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NEW QUESTION 20
A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues.
The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset.
Which feature engineering technique should the Data Scientist use to meet the objectives?

• A. Cluster raw data using k-means and use sample data from each cluster to build a new dataset
• B. Use an autoencoder or principal component analysis (PCA) to replace original features with new features
• C. Normalize all numerical values to be between 0 and 1
• D. Run self-correlation on all features and remove highly correlated features

Answer: C

NEW QUESTION 21
This graph shows the training and validation loss against the epochs for a neural network The network being trained is as follows
* Two dense layers one output neuron
* 100 neurons in each layer
* 100 epochs
* Random initialization of weights

Which technique can be used to improve model performance in terms of accuracy in the validation set?

• A. Early stopping
• B. Random initialization of weights with appropriate seed
• C. Adding another layer with the 100 neurons
• D. Increasing the number of epochs

Answer: C

NEW QUESTION 22
A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.
What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

• A. Residual plots
• B. Root Mean Square Error (RMSE)
• C. Area under the curve
• D. Confusion matrix

Answer: C

NEW QUESTION 23
A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.
Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)

• A. Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.
• B. The sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.
• C. The sales data is missing zero entries for item sales. Request that item sales data from the source database include zero entries to enable building the model.
• D. Only 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.
• E. Detecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.

Answer: B,E

Explanation:
Reference:
https://arxiv.org/ftp/arxiv/papers/1302/1302.6613.pdf

NEW QUESTION 24
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