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NEW QUESTION 50
A Data Engineer needs to build a model using a dataset containing customer credit card information How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?
- A. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
- B. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
- C. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.
- D. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.
Answer: B
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/pca.html
NEW QUESTION 51
A retail company intends to use machine learning to categorize new products. A labeled dataset of current products was provided to the Data Science team. The dataset includes 1,200 products.
The labeled dataset has 15 features for each product such as title dimensions, weight, and price.
Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?
- A. A deep convolutional neural network (CNN) with a softmax activation function for the last layer
- B. AnXGBoost model where the objective parameter is set to multi:softmax
- C. A regression forest where the number of trees is set equal to the number of product categories
- D. A DeepAR forecasting model based on a recurrent neural network (RNN)
Answer: B
Explanation:
A XGBoost multi class classification.
https://medium.com/@gabrielziegler3/multiclass-multilabel-classification-with-xgboost-
66195e4d9f2d
CNN is used for image classificaiton problems.
NEW QUESTION 52
A data engineer at a bank is evaluating a new tabular dataset that includes customer dat a. The data engineer will use the customer data to create a new model to predict customer behavior. After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other.
Which steps should the data engineer take to address this issue? (Choose two.)
- A. Apply min-max feature scaling to the dataset.
- B. Apply principal component analysis (PCA).
- C. Apply one-hot encoding category-based variables.
- D. Use a linear-based algorithm to train the model.
- E. Remove a portion of highly correlated features from the dataset.
Answer: A,B
Explanation:
Reference:
https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html
NEW QUESTION 53
The displayed graph is from a forecasting model for testing a time series.
Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?
- A. The model predicts the trend well, but not the seasonality.
- B. The model predicts the seasonality well, but not the trend.
- C. The model predicts both the trend and the seasonality well
- D. The model does not predict the trend or the seasonality well.
Answer: D
NEW QUESTION 54
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.
Based on this information which model would have the HIGHEST accuracy?
- A. Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))
- B. Single perceptron with tanh activation function
- C. Support vector machine (SVM) with non-linear kernel
- D. Logistic regression
Answer: C
NEW QUESTION 55
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