three.)

  • A. Choose a smaller learning rate
  • B. Choose a higher number of layers
  • C. Choose a lower number of layers
  • D. Enable dropout
  • E. Include all the images from the test set in the training set
  • F. Enable early stopping

Answer: B,D,E


NEW QUESTION # 223
A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test"?

  • A. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.
  • B. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the data as it is generated by Amazon SageMaker
  • C. Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker
  • D. Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon OuickSight to visualize logs as they are being produced

Answer: B


NEW QUESTION # 224
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