GIAC GMLE EXAM OUTLINE & GMLE DETAIL EXPLANATION

GIAC GMLE Exam Outline & GMLE Detail Explanation

GIAC GMLE Exam Outline & GMLE Detail Explanation

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Tags: GMLE Exam Outline, GMLE Detail Explanation, Relevant GMLE Answers, Official GMLE Practice Test, GMLE Test Collection

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GIAC Machine Learning Engineer Sample Questions (Q44-Q49):

NEW QUESTION # 44
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. Ignore the inconsistencies and proceed directly to model training
  • B. Identify and remove records with missing values, apply data normalization techniques, and ensure data consistency before model training
  • C. Collect additional data from a different source without fixing the original dataset
  • D. Focus on fixing inconsistencies only in the training data, ignoring the test data

Answer: B


NEW QUESTION # 45
What refers to models capturing noise in the training data as if it were a true signal in supervised learning models?
Response:

  • A. The process of training models on large datasets
  • B. Models that are too simplistic to capture underlying patterns
  • C. Overfitting
  • D. Models performing equally on training and test data

Answer: C


NEW QUESTION # 46
Which of the following is a data manipulation technique commonly applied to prepare data for machine learning models?
Response:

  • A. Data augmentation
  • B. Overfitting
  • C. Hyperparameter tuning
  • D. Normalization

Answer: D


NEW QUESTION # 47
Which of the following are important considerations when acquiring data from SQL databases?
(Choose two)
Response:

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

Answer: A,C


NEW QUESTION # 48
What is the primary goal of a 'confusion matrix' in classification problems?
Response:

  • A. To calculate the loss function of the model
  • B. To visualize the model's performance by showing true and false predictions
  • C. To display the number of features used in the model
  • D. To determine the optimal number of clusters

Answer: B


NEW QUESTION # 49
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