[Oct 17, 2024] Valid A00-406 Test Answers & A00-406 Exam PDF [Q15-Q36]

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[Oct 17, 2024] Valid A00-406 Test Answers & A00-406 Exam PDF

Valid SAS Certified Specialist A00-406 Dumps Ensure Your Passing

NEW QUESTION # 15
What is the primary goal of building models in data science and machine learning?

  • A. Making predictions or inferences from data
  • B. Data cleaning
  • C. Data visualization
  • D. Feature engineering

Answer: A


NEW QUESTION # 16
What is a data lake?

  • A. A centralized repository for storing all structured and unstructured data at any scale
  • B. A data storage solution designed for high-speed data retrieval
  • C. A specialized database for time-series data
  • D. A backup system for relational databases

Answer: A


NEW QUESTION # 17
What is the primary objective of model evaluation in the context of building predictive models?

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

Answer: A


NEW QUESTION # 18
In the context of model assessment, what does "bias" refer to?

  • A. A systematic error that causes a model to consistently overpredict
  • B. A measure of the model's precision
  • C. The simplicity of the model
  • D. The process of feature engineering

Answer: A


NEW QUESTION # 19
In the context of model deployment, what is "model compliance"?

  • A. The model's efficiency
  • B. The degree to which the model adheres to regulatory or ethical guidelines
  • C. The process of feature selection
  • D. The model's simplicity

Answer: B


NEW QUESTION # 20
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 1.
  • B. The number of hidden units is 50.
  • C. The number of hidden units is 26.
  • D. There are no hidden units in the model.

Answer: C


NEW QUESTION # 21
Which data source typically provides access to real-time financial market data?

  • A. Social media platforms
  • B. Weather stations
  • C. Stock market APIs
  • D. Online news websites

Answer: C


NEW QUESTION # 22
Which statements are true for the F1 score?
(Choose 2.)

  • A. F1 score is applicable to a model with an interval target.
  • B. F1 score is calculated based on a cut off value.
  • C. F1 score is calculated based on a depth value.
  • D. F1 score is applicable to a model with a binary target.

Answer: B,D


NEW QUESTION # 23
When building a deep learning neural network, what is the purpose of the activation function in each neuron?

  • A. To control the number of hidden layers
  • B. To introduce non-linearity
  • C. To define the learning rate
  • D. To initialize the model

Answer: B


NEW QUESTION # 24
In a machine learning pipeline, what is the purpose of cross-validation?

  • A. To visualize the data distribution
  • B. To split the dataset into training and testing sets
  • C. To evaluate the model's performance on new data
  • D. To train multiple models on different subsets of the data to assess generalization

Answer: D


NEW QUESTION # 25
What does the term "bagging" refer to in ensemble learning?

  • A. The process of combining multiple identical models to reduce variance
  • B. A type of feature extraction
  • C. A form of dimensionality reduction
  • D. A technique that reduces model complexity

Answer: A


NEW QUESTION # 26
In model assessment, what is the purpose of feature importance analysis?

  • A. To assess data quality
  • B. To visualize data distribution
  • C. To evaluate the significance of input features in making predictions
  • D. To create synthetic features

Answer: C


NEW QUESTION # 27
What is feature engineering in the context of machine learning pipelines?

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

Answer: D


NEW QUESTION # 28
In a supervised learning pipeline, what is the role of the training data set?

  • A. To evaluate the model's predictions
  • B. To train the machine learning model
  • C. To validate the model's performance
  • D. To test the model's generalization capability

Answer: B


NEW QUESTION # 29
What is the significance of the "bias-variance trade-off" in machine learning?

  • A. It refers to the trade-off between the number of features and the model's complexity.
  • B. It indicates the trade-off between accuracy and precision.
  • C. It represents the trade-off between underfitting and overfitting.
  • D. It is not relevant in machine learning.

Answer: C


NEW QUESTION # 30
What is "model versioning" in the context of model deployment?

  • A. The process of evaluating model performance
  • B. The process of training a model from scratch
  • C. The practice of keeping track of different versions of a model to maintain reproducibility
  • D. The process of creating synthetic data

Answer: C


NEW QUESTION # 31
In the context of data sources, what is meant by data versioning?

  • A. Encrypting data to protect against unauthorized access
  • B. Storing multiple copies of the same data to increase redundancy
  • C. Compressing data to reduce storage space
  • D. Keeping track of different versions or changes to data over time

Answer: D


NEW QUESTION # 32
Which SAS Viya component is typically used for deploying and monitoring machine learning models in production?

  • A. SAS Data Loader
  • B. SAS Visual Analytics
  • C. SAS Model Manager
  • D. SAS Enterprise Miner

Answer: C


NEW QUESTION # 33
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 # 34
What is the purpose of a confusion matrix in the context of classification models?

  • A. To compute the mean squared error
  • B. To summarize the distribution of target variables
  • C. To evaluate model performance, especially for binary classification
  • D. To visualize the data

Answer: C


NEW QUESTION # 35
What is a common example of an external data source for an organization?

  • A. Customer surveys
  • B. Intranet portals
  • C. Internal emails
  • D. Employee databases

Answer: A


NEW QUESTION # 36
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