2024 New A00-406 Dumps – Real SASInstitute Exam Questions [Q31-Q53]

NO.31 In a supervised learning pipeline, what is the role of the training data set?

To validate the model’s performance

To evaluate the model’s predictions

To train the machine learning model

To test the model’s generalization capability

NO.32 What is the primary difference between supervised and unsupervised learning in model building?

The presence or absence of a target variable

The type of data used

The use of feature engineering

The amount of labeled data required

NO.33 In machine learning, what does “overfitting” refer to?

A model that performs well on new, unseen data

A model that has too much complexity and fits the training data too closely

A model that is undertrained and has high bias

A model that is unable to make predictions

NO.34 In a supervised machine learning pipeline, what is the purpose of the test data set?

To train the machine learning model

To validate the model’s performance

To evaluate the model’s predictions

To preprocess the data

NO.35 What is the primary purpose of model assessment in the context of data science and machine learning?

Data preprocessing

Model building

Evaluating and selecting the best-performing model

Data visualization

NO.36 Which of the following is a common technique for handling missing data in a machine learning pipeline?

Ignoring missing data

Replacing missing values with zeros

Imputing missing values

Deleting rows with missing data

NO.37 What does the term “bagging” refer to in ensemble learning?

A technique that reduces model complexity

The process of combining multiple identical models to reduce variance

A form of dimensionality reduction

A type of feature extraction

NO.38 What is a data lake?

A data storage solution designed for high-speed data retrieval

A centralized repository for storing all structured and unstructured data at any scale

A specialized database for time-series data

A backup system for relational databases

NO.39 Which type of model is typically used for time-series forecasting?

Decision Trees

Logistic Regression

AutoRegressive Integrated Moving Average (ARIMA)

K-Means Clustering

NO.40 Which machine learning technique is typically used for building a model to predict a numeric target variable?

Classification

Regression

Clustering

Dimensionality reduction

NO.41 What is the purpose of data profiling in data source management?

To assess the quality and characteristics of data

To optimize data storage

To create data visualizations

To execute data queries

NO.42 In the context of data sources, what is ETL?

Extract, Transform, Load

Efficient Text Link

Examine, Test, Log

Execute, Terminate, Launch

NO.43 When building a deep learning neural network, what is the purpose of the activation function in each neuron?

To initialize the model

To define the learning rate

To introduce non-linearity

To control the number of hidden layers

NO.44 In model assessment, what does “cross-validation” aim to address?

Training a model

Overfitting and generalization

Data preprocessing

Model deployment

NO.45 In model assessment, what is the purpose of feature importance analysis?

To create synthetic features

To assess data quality

To evaluate the significance of input features in making predictions

To visualize data distribution

NO.46 Which algorithm is commonly used for decision-making tasks in classification models?

K-Means

Decision Trees

Principal Component Analysis (PCA)

Linear Regression

NO.47 What is feature engineering in the context of machine learning pipelines?

Building a machine learning model from scratch

Creating new features from existing data

Testing the model’s performance

Applying the model to new data

NO.48 Refer to the exhibit below:

Based on the output from the Data Exploration node shown in the exhibit, which variable has the most thin tails (most platykurtic distribution)?

Logi_rfm4

Logi_rfm6

Logi_rfm8

Logi_rfm12

NO.49 What is the primary function of a data catalog in managing data sources?

Data storage

Data analysis

Data documentation and discovery

Data visualization

NO.50 Which algorithm is commonly used for binary classification in machine learning pipelines, especially when dealing with imbalanced datasets?

Linear Regression

K-Means Clustering

Support Vector Machine (SVM)

Principal Component Analysis (PCA)

NO.51 When deploying a machine learning model, what is “model drift”?

A sudden increase in the model’s accuracy

A change in the distribution of the input data or target variable over time

The process of feature extraction

A measure of feature importance

NO.52 Which data source typically provides access to real-time financial market data?

Social media platforms

Weather stations

Stock market APIs

Online news websites

NO.53 In the context of model building, what is the purpose of hyperparameter tuning?

Selecting the most important features

Optimizing the model’s hyperparameters for better performance

Training the model

Visualizing data

2024 New A00-406 Dumps – Real SASInstitute Exam Questions [Q31-Q53]

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