100% Free A00-406 Files For passing the exam Quickly UPDATED Nov 08, 2024 A00-406 Dumps Questions Study Exam Guide NEW QUESTION # 25 In the context of data sources, what is ETL? A. Efficient Text Link B. Examine, Test, Log C. Execute, Terminate, Launch D. Extract, Transform, Load Answer: D NEW QUESTION # 26 Which statements are true for the F1 score?(Choose 2.) A. F1 score is applicable to a model [...]

100% Free A00-406 Files For passing the exam Quickly UPDATED Nov 08, 2024 [Q25-Q45]

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100% Free A00-406 Files For passing the exam Quickly UPDATED Nov 08, 2024

A00-406 Dumps Questions Study Exam Guide 

NEW QUESTION # 25
In the context of data sources, what is ETL?

  • A. Efficient Text Link
  • B. Examine, Test, Log
  • C. Execute, Terminate, Launch
  • D. Extract, Transform, Load

Answer: D


NEW QUESTION # 26
Which statements are true for the F1 score?
(Choose 2.)

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

Answer: A,C


NEW QUESTION # 27
In model assessment, what does "cross-validation" aim to address?

  • A. Model deployment
  • B. Training a model
  • C. Overfitting and generalization
  • D. Data preprocessing

Answer: C


NEW QUESTION # 28
What does the term "bagging" refer to in ensemble learning?

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

Answer: D


NEW QUESTION # 29
Refer to the treemap shown in the exhibit below:

Which statement is true about the tree map for a decision tree with a binary target?

  • A. The top bar represents the node with the highest count.
  • B. The darker bars represent nodes with a lower probability of event.
  • C. The top bar represents the node with the highest probability of event.
  • D. The wider bars represent nodes with a higher probability of event.

Answer: A


NEW QUESTION # 30
In model evaluation, what is the purpose of a ROC curve (Receiver Operating Characteristic)?

  • A. To evaluate the mean squared error of a model
  • B. To compare models' performance in terms of sensitivity and specificity
  • C. To measure feature importance
  • D. To visualize data distribution

Answer: B


NEW QUESTION # 31
In the context of data integration, what does "data transformation" refer to?

  • A. Storing data in a centralized repository
  • B. Backing up data for disaster recovery
  • C. Extracting data from source systems
  • D. Converting and reshaping data to match the target schema

Answer: D


NEW QUESTION # 32
What does the term "bias" in machine learning refer to?

  • A. The overall accuracy of a model
  • B. The simplicity of a model
  • C. A model's inability to generalize to new data
  • D. Systematic errors that cause a model to consistently underpredict or overpredict

Answer: D


NEW QUESTION # 33
What is the primary difference between supervised and unsupervised learning in model building?

  • A. The presence or absence of a target variable
  • B. The amount of labeled data required
  • C. The type of data used
  • D. The use of feature engineering

Answer: A


NEW QUESTION # 34
Which type of model is well-suited for solving classification problems when dealing with high- dimensional data, such as text?

  • A. K-Means Clustering
  • B. Random Forest
  • C. Linear Regression
  • D. Support Vector Machine (SVM)

Answer: D


NEW QUESTION # 35
What does API stand for in the context of data sources?

  • A. Application Programming Interface
  • B. Advanced Programming Integration
  • C. Automated Program Integration
  • D. Application Program Interface

Answer: A


NEW QUESTION # 36
In natural language processing (NLP), what is a common preprocessing step for text data before building models?

  • A. Standardization
  • B. Tokenization
  • C. One-Hot Encoding
  • D. Principal Component Analysis (PCA)

Answer: B


NEW QUESTION # 37
In model assessment, what is the purpose of feature importance analysis?

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

Answer: B


NEW QUESTION # 38
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 # 39
What is the primary purpose of a supervised machine learning pipeline in SAS Viya?

  • A. Data visualization
  • B. Data preprocessing and cleaning
  • C. Data storage and retrieval
  • D. Model training and evaluation

Answer: D


NEW QUESTION # 40
Which of the following is a common source for external data in the context of business analytics?

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

Answer: D


NEW QUESTION # 41
What is "model reevaluation" in the model deployment phase?

  • A. The periodic assessment of a deployed model's performance and potential retraining
  • B. The evaluation of data distribution
  • C. The process of selecting features
  • D. The process of data preprocessing

Answer: A


NEW QUESTION # 42
What is the primary function of a data catalog in managing data sources?

  • A. Data visualization
  • B. Data documentation and discovery
  • C. Data storage
  • D. Data analysis

Answer: B


NEW QUESTION # 43
Which of the following best describes unstructured data?

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

Answer: D


NEW QUESTION # 44
What is the primary purpose of model documentation in the model deployment phase?

  • A. To provide information on the model's development, architecture, and usage
  • B. To create synthetic data
  • C. To assess data quality
  • D. To evaluate the model's accuracy

Answer: A


NEW QUESTION # 45
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