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NEW QUESTION # 26
What is feature engineering in the context of machine learning pipelines?
- A. Creating new features from existing data
- B. Applying the model to new data
- C. Testing the model's performance
- D. Building a machine learning model from scratch
Answer: A
NEW QUESTION # 27
What is the purpose of cross-validation in model building and evaluation?
- A. Reducing the dataset size
- B. Splitting the dataset into training and testing sets
- C. Generating synthetic data
- D. Assessing the model's generalization performance
Answer: D
NEW QUESTION # 28
In reinforcement learning, what is the "reward signal"?
- A. The final prediction made by the model
- B. A numerical value that indicates the performance of an action taken by the agent
- C. A regularization parameter
- D. The accuracy of the model's predictions
Answer: B
NEW QUESTION # 29
In model assessment, what does "cross-validation" aim to address?
- A. Training a model
- B. Overfitting and generalization
- C. Model deployment
- D. Data preprocessing
Answer: B
NEW QUESTION # 30
In machine learning, what does "overfitting" refer to?
- A. A model that performs well on new, unseen data
- B. A model that is undertrained and has high bias
- C. A model that has too much complexity and fits the training data too closely
- D. A model that is unable to make predictions
Answer: C
NEW QUESTION # 31
Which data source allows for real-time data streaming and processing?
- A. Static data files
- B. IoT devices
- C. Cloud storage
- D. Data warehouses
Answer: B
NEW QUESTION # 32
What is the primary purpose of "continuous integration and continuous deployment" (CI/CD) in the context of model deployment?
- A. To evaluate the model's accuracy
- B. To visualize data distribution
- C. To automate the testing, integration, and deployment of new model versions
- D. To create synthetic data
Answer: C
NEW QUESTION # 33
What is metadata in the context of data sources?
- A. Data about data, providing information such as data source, structure, and context
- B. Data that is encrypted for security
- C. Data that is stored in a physical format
- D. Data that is in a non-standard, proprietary format
Answer: A
NEW QUESTION # 34
What is the purpose of data profiling in data source management?
- A. To create data visualizations
- B. To execute data queries
- C. To optimize data storage
- D. To assess the quality and characteristics of data
Answer: D
NEW QUESTION # 35
What is the primary objective of model validation during the model assessment phase?
- A. To build a model from scratch
- B. To assess the accuracy of the model
- C. To create synthetic data
- D. To ensure the model generalizes well to new, unseen data
Answer: D
NEW QUESTION # 36
Which of the following is a common technique for handling missing data in a machine learning pipeline?
- A. Imputing missing values
- B. Ignoring missing data
- C. Deleting rows with missing data
- D. Replacing missing values with zeros
Answer: A
NEW QUESTION # 37
Which feature extraction method can take both interval variables and class variables as inputs?
- A. Autoencoder
- B. Robust PCA
- C. Principal component analysis
- D. Singular value decomposition
Answer: A
NEW QUESTION # 38
What is the primary goal of A/B testing in the context of model deployment?
- A. To compare two different versions of a model or strategy to determine which performs better
- B. To evaluate the model's accuracy
- C. To assess data quality
- D. To create synthetic data
Answer: A
NEW QUESTION # 39
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. The number of hidden units is 50.
- B. There are no hidden units in the model.
- C. The number of hidden units is 26.
- D. The number of hidden units is 1.
Answer: C
NEW QUESTION # 40
What is the primary purpose of model documentation in the model deployment phase?
- A. To evaluate the model's accuracy
- B. To assess data quality
- C. To provide information on the model's development, architecture, and usage
- D. To create synthetic data
Answer: C
NEW QUESTION # 41
In a machine learning pipeline, what is the purpose of cross-validation?
- A. To split the dataset into training and testing sets
- B. To visualize the data distribution
- C. To train multiple models on different subsets of the data to assess generalization
- D. To evaluate the model's performance on new data
Answer: C
NEW QUESTION # 42
Which algorithm is commonly used for binary classification in machine learning pipelines, especially when dealing with imbalanced datasets?
- A. Linear Regression
- B. K-Means Clustering
- C. Principal Component Analysis (PCA)
- D. Support Vector Machine (SVM)
Answer: D
NEW QUESTION # 43
What is a data lake?
- A. A backup system for relational databases
- B. A centralized repository for storing all structured and unstructured data at any scale
- C. A data storage solution designed for high-speed data retrieval
- D. A specialized database for time-series data
Answer: B
NEW QUESTION # 44
In natural language processing, what does "stemming" involve?
- A. Creating new words to improve model performance
- B. Grouping similar words together based on their meanings
- C. Reducing words to their base or root form
- D. Converting text to numbers for model input
Answer: C
NEW QUESTION # 45
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