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The AWS Certified Machine Learning - Specialty certification exam is a valuable credential for individuals who want to demonstrate their proficiency in designing, deploying, and managing machine learning solutions on the AWS platform. AWS Certified Machine Learning - Specialty certification demonstrates that you have the skills and knowledge necessary to work with AWS machine learning services and provides you with a competitive edge in the job market.

To pass the MLS-C01 exam, candidates need to demonstrate a deep understanding of the principles of machine learning, as well as practical experience in designing, implementing, and deploying machine learning solutions in the AWS environment. This includes a thorough understanding of AWS machine learning services and how to use them effectively to build and deploy machine learning models.

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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) Certification Exam is a professional certification that validates a candidate's expertise in designing, developing, and deploying machine learning models using Amazon Web Services (AWS). AWS Certified Machine Learning - Specialty certification is intended for individuals who have a strong understanding of machine learning and are looking to demonstrate their skills and knowledge in this field.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q274-Q279):

NEW QUESTION # 274
An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?

A. Create one-hot word encoding vectors.
B. Create word embedding factors that store edit distance with every other word.
C. Download word embedding's pre-trained on a large corpus.
D. Produce a set of synonyms for every word using Amazon Mechanical Turk.

Answer: C

Explanation:
Word embeddings are a type of dense representation of words, which encode semantic meaning in a vector form. These embeddings are typically pre-trained on a large corpus of text data, such as a large set of books, news articles, or web pages, and capture the context in which words are used. Word embeddings can be used as features for a nearest neighbor model, which can be used to find words used in similar contexts. Downloading pre-trained word embeddings is a good way to get started quickly and leverage the strengths of these representations, which have been optimized on a large amount of data. This is likely to result in more accurate and reliable features than other options like one-hot encoding, edit distance, or using Amazon Mechanical Turk to produce synonyms.

NEW QUESTION # 275
A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000
Test set images = 100 (constant test set)
The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

A. Increase the number of layers for the neural network.
B. Increase the training data by adding variation in rotation for training images.
C. Increase the number of epochs for model training
D. Increase the dropout rate for the second-to-last layer.

Answer: C

NEW QUESTION # 276
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

A. Mean absolute percentage error (MAPE)
B. Recall
C. Misclassification rate
D. Area Under the ROC Curve (AUC)

Answer: D

Explanation:
Explanation
Area Under the ROC Curve (AUC) is a metric that measures the performance of a binary classifier across all possible thresholds. It is also known as the probability that a randomly chosen positive example will be ranked higher than a randomly chosen negative example by the classifier. AUC is a good metric to compare different classification models because it is independent of the class distribution and the decision threshold. It also captures both the sensitivity (true positive rate) and the specificity (true negative rate) of the model.
References:
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Specialty Sample Questions

NEW QUESTION # 277
A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data scientist wants to use the data to build student profiles. The university will use the profiles to direct resources to recruit students who are likely to enroll in the university.
Which combination of steps should the data scientist take to predict whether a particular student applicant is likely to enroll in the university? (Select TWO)

A. Use a regression algorithm to run predictions.
B. Use a forecasting algorithm to run predictions.
C. Use the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named
"enrolled" or "not enrolled."
D. Use Amazon SageMaker Ground Truth to sort the data into two groups named "enrolled" or "not enrolled."
E. Use a classification algorithm to run predictions

Answer: D,E

Explanation:
Explanation
The data scientist should use Amazon SageMaker Ground Truth to sort the data into two groups named
"enrolled" or "not enrolled." This will create a labeled dataset that can be used for supervised learning. The data scientist should then use a classification algorithm to run predictions on the test data. A classification algorithm is a suitable choice for predicting a binary outcome, such as enrollment status, based on the input features, such as academic performance. A classification algorithm will output a probability for each class label and assign the most likely label to each observation.
References:
Use Amazon SageMaker Ground Truth to Label Data
Classification Algorithm in Machine Learning

NEW QUESTION # 278
For the given confusion matrix, what is the recall and precision of the model?

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

Answer: C

NEW QUESTION # 279
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AWS-Certified-Machine-Learning-Specialty Test Fee - High Pass-Rate Amazon Valid AWS-Certified-Machine-Learning-Specialty Test Online: AWS Certified Machine Learning - Specialty

Hot AWS-Certified-Machine-Learning-Specialty Test Fee | Valid Amazon Valid AWS-Certified-Machine-Learning-Specialty Test Online: AWS Certified Machine Learning - Specialty

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q274-Q279):

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