CCNA Exploratory Data Analysis Questions

Amazon Athena is a serverless interactive query service that allows running standard SQL queries directly on data stored in Amazon S3, without needing to move the data. It can easily count duplicate rows using SQL GROUP BY and HAVING clauses. Option B (Amazon Redshift Spectrum) is wrong because although it can query data in S3, it requires an active Redshift cluster, which is unnecessary for this simple ad-hoc query.

Option C (AWS Glue) is wrong because Glue is an ETL service for data preparation and cataloging, not a query engine. Option D (Amazon SageMaker Studio) is wrong because it is an integrated development environment for machine learning, not a SQL query service.

Options A and B are correct because a Pearson correlation coefficient of 0.95 indicates a very strong positive linear relationship between the two features. This strong linear relationship suggests potential multicollinearity if both features are used as predictors in a linear model. Option C is incorrect because a positive correlation means the features move in the same direction, not opposite.

Option D is wrong because a high correlation implies dependence, not statistical independence. Option E is incorrect because correlation does not imply causation; it only measures the strength and direction of a linear relationship.

Median imputation is robust to outliers and appropriate for numerical features like Age and Income. Using median preserves the central tendency without being affected by extreme values. Option B is incorrect because mode is suitable for categorical features, not continuous numerical ones.

Option C is incorrect because k-NN imputation, while possible, is more complex and typically used after simpler methods in EDA. Option D is incorrect because dropping rows with missing values would discard a significant portion of the dataset (up to 25% if missingness is independent), which is not ideal for initial analysis.

Features with near-zero variance have little to no information content and are often redundant for modeling. Removing them reduces dimensionality without significant loss. Option A is incorrect: high correlation between features suggests multicollinearity, but variance is not the direct measure; correlation is addressed by other techniques like PCA.

Option B is incorrect: negative covariance with the target indicates an inverse relationship, which can be informative. Option C is incorrect: high variance often indicates useful information, though it may warrant scaling; it is not a reason for removal.

The AWS CLI command `aws s3api list-objects --bucket your-bucket --prefix data/ --query 'Contents[?Size > `1000000`].[Key]'` lists the keys (names) of objects in the bucket under the 'data/' prefix whose size is greater than 1,000,000 bytes. The `--query` uses JMESPath to filter objects where Size > 1000000 and then projects the Key field. Option B is incorrect because it states 'counts', but the command returns keys, not a count.

Option C is incorrect because it filters by size, not by date. Option D is incorrect because 1,000,000 bytes is not exactly 1 MB (which is 1,048,576 bytes), so the description 'larger than 1 MB' is inaccurate; the command uses bytes, not MB.

Removing rows with negative transaction amounts is the most appropriate approach during EDA. The negative values are data entry errors, not legitimate transactions. Removing them cleans the dataset without introducing bias from imputation or transformation.

Option A is incorrect because imputing negative values with the median would treat the errors as missing data, but they are not missing; they are erroneous. This could distort the distribution. Option C is incorrect because taking absolute values would convert errors into positive values, adding noise and misrepresenting the data (e.g., a negative $100 error becomes a legitimate $100 transaction).

Option D is incorrect because capping negative values at zero would create a spike at zero and distort the distribution, treating errors as valid zero amounts. Therefore, removal is the cleanest approach for erroneous data.

PCA is a linear dimensionality reduction technique that preserves global structure (variance) and is computationally efficient for large datasets. Option A is wrong because t-SNE is non-linear, slower, and focuses on preserving local structure, not global. Option B is wrong because LDA is a supervised technique that requires class labels, and it is not typically used for unsupervised exploration of clusters.

Option D is wrong because UMAP is non-linear and can be slower than PCA; while it preserves both local and global structure to some extent, it is not as computationally efficient as PCA for very large datasets.

Options A, B, and E are correct. A: Checking data types is fundamental in EDA to understand the nature of each variable. B: Generating descriptive statistics (mean, std, min, max) provides a quick summary of central tendency, dispersion, and range for numerical features.

E: Creating histograms helps visualize the distribution of numerical features, revealing skewness, outliers, or patterns. Option C is incorrect because fitting a linear regression model is a modeling step, not part of EDA. Option D is incorrect because splitting the dataset into training and test sets is for model validation, not for initial data exploration.

Correct options: D and E. Variance Inflation Factor (VIF) (E) is a key metric for detecting multicollinearity by measuring how much the variance of a coefficient increases due to collinearity. PCA (D) addresses multicollinearity by transforming correlated features into orthogonal components.

Option A is incorrect because a correlation matrix only shows pairwise correlations and may miss higher-order multicollinearity. Option B is incorrect because Lasso regression performs feature selection by shrinking coefficients but does not directly identify multicollinearity. Option C is incorrect because Recursive Feature Elimination (RFE) is a feature selection method that does not detect multicollinearity.

Options C and E are the most effective EDA techniques for detecting multicollinearity. Scatter plots of each pair of features (C) allow visual inspection of linear relationships between features. A correlation matrix displayed as a heatmap (E) provides a quantitative measure of pairwise correlations, making it easy to spot high correlations indicative of multicollinearity.

Option A (box plots) shows univariate distributions and does not reveal relationships between features. Option B (VIF analysis) is a formal statistical test for multicollinearity, but it is not typically considered an EDA technique; EDA focuses on visual exploration. Option D (histograms) similarly only show univariate distributions.

Amazon SageMaker Data Wrangler can directly import the dataset from S3 and generate a correlation matrix efficiently without needing to write custom code, making it the most efficient approach for identifying feature correlations with the target variable. Option B is incorrect because using Amazon QuickSight to create scatter plots for each of the 500 features would be time-consuming and not scalable. Option C is incorrect because Amazon Athena uses SQL queries which are not designed to compute correlation coefficients efficiently across a large number of features.

Option D is incorrect because AWS Glue ETL is intended for data transformation pipelines and is not suitable for quick interactive correlation analysis.

Options C and E are correct. Variance Inflation Factor (VIF) is a standard metric to detect multicollinearity; removing features with VIF > 10 reduces multicollinearity. Ridge regression (L2 regularization) can also handle multicollinearity by shrinking coefficients, which stabilizes estimates even when predictors are correlated.

Option A is incorrect because PCA reduces dimensionality but the resulting components are orthogonal, not the original features, and using all components does not address multicollinearity among the original predictors. Option B is incorrect because standardizing features only changes their scale, not their correlations. Option D is incorrect because stepwise selection does not directly detect or mitigate multicollinearity; it can even produce unstable models if collinearity is present.

(F1 score) is the correct metric because it balances precision and recall, providing a single measure that is robust to class imbalance. With only 1% positive class, accuracy (Option B) is misleadingly high due to the majority class. AUC-ROC (Option C) can still be high even if recall is low, as it evaluates ranking rather than absolute performance.

Precision (Option D) only considers the proportion of correct positive predictions, ignoring false negatives, which is not suitable when recall is poor. The F1 score captures both aspects, making it the best choice for evaluating minority class performance in this scenario.

The IAM policy includes the s3:ListBucket action with a condition that uses s3:ExistingObjectTag to require each object to have the tag 'data-type' set to 'training'. However, the ListBucket operation lists all objects in the bucket, and the condition is evaluated against each object. If any object in the bucket does not have this tag, the request fails with an access denied error.

Option B is incorrect because the policy does include s3:ListBucket. Option C is incorrect because the bucket name 'my-bucket' is a valid resource identifier; the full ARN is not required. Option D is incorrect because the StringEquals operator is valid for this condition key; the issue is the condition's requirement, not the operator.

Amazon Kinesis Data Analytics enables running SQL queries on streaming data in real-time, which is exactly what the data analyst needs for real-time exploratory data analysis and anomaly detection. Option B (Amazon SageMaker) is incorrect because it is a machine learning service for building and training models, not for running SQL on streaming data. Option C (AWS Glue) is incorrect because it is a serverless ETL service for batch processing, not real-time SQL.

Option D (Amazon Athena) is incorrect because it is an interactive query service for analyzing data in S3 using SQL, but it is designed for batch queries on static data, not streaming data.

Multiple Imputation by Chained Equations (MICE) accounts for relationships between features and preserves variability. Option A is wrong because last observation carried forward is only appropriate for time series data where missing values are filled with the previous observation; it does not handle non-random missing data correlated with other features. Option C is wrong because listwise deletion reduces sample size and may introduce bias when data is not missing completely at random.

Option D is wrong because mean imputation can bias estimates and reduce variability, especially when missingness is related to other features.

Removing one of the highly correlated features reduces multicollinearity, which can stabilize model coefficients and improve interpretability. Option C is correct: Principal Component Analysis (PCA) transforms the correlated features into a set of uncorrelated components, effectively addressing multicollinearity. Option A is incorrect: Log transformation is used to handle skewness or scale differences, not correlation between features.

Option D is incorrect: Creating an interaction term would add a new feature that is highly correlated with the original ones, potentially increasing multicollinearity. Option E is incorrect: Imputing missing values is unrelated to feature correlation; missing value imputation addresses data completeness, not multicollinearity.

Isolation Forest is the most appropriate method for outlier detection in high-dimensional space because it isolates anomalies by randomly splitting features, making it effective for high-dimensional data without assuming any underlying distribution. Option A is wrong because Z-score assumes normality and is univariate, unsuitable for high-dimensional data. Option C is wrong because Mahalanobis distance assumes multivariate normality and can be computationally expensive and sensitive to high dimensionality.

Option D is wrong because IQR is univariate and does not capture interactions between features in high-dimensional spaces.

Computing summary statistics (min, max, percentiles) helps identify the range and potential outliers in the 'age' feature. Option E is correct because a box plot visualizes the distribution and clearly shows outliers, allowing the data scientist to investigate further. Option A is incorrect because removing rows with age > 100 without understanding the context may discard valid data (e.g., errors or special cases).

Option C is incorrect because log transformation changes the scale but does not help in identifying outliers; it is used to handle skewed distributions. Option D is incorrect because imputing age values outside 18-100 with the mean would distort the distribution and is not appropriate for investigating outliers; it should only be considered after understanding the nature of the outliers.

Using pandas .isnull().sum() in a SageMaker notebook is the most appropriate method because it directly provides the count (and thus the percentage when divided by total rows) of missing values per column, which is a standard exploratory data analysis technique. Option A is incorrect because Amazon S3 Select is used for filtering and retrieving subsets of data from S3 objects, not for computing missing values. Option B is incorrect because while Amazon Athena can run SQL queries like SELECT COUNT(*), it is less direct for per-column missing value analysis and requires a schema.

Option C is incorrect because Amazon QuickSight is a visualization tool, not designed for programmatic missing value detection. Option D is incorrect because AWS Glue Crawler discovers schema and partitions, not missing values.

Zero-inflated negative binomial regression models are designed for count data with a high frequency of zeros, which matches the 'purchase_amount' feature having many zeros and a long tail of positive values. Option B is incorrect: Log transformation does not handle the zero-inflation problem; zeros become undefined or need adjustment. Option C is incorrect: Logistic regression is for binary outcomes, not continuous or count data.

Option D is incorrect: Poisson regression accommodates count data but assumes the variance equals the mean and does not handle excess zeros; zero-inflation violates this assumption.

Options A and D are correct. A count plot directly visualizes the frequency of each class in the target variable, making it easy to assess balance. Similarly, value_counts() returns the exact count of each class, providing a numeric measure of balance.

Option B (histogram) is designed for continuous variables; while it can depict the counts of two categories, it is not the standard or most appropriate method for evaluating binary class balance. Option C (scatter plot) is used to explore relationships between two numeric features and does not show class distribution. Option E (correlation matrix) measures linear relationships between numeric features and is irrelevant for assessing target class balance.

Correlation-based feature selection removes highly correlated features, reducing redundancy without distorting the original feature space, and target encoding converts high-cardinality categorical variables into numeric values based on the target mean, which preserves interpretability and avoids dimensionality explosion. Option A is incorrect because PCA reduces interpretability by transforming features into principal components and does not handle categorical variables directly. Option B is incorrect because mutual information is a feature selection method, but label encoding for high-cardinality categoricals can impose arbitrary ordinal relationships.

Option C is incorrect because chi-squared test requires categorical features and is not suitable for high-dimensional numerical data; also, one-hot encoding high-cardinality categoricals leads to a drastic increase in dimensionality.

The command `aws s3api list-objects-v2 --bucket <bucket> --prefix logs/2023/ --query 'Contents[?Size > `100000`].[Key,Size]' --output text` lists objects under the specified prefix with a size greater than 100000 bytes (≈100 KB). The output shows three objects, indicating there are three log files larger than 100 KB. Option A is incorrect because the output does not provide exact size ranges (e.g., 150KB-200KB); it only indicates files exceeding 100 KB.

Option B is incorrect because the command filters by size, so it does not count all objects in the bucket under the prefix. Option D is incorrect because it states the prefix contains exactly three objects, but the command only returns objects larger than the threshold; there may be additional smaller objects not shown.

Options B, C, and D are correct. B: Eigenvalue analysis from PCA can detect multicollinearity; if some eigenvalues are near zero, it indicates high multicollinearity. C: Correlation matrix shows pairwise correlations between features; high correlation coefficients (e.g., >0.8) indicate collinearity.

D: Variance Inflation Factor (VIF) quantifies how much a feature's variance is inflated due to multicollinearity; VIF >10 is often considered problematic. Option A is incorrect because a heatmap of missing values visualizes missing data, not relationships between features. Option E is incorrect because a scatter matrix shows pairwise scatter plots, which can reveal linear relationships but is not a quantitative measure for multicollinearity.

The most appropriate approach for understanding the distribution of numeric features for each class is to use histograms grouped by the class label. Amazon SageMaker Data Wrangler (option C) can generate these histograms, providing a clear visual comparison of how each numeric feature is distributed across class A and class B. This is especially useful with imbalanced data (95% vs 5%) because it reveals differences in shape, central tendency, and spread without being influenced by class frequencies.

Option A (t-test) tests for statistical significance but does not visualize the distribution. Option B (box plots) can show summary statistics but not the full distribution shape as effectively as histograms. Option D (correlation matrix) measures linear relationships with the target but does not show per-class feature distributions.