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NEW QUESTION 49
A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.
Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?

• A. Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.
• B. Plot a histogram of the features and compute their standard deviation. Remove features with low variance.
• C. Plot a histogram of the features and compute their standard deviation. Remove features with high variance.
• D. Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.

Answer: D

NEW QUESTION 50
An ecommerce company sends a weekly email newsletter to all of its customers. Management has hired a team of writers to create additional targeted content. A data scientist needs to identify five customer segments based on age, income, and location. The customers' current segmentation is unknown. The data scientist previously built an XGBoost model to predict the likelihood of a customer responding to an email based on age, income, and location.
Why does the XGBoost model NOT meet the current requirements, and how can this be fixed?

• A. The XGBoost model provides a true/false binary output. Apply principal component analysis (PCA) with five feature dimensions to predict a segment.
• B. The XGBoost model provides a true/false binary output. Increase the number of classes the XGBoost model predicts to five classes to predict a segment.
• C. The XGBoost model is a supervised machine learning algorithm. Train a k-Nearest-Neighbors (kNN) model with K = 5 on the same dataset to predict a segment.
• D. The XGBoost model is a supervised machine learning algorithm. Train a k-means model with K = 5 on the same dataset to predict a segment.

Answer: C

NEW QUESTION 51
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. The sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.
• B. Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.
• C. 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.
• D. 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.
• E. 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.

Answer: A,D

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

NEW QUESTION 52
A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population.
How should the Data Scientist correct this issue?

• A. Replace the age field value for records with a value of 0 with the mean or median value from the dataset.
• B. Use k-means clustering to handle missing features.
• C. Drop the age feature from the dataset and train the model using the rest of the features.
• D. Drop all records from the dataset where age has been set to 0.

Answer: A

NEW QUESTION 53
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