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Title: Go to A00-406 Questions - Try A00-406 dumps pdf [Q11-Q29]

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 Go to A00-406 Questions - Try A00-406 dumps pdf 
Dumps Practice Exam Questions Study Guide for the A00-406 Exam


Q11. What is “model retraining” in the context of model deployment?
 The process of building the initial model
 The process of feature selection
 Periodically updating and improving a deployed model with new data
 The process of data preprocessing
Q12. Which evaluation metric is commonly used for assessing the performance of a regression model?
 F1 Score
 Mean Absolute Error (MAE)
 Precision
 Confusion Matrix
Q13. What does “feature selection” refer to in the context of model building?
 The process of choosing the most relevant variables (features) for the model
 The creation of synthetic features from existing data
 The evaluation of model accuracy
 The visualization of data distribution
Q14. In the context of data sources, what is meant by data versioning?
 Storing multiple copies of the same data to increase redundancy
 Keeping track of different versions or changes to data over time
 Encrypting data to protect against unauthorized access
 Compressing data to reduce storage space
Q15. Which evaluation metric is commonly used for assessing the performance of a binary classification model?
 Mean Absolute Error (MAE)
 Root Mean Squared Error (RMSE)
 Accuracy
 R-squared
Q16. What is the difference between a classification problem and a regression problem in machine learning?
 Classification predicts categorical outcomes, while regression predicts numeric outcomes.
 Classification is a type of regression problem.
 Regression predicts categorical outcomes, while classification predicts numeric outcomes.
 There is no difference; the terms are used interchangeably.
Q17. In a machine learning pipeline, what is the purpose of cross-validation?
 To split the dataset into training and testing sets
 To evaluate the model’s performance on new data
 To train multiple models on different subsets of the data to assess generalization
 To visualize the data distribution
Q18. What is the purpose of hyperparameter tuning in a machine learning pipeline?
 To train the model
 To select the most important features
 To optimize the model’s hyperparameters for better performance
 To evaluate the model’s predictions
Q19. What is “model deployment” in the context of data science and machine learning?
 The process of building a model
 The process of selecting features
 Making the model available for use in real-world applications
 The process of data cleaning
Q20. Which type of model is typically used for time-series forecasting?
 Decision Trees
 Logistic Regression
 AutoRegressive Integrated Moving Average (ARIMA)
 K-Means Clustering
Q21. Which data source allows for real-time data streaming and processing?
 Data warehouses
 Cloud storage
 IoT devices
 Static data files
Q22. In natural language processing (NLP), what is a common preprocessing step for text data before building models?
 Standardization
 Tokenization
 Principal Component Analysis (PCA)
 One-Hot Encoding
Q23. What is metadata in the context of data sources?
 Data that is stored in a physical format
 Data about data, providing information such as data source, structure, and context
 Data that is encrypted for security
 Data that is in a non-standard, proprietary format
Q24. What is the primary purpose of “continuous integration and continuous deployment” (CI/CD) in the context of model deployment?
 To evaluate the model’s accuracy
 To automate the testing, integration, and deployment of new model versions
 To create synthetic data
 To visualize data distribution
Q25. What is the purpose of a “canary release” in the context of model deployment?
 To assess data quality
 To deploy a new model version to a small subset of users or systems for testing
 To create synthetic data
 To evaluate model accuracy
Q26. What is the main goal of data preprocessing in a machine learning pipeline?
 To train the model
 To remove irrelevant features
 To prepare the data for analysis and modeling
 To visualize the data
Q27. What is a common example of an external data source for an organization?
 Employee databases
 Customer surveys
 Internal emails
 Intranet portals
Q28. In natural language processing, what does “stemming” involve?
 Grouping similar words together based on their meanings
 Reducing words to their base or root form
 Converting text to numbers for model input
 Creating new words to improve model performance
Q29. When building a deep learning neural network, what is the purpose of the activation function in each neuron?
 To initialize the model
 To define the learning rate
 To introduce non-linearity
 To control the number of hidden layers
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Post date: 2024-10-07 11:14:37

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