A00-406 PDF Dumps Dec 03, 2024 Exam Questions – Valid A00-406 Dumps
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NEW QUESTION # 33
In the context of model building, what is the purpose of hyperparameter tuning?
- A. Selecting the most important features
- B. Visualizing data
- C. Optimizing the model's hyperparameters for better performance
- D. Training the model
Answer: C
NEW QUESTION # 34
In the context of data integration, what does "data transformation" refer to?
- A. Backing up data for disaster recovery
- B. Storing data in a centralized repository
- C. Extracting data from source systems
- D. Converting and reshaping data to match the target schema
Answer: D
NEW QUESTION # 35
What is the primary function of a data catalog in managing data sources?
- A. Data documentation and discovery
- B. Data analysis
- C. Data storage
- D. Data visualization
Answer: A
NEW QUESTION # 36
In a supervised machine learning pipeline, what is the purpose of the test data set?
- A. To preprocess the data
- B. To train the machine learning model
- C. To validate the model's performance
- D. To evaluate the model's predictions
Answer: C
NEW QUESTION # 37
What is the purpose of cross-validation in model building and evaluation?
- A. Reducing the dataset size
- B. Generating synthetic data
- C. Assessing the model's generalization performance
- D. Splitting the dataset into training and testing sets
Answer: C
NEW QUESTION # 38
Which technique is commonly used for feature scaling or normalization in machine learning pipelines?
- A. Standardization
- B. Principal Component Analysis (PCA)
- C. Decision Trees
- D. One-Hot Encoding
Answer: A
NEW QUESTION # 39
Which type of model is typically used for time-series forecasting?
- A. Logistic Regression
- B. K-Means Clustering
- C. Decision Trees
- D. AutoRegressive Integrated Moving Average (ARIMA)
Answer: D
NEW QUESTION # 40
What does "data lineage" refer to in the context of data source management?
- A. The structure of a relational database
- B. The physical location of data storage
- C. The security protocols for data access
- D. The history of data transformation processes
Answer: D
NEW QUESTION # 41
When deploying a machine learning model, what is "model drift"?
- A. The process of feature extraction
- B. A measure of feature importance
- C. A sudden increase in the model's accuracy
- D. A change in the distribution of the input data or target variable over time
Answer: D
NEW QUESTION # 42
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 50.
- C. The number of hidden units is 26.
- D. The number of hidden units is 1.
Answer: C
NEW QUESTION # 43
When deploying a model, what is "model explainability"?
- A. The process of data preprocessing
- B. The capability to interpret and understand the model's decisions and predictions
- C. The simplicity of the model
- D. The time it takes to make predictions
Answer: B
NEW QUESTION # 44
When building a deep learning neural network, what is the purpose of the activation function in each neuron?
- A. To initialize the model
- B. To define the learning rate
- C. To control the number of hidden layers
- D. To introduce non-linearity
Answer: D
NEW QUESTION # 45
In natural language processing (NLP), what is a common preprocessing step for text data before building models?
- A. Principal Component Analysis (PCA)
- B. Standardization
- C. One-Hot Encoding
- D. Tokenization
Answer: D
NEW QUESTION # 46
Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?
- A. Min samples split
- B. Max depth
- C. Max features
- D. Learning rate
Answer: B
NEW QUESTION # 47
Which feature extraction method can take both interval variables and class variables as inputs?
- A. Autoencoder
- B. Singular value decomposition
- C. Robust PCA
- D. Principal component analysis
Answer: A
NEW QUESTION # 48
Which statement is true regarding decision trees and models based on ensembles of trees?
- A. For a Forest model, the out-of-bag sample is simply the original validation data set from when the raw data partitioning took place.
- B. In the gradient boosting algorithm, for all but the first iteration, the target is the residual from the previous decision tree model.
- C. In the Forest algorithm, each individual tree is pruned based on using minimum Average Squared Error.
- D. A single decision tree will always be outperformed by a model based on an ensemble of trees.
Answer: B
NEW QUESTION # 49
Which of the following is an example of a NoSQL database that is commonly used to store unstructured data?
- A. MongoDB
- B. Microsoft SQL Server
- C. Oracle Database
- D. MySQL
Answer: A
NEW QUESTION # 50
What does API stand for in the context of data sources?
- A. Automated Program Integration
- B. Application Program Interface
- C. Advanced Programming Integration
- D. Application Programming Interface
Answer: D
NEW QUESTION # 51
What is metadata in the context of data sources?
- A. Data that is encrypted for security
- B. Data that is in a non-standard, proprietary format
- C. Data about data, providing information such as data source, structure, and context
- D. Data that is stored in a physical format
Answer: C
NEW QUESTION # 52
What is the primary objective of model validation during the model assessment phase?
- A. To create synthetic data
- B. To ensure the model generalizes well to new, unseen data
- C. To assess the accuracy of the model
- D. To build a model from scratch
Answer: B
NEW QUESTION # 53
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