Get Dec-2024 Download Latest & Valid Questions For SASInstitute A00-406 exam [Q58-Q73]

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Get Dec-2024 Download Latest & Valid Questions For SASInstitute A00-406 exam

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NEW QUESTION # 58
What does "data lineage" refer to in the context of data source management?

  • A. The history of data transformation processes
  • B. The security protocols for data access
  • C. The structure of a relational database
  • D. The physical location of data storage

Answer: A


NEW QUESTION # 59
What is feature engineering in the context of machine learning pipelines?

  • A. Applying the model to new data
  • B. Creating new features from existing data
  • C. Building a machine learning model from scratch
  • D. Testing the model's performance

Answer: B


NEW QUESTION # 60
Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?

  • A. Max depth
  • B. Min samples split
  • C. Learning rate
  • D. Max features

Answer: A


NEW QUESTION # 61
Which of the following best describes unstructured data?

  • A. Data stored in a relational database
  • B. Data with a clear schema
  • C. Data that is difficult to process and lacks a predefined structure
  • D. Data that is organized in rows and columns

Answer: C


NEW QUESTION # 62
Which of the following is an example of a NoSQL database that is commonly used to store unstructured data?

  • A. Microsoft SQL Server
  • B. MongoDB
  • C. MySQL
  • D. Oracle Database

Answer: B


NEW QUESTION # 63
Which of the following is a common source for external data in the context of business analytics?

  • A. Company financial reports
  • B. Intranet databases
  • C. Employee records
  • D. CRM data

Answer: A


NEW QUESTION # 64
Which type of data source typically stores structured data in a tabular format?

  • A. Text documents
  • B. APIs
  • C. NoSQL databases
  • D. Relational databases

Answer: D


NEW QUESTION # 65
Which of the following metrics is commonly used to evaluate the performance of a binary classification model in a machine learning pipeline?

  • A. R-squared
  • B. Root Mean Squared Error (RMSE)
  • C. Accuracy
  • D. Mean Absolute Error (MAE)

Answer: C


NEW QUESTION # 66
Which type of model is commonly used for anomaly detection in datasets?

  • A. Principal Component Analysis (PCA)
  • B. Clustering Models
  • C. Decision Trees
  • D. Linear Regression

Answer: B


NEW QUESTION # 67
In the context of model building, what is the purpose of hyperparameter tuning?

  • A. Visualizing data
  • B. Training the model
  • C. Optimizing the model's hyperparameters for better performance
  • D. Selecting the most important features

Answer: C


NEW QUESTION # 68
Given the following properties for a neural network model, which statement is true regrading hidden units in the model? The following SAS program is submitted:

  • A. There are no hidden units in the model.
  • B. The number of hidden units is 26.
  • C. The number of hidden units is 1.
  • D. The number of hidden units is 50.

Answer: B


NEW QUESTION # 69
What is the purpose of regularization techniques in model building, such as L1 and L2 regularization?

  • A. To prevent overfitting and reduce model complexity
  • B. To add more features to the model
  • C. To speed up model training
  • D. To increase model complexity

Answer: A


NEW QUESTION # 70
What is the primary objective of model evaluation in the context of building predictive models?

  • A. Cleaning the data
  • B. Visualizing data
  • C. Assessing the model's performance and accuracy
  • D. Discovering patterns in data

Answer: C


NEW QUESTION # 71
In reinforcement learning, what is the "reward signal"?

  • A. The final prediction made by the model
  • B. A regularization parameter
  • C. A numerical value that indicates the performance of an action taken by the agent
  • D. The accuracy of the model's predictions

Answer: C


NEW QUESTION # 72
What is the purpose of cross-entropy loss in machine learning, especially in the context of classification?

  • A. To quantify the variance of a model
  • B. To measure the dissimilarity between predicted and actual class probabilities
  • C. To calculate the mean squared error of a regression model
  • D. To evaluate feature importance

Answer: B


NEW QUESTION # 73
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