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[General] Pass Your Amazon MLS-C01: AWS Certified Machine Learning - Specialty Exam with C

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【General】 Pass Your Amazon MLS-C01: AWS Certified Machine Learning - Specialty Exam with C

Posted at 5 day before      View：45 | Replies：1        Print      Only Author   [Copy Link] 1#
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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is a certification exam that tests individuals on their knowledge and skills related to machine learning on the Amazon Web Services (AWS) platform. MLS-C01 exam is designed for individuals who wish to become certified professionals in the field of machine learning and want to showcase their expertise in designing, implementing, deploying, and maintaining machine learning solutions on AWS.
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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q113-Q118):NEW QUESTION # 113
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. Download word embedding's pre-trained on a large corpus.
  • B. Create one-hot word encoding vectors.
  • C. Create word embedding factors that store edit distance with every other word.
  • D. Produce a set of synonyms for every word using Amazon Mechanical Turk.
Answer: D

NEW QUESTION # 114
A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the team requires better performance How should the records be stored in Amazon S3 to improve query performance?
  • A. CSV files
  • B. Compressed JSON
  • C. Parquet files
  • D. RecordIO
Answer: C

NEW QUESTION # 115
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 the built-in Amazon SageMaker k-means algorithm to cluster the data into two groups named
    "enrolled" or "not enrolled."
  • B. Use a classification algorithm to run predictions
  • C. Use Amazon SageMaker Ground Truth to sort the data into two groups named "enrolled" or "not enrolled."
  • D. Use a regression algorithm to run predictions.
  • E. Use a forecasting algorithm to run predictions.
Answer: B,C
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 # 116
For the given confusion matrix, what is the recall and precision of the model?

  • A. Recall = 0.92 Precision = 0.8
  • B. Recall = 0.92 Precision = 0.84
  • C. Recall = 0.84 Precision = 0.8
  • D. Recall = 0.8 Precision = 0.92
Answer: A
Explanation:
Explanation
Recall and precision are two metrics that can be used to evaluate the performance of a classification model.
Recall is the ratio of true positives to the total number of actual positives, which measures how well the model can identify all the relevant cases. Precision is the ratio of true positives to the total number of predicted positives, which measures how accurate the model is when it makes a positive prediction. Based on the confusion matrix in the image, we can calculate the recall and precision as follows:
Recall = TP / (TP + FN) = 12 / (12 + 1) = 0.92
Precision = TP / (TP + FP) = 12 / (12 + 3) = 0.8
Where TP is the number of true positives, FN is the number of false negatives, and FP is the number of false positives. Therefore, the recall and precision of the model are 0.92 and 0.8, respectively.

NEW QUESTION # 117
While working on a neural network project, a Machine Learning Specialist discovers thai some features in the data have very high magnitude resulting in this data being weighted more in the cost function What should the Specialist do to ensure better convergence during backpropagation?
  • A. Model regulanzation
  • B. Dimensionality reduction
  • C. Data normalization
  • D. Data augmentation for the minority class
Answer: D

NEW QUESTION # 118
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Posted at yesterday 18:17        Only Author  2#
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