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NEW QUESTION # 38
In the context of model building, what is the purpose of hyperparameter tuning?
- A. Visualizing data
- B. Selecting the most important features
- C. Optimizing the model's hyperparameters for better performance
- D. Training the model
Answer: C
NEW QUESTION # 39
When deploying a model, what is "model explainability"?
- A. The process of data preprocessing
- B. The capability to interpret and understand the model's decisions and predictions
- C. The time it takes to make predictions
- D. The simplicity of the model
Answer: B
NEW QUESTION # 40
What is metadata in the context of data sources?
- A. Data that is in a non-standard, proprietary format
- B. Data about data, providing information such as data source, structure, and context
- C. Data that is encrypted for security
- D. Data that is stored in a physical format
Answer: B
NEW QUESTION # 41
What is the main purpose of feature engineering in model building?
- A. Data preprocessing
- B. Creating new features or transforming existing ones to improve model performance
- C. Data visualization
- D. Model evaluation
Answer: B
NEW QUESTION # 42
In natural language processing (NLP), what is a common preprocessing step for text data before building models?
- A. Tokenization
- B. Standardization
- C. One-Hot Encoding
- D. Principal Component Analysis (PCA)
Answer: A
NEW QUESTION # 43
Which technique is used for feature selection in a machine learning pipeline when dealing with a large number of features?
- A. Regularization
- B. Naive Bayes
- C. One-Hot Encoding
- D. Principal Component Analysis (PCA)
Answer: A
NEW QUESTION # 44
What is the primary goal of A/B testing in the context of model deployment?
- A. To create synthetic data
- B. To evaluate the model's accuracy
- C. To compare two different versions of a model or strategy to determine which performs better
- D. To assess data quality
Answer: C
NEW QUESTION # 45
Which metric is commonly used to evaluate the performance of a regression model?
- A. Mean Absolute Error (MAE)
- B. Precision
- C. F1 Score
- D. Confusion Matrix
Answer: A
NEW QUESTION # 46
In model assessment, what is the purpose of feature importance analysis?
- A. To create synthetic features
- B. To visualize data distribution
- C. To evaluate the significance of input features in making predictions
- D. To assess data quality
Answer: C
NEW QUESTION # 47
In reinforcement learning, what is the agent's objective?
- A. To generate synthetic data
- B. To learn from labeled data
- C. To maximize a cumulative reward over time
- D. To make predictions
Answer: C
NEW QUESTION # 48
What is the main advantage of ensemble methods in model building?
- A. They produce simple and interpretable models
- B. They work well with high-dimensional data
- C. They combine multiple models to improve predictive performance
- D. They require minimal data preprocessing
Answer: C
NEW QUESTION # 49
When deploying a machine learning model, what is "model drift"?
- A. A change in the distribution of the input data or target variable over time
- B. A sudden increase in the model's accuracy
- C. The process of feature extraction
- D. A measure of feature importance
Answer: A
NEW QUESTION # 50
What is "model retraining" in the context of model deployment?
- A. The process of data preprocessing
- B. The process of feature selection
- C. Periodically updating and improving a deployed model with new data
- D. The process of building the initial model
Answer: C
NEW QUESTION # 51
What is the main advantage of ensemble learning methods, such as Random Forest, in a machine learning pipeline?
- A. They require minimal data preprocessing.
- B. They are simple and easy to interpret.
- C. They are not suitable for large datasets.
- D. They combine multiple models to improve predictive performance.
Answer: D
NEW QUESTION # 52
Which type of model is commonly used for anomaly detection in datasets?
- A. Decision Trees
- B. Linear Regression
- C. Clustering Models
- D. Principal Component Analysis (PCA)
Answer: C
NEW QUESTION # 53
Which of the following best describes unstructured data?
- A. Data that is difficult to process and lacks a predefined structure
- B. Data stored in a relational database
- C. Data that is organized in rows and columns
- D. Data with a clear schema
Answer: A
NEW QUESTION # 54
Which machine learning technique is typically used for building a model to predict a numeric target variable?
- A. Classification
- B. Clustering
- C. Regression
- D. Dimensionality reduction
Answer: C
NEW QUESTION # 55
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