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Download AWS Certified Machine Learning - Specialty Exam Dumps
NEW QUESTION 31
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. Use Amazon Lex to preprocess the text files for pronunciation
- B. Create an appropriate pronunciation lexicon.
- C. Convert current documents to SSML with pronunciation tags.
- D. Output speech marks to guide in pronunciation.
Answer: C
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/polly/latest/dg/ssml.html
NEW QUESTION 32
A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?
- A. Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
- B. Add event type and event value fields to the interactions dataset in Amazon Personalize.
- C. Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
- D. Use the event tracker in Amazon Personalize to include real-time user interactions.
Answer: B
NEW QUESTION 33
A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist needs to reduce the number of false negatives.
Which combination of steps should the Data Scientist take to reduce the number of false negative predictions by the model? (Choose two.)
- A. Change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC).
- B. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
- C. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
- D. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
- E. Change the XGBoost eval_metric parameter to optimize based on Root Mean Square Error (RMSE).
Answer: A,B
NEW QUESTION 34
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