ONLINE GIAC GMLE PRACTICE TEST

Online GIAC GMLE Practice Test

Online GIAC GMLE Practice Test

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Tags: GMLE Simulation Questions, Reliable GMLE Test Dumps, GMLE Valid Test Dumps, GMLE New Dumps Questions, Reliable GMLE Dumps Files

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GIAC Machine Learning Engineer Sample Questions (Q113-Q118):

NEW QUESTION # 113
Which of the following are common applications of regression models?
(Choose two)
Response:

  • A. Forecasting sales trends over time
  • B. Classifying spam emails
  • C. Predicting house prices based on various features
  • D. Clustering customer segments

Answer: A,C


NEW QUESTION # 114
Which of the following are important considerations when acquiring data from SQL databases?
(Choose two)
Response:

  • A. Ensuring data integrity by maintaining relationships between tables
  • B. Using raw, unvalidated data for better model accuracy
  • C. Optimizing queries to handle large datasets efficiently
  • D. Ignoring null values to prevent any potential issues

Answer: A,C


NEW QUESTION # 115
What is the purpose of a validation set in machine learning?
Response:

  • A. To train the model
  • B. To prevent overfitting
  • C. To test the model on unseen data
  • D. To fine-tune the model's hyperparameters

Answer: D


NEW QUESTION # 116
What are correlation matrices useful for in data exploration and visualization?
Response:

  • A. Identifying missing data
  • B. Visualizing geographical data
  • C. Determining the accuracy of a model
  • D. Understanding relationships between pairs of variables

Answer: D


NEW QUESTION # 117
You are tasked with collecting data from an external API for use in a machine learning model. After making several requests, you notice that the data contains missing values and inconsistencies across different records. What steps should you take to clean and prepare this data for further analysis and modeling?
Response:

  • A. Focus on fixing inconsistencies only in the training data, ignoring the test data
  • B. Collect additional data from a different source without fixing the original dataset
  • C. Ignore the inconsistencies and proceed directly to model training
  • D. Identify and remove records with missing values, apply data normalization techniques, and ensure data consistency before model training

Answer: D


NEW QUESTION # 118
......

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