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GIAC Machine Learning Engineer Sample Questions (Q105-Q110):

NEW QUESTION # 105
What does the term 'boosting' refer to in the context of machine learning algorithms?
Response:

• A. Decreasing the computational complexity of models
• B. Combining several weak models to form a strong model
• C. Sequentially building models to correct the errors of previous ones
• D. Both B and C

Answer: D

 

NEW QUESTION # 106
In the context of machine learning, what is a 'loss function' used for?
Response:

• A. To increase the speed of training
• B. To select the most important features of the data
• C. To reduce the memory usage of the model
• D. To evaluate the performance of the model

Answer: D

 

NEW QUESTION # 107
You have a large dataset stored as a CSV file, and you want to load, clean, and prepare it for a machine learning model using Python. What steps should you take using Pandas to clean the data and remove any rows with missing values?
Response:

• A. Visualize the missing values with Matplotlib before cleaning the data
• B. Load the data with pd.read_csv() and manually remove missing values from the file
• C. Use the pd.read_csv() function to load the data, followed by the dropna() function to remove rows with missing values, and the describe() function to get summary statistics for further analysis
• D. Use np.load() to load the data and ignore any missing values

Answer: C

 

NEW QUESTION # 108
What is the primary goal of a 'confusion matrix' in classification problems?
Response:

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

Answer: D

 

NEW QUESTION # 109
In CNNs, what is the purpose of pooling layers?
Response:

• A. To increase the depth of the network
• B. To increase the network's sensitivity to noise
• C. To reduce the spatial size of the representation
• D. To directly classify images

Answer: C

 

NEW QUESTION # 110
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