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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q261-Q266):
NEW QUESTION # 261
A manufacturing company has a large set of labeled historical sales data The manufacturer would like to predict how many units of a particular part should be produced each quarter Which machine learning approach should be used to solve this problem?
- A. Random Cut Forest (RCF)
- B. Linear regression
- C. Principal component analysis (PCA)
- D. Logistic regression
Answer: B
Explanation:
Explanation
Linear regression is a machine learning approach that can be used to solve this problem. Linear regression is a supervised learning technique that can model the relationship between one or more input variables (features) and an output variable (target). In this case, the input variables could be the historical sales data of the part, such as the quarter, the demand, the price, the inventory, etc. The output variable could be the number of units to be produced for the part. Linear regression can learn the coefficients (weights) of the input variables that best fit the output variable, and then use them to make predictions for new data. Linear regression is suitable for problems that involve continuous and numeric output variables, such as predicting house prices, stock prices, or sales volumes. References:
AWS Machine Learning Specialty Exam Guide
Linear Regression
NEW QUESTION # 262
A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?
- A. The precision of the model is 86%, which is greater than the accuracy of the model.
- B. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
- C. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
- D. The precision of the model is 86%, which is less than the accuracy of the model.
Answer: C
NEW QUESTION # 263
A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena The dataset contains more than 800.000 records stored as plaintext CSV files Each record contains 200 columns and is approximately 1 5 MB in size Most queries will span 5 to 10 columns only How should the Machine Learning Specialist transform the dataset to minimize query runtime?
- A. Convert the records to JSON format
- B. Convert the records to XML format
- C. Convert the records to GZIP CSV format
- D. Convert the records to Apache Parquet format
Answer: D
NEW QUESTION # 264
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 dropout rate for the second-to-last layer.
- B. Increase the number of epochs for model training
- C. Increase the training data by adding variation in rotation for training images.
- D. Increase the number of layers for the neural network.
Answer: B
NEW QUESTION # 265
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. Natural Language Toolkit (NLTK) stemming and stop word removal
- B. Scikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer
- C. Amazon Comprehend syntax analysis and entity detection
- D. Amazon SageMaker BlazingText cbowmode
Answer: B
Explanation:
Explanation/Reference: https://monkeylearn.com/sentiment-analysis/
NEW QUESTION # 266
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