D491 Introduction to Analytics - Set 3 - Part 1

Test your knowledge of technical writing concepts with these practice questions. Each question includes detailed explanations to help you understand the correct answers.

Question 1: Which tool is commonly used for data wrangling in the data preparation phase?

OpenRefine

SAS

KNIME

Tableau

Question 2: Which phase in the data analytics lifecycle involves interpreting results and sharing them with stakeholders?

Discovery

Data preparation

Communicate results

Model execution

Question 3: What is the main purpose of feature engineering in machine learning?

To clean the dataset

To visualize the data

To create new input features that improve model performance

To select the most important features

Question 4: Which data visualization is most appropriate for comparing proportions of different categories?

Scatter plot

Pie chart

Line chart

Box plot

Question 5: Which algorithm is best suited for classifying non-linear relationships?

Logistic regression

Naive Bayes

Decision tree

Linear regression

Question 6: Which model is most appropriate for predicting binary outcomes?

Linear regression

K-means clustering

Logistic regression

Principal component analysis

Question 7: Which method is commonly used to evaluate the performance of a regression model?

Accuracy

Precision

Mean squared error (MSE)

F1 Score

Question 8: What is the purpose of a decision tree pruning technique?

To remove overfitting by trimming nodes

To add more features to the model

To increase the depth of the tree

To improve visualization

Question 9: Which data preprocessing step is essential for handling categorical variables in machine learning?

Normalization

One-hot encoding

Feature scaling

Imputation

Question 10: Which type of data is best suited for a time series analysis?

Categorical data

Text data

Continuous data collected over time

Demographic data

Question 11: What is the key benefit of using ensemble learning techniques like Random Forest?

Faster training

Better interpretability

Improved accuracy by combining multiple models

Requires less data

Question 12: Which metric is used to evaluate the performance of clustering algorithms?

Silhouette score

F1 Score

Precision

Mean absolute error

Question 13: What is the main purpose of using smoothing techniques in time series forecasting?

To improve model interpretability

To handle missing data

To remove noise and identify trends

To cluster similar time series

Question 14: Which tool is commonly used for creating dashboards in data visualization?

Hadoop

Tableau

SPSS

Question 15: Which step follows the model execution phase in the data analytics lifecycle?

Data collection

Model planning

Communicate results

Feature selection

Question 16: What is a key characteristic of unsupervised learning algorithms?

They require labeled data

They predict categorical outcomes

They identify hidden patterns in unlabeled data

They minimize mean squared error

Question 17: Which algorithm is commonly used for classification tasks?

K-means clustering

Logistic regression

Principal component analysis

Linear regression

Question 18: Which data visualization is used to display the distribution of a continuous variable?

Bar chart

Histogram

Line chart

Scatter plot

Question 19: What is the main goal of using association rule mining in market basket analysis?

To reduce dimensionality

To identify frequent itemsets and relationships between items

To predict continuous outcomes

To classify data into categories

Question 20: Which evaluation metric is commonly used for measuring the performance of binary classification models?

R-squared

Root mean squared error

F1 Score

Mean absolute error

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