AWS ML Specialty EDA Quiz

1. A histogram is most useful in EDA for understanding which characteristic of a feature?

Correlation with other features

The distribution and frequency of values

Categorical relationships

Temporal trends

2. Which AWS service integrates with SageMaker to enable collaborative exploratory data analysis workflows?

AWS Glue

Amazon EMR

SageMaker Studio

AWS Step Functions

3. When preparing data for ML models, why is understanding data skewness important during EDA?

To automatically select the best algorithm

To identify non-normal distributions requiring transformation

To eliminate all numerical features

To increase computational speed

4. What does the interquartile range (IQR) represent in statistical EDA?

The mean of all values

The spread of the middle 50% of data

The total number of observations

The correlation strength

5. In EDA, dimensionality reduction via PCA is useful for which purpose?

To increase the number of features

To visualize high-dimensional data and reduce noise

To remove all categorical variables

To eliminate the need for model training

6. Which metric helps assess data quality by measuring the proportion of missing values in a dataset?

Precision

Completeness

Accuracy

F1-score

7. Missing data patterns in EDA can best be visualized using which technique?

Scatter plots

Missing data heatmaps or nullity matrices

Histograms

Pie charts

8. When exploring categorical data in EDA, what does a stacked bar chart effectively display?

The distribution of one category across groups

Continuous variable relationships

Time-series trends

Outlier detection

9. In AWS SageMaker, which tool provides automated data profiling and quality insights?

Data Wrangler

Ground Truth

Feature Store

Model Monitor

10. What does a heatmap reveal when applied to a correlation matrix during EDA?

The geographic location of data points

Linear relationships and multicollinearity among features

The optimal number of clusters

Missing data patterns

11. Which AWS service is best suited for interactive exploratory data analysis on large datasets stored in S3?

Amazon SageMaker Notebooks

AWS Lambda

Amazon RDS

AWS Glue

12. In EDA, what is the purpose of feature scaling or normalization?

To remove categorical variables

To ensure features have comparable ranges for analysis

To eliminate outliers

To increase model accuracy automatically

13. Which AWS service can be used to query and analyze data stored in S3 without loading it into memory?

Amazon Athena

Amazon EC2

AWS Lambda

Amazon RDS

14. What does class imbalance in a classification dataset indicate during EDA?

The model will automatically balance classes

One class has significantly fewer samples than others

Data preprocessing is unnecessary

The features are perfectly correlated

15. When is it appropriate to use a box plot during exploratory data analysis?

To show relationships between three variables

To identify outliers and quartiles in univariate data

To compare categorical distributions

To display time-series trends

16. Which technique helps identify patterns and anomalies in time-series data during EDA?

Principal Component Analysis

Autocorrelation and seasonal decomposition

K-means clustering

Cross-validation

17. In AWS SageMaker Data Wrangler, what is the primary benefit of using built-in data quality reports?

Automatic model training

Identifying missing values and data inconsistencies

Deploying endpoints directly

Reducing infrastructure costs

18. What does a high correlation coefficient between two features suggest in EDA?

One feature causes the other

The features are strongly linearly related

Both features should be removed

The data contains errors

19. Which visualization is most effective for detecting the relationship between two continuous variables?

Histogram

Scatter plot

Bar chart

Box plot

20. When performing EDA, what is the primary purpose of calculating summary statistics like mean, median, and standard deviation?

To remove outliers automatically

To understand central tendency and data spread

To normalize all features

To identify the correct ML algorithm

AWS ML Specialty Exploratory Data Analysis Quiz