USEFUL GIAC - GMLE - GIAC MACHINE LEARNING ENGINEER TEST QUESTIONS FEE

Useful GIAC - GMLE - GIAC Machine Learning Engineer Test Questions Fee

Useful GIAC - GMLE - GIAC Machine Learning Engineer Test Questions Fee

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Tags: GMLE Test Questions Fee, Upgrade GMLE Dumps, Reliable GMLE Test Sample, Latest GMLE Test Preparation, GMLE Training Kit

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GIAC Machine Learning Engineer Sample Questions (Q138-Q143):

NEW QUESTION # 138
SQL is commonly used in data manipulation for:
Response:

  • A. Implementing machine learning algorithms
  • B. Generating complex neural network models
  • C. Visualizing data distributions
  • D. Extracting and querying data from databases

Answer: D


NEW QUESTION # 139
What is the primary function of NumPy in machine learning workflows?
Response:

  • A. Web scraping
  • B. Handling data visualizations
  • C. Building deep learning models
  • D. Providing support for large multi-dimensional arrays and matrices

Answer: D


NEW QUESTION # 140
In machine learning, what is 'data normalization'?
Response:

  • A. Encoding categorical variables into numerical values
  • B. Scaling numeric features to a standard range
  • C. Removing duplicate records from the dataset
  • D. Dividing the dataset into training and test sets

Answer: B


NEW QUESTION # 141
Which of the following are common applications of Bayes' Theorem in machine learning?
(Choose two)
Response:

  • A. Developing recommendation systems
  • B. Building decision trees
  • C. Creating Naive Bayes classifiers
  • D. Calculating the posterior probability in classification tasks

Answer: C,D


NEW QUESTION # 142
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. Identify and remove records with missing values, apply data normalization techniques, and ensure data consistency before model training
  • B. Focus on fixing inconsistencies only in the training data, ignoring the test data
  • C. Collect additional data from a different source without fixing the original dataset
  • D. Ignore the inconsistencies and proceed directly to model training

Answer: A


NEW QUESTION # 143
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