CCNA Exploratory Data Analysis Questions

Removing stop words (common words like 'the', 'and') ensures that the word cloud highlights meaningful content. Stemming (C) may not be necessary for a word cloud. Tokenization (D) is fundamental but not the most critical for meaningfulness.

POS tagging (B) is overkill.

(bar chart) is wrong because bar charts are for categorical data, not for showing distribution of a continuous feature. Option B (scatter plot) is wrong because scatter plots show relationships between two variables, not distribution. Option C is correct because a box plot explicitly shows median, quartiles, and outliers.

Option D is wrong because heatmaps show correlations, not distribution.

The autocorrelation function (ACF) measures the correlation between a time series and its lagged values. A significant spike at lag 7 confirms weekly seasonality. Option A (line chart) is subjective and not a definitive test.

Option C (Fourier transform) identifies frequency components but is more complex and less direct for confirming seasonality. Option D (moving average) smooths the series and may obscure seasonality.

Using Data Wrangler's built-in data quality and insights report is the most efficient way to get statistics and detect issues without custom code. Option B (Athena) requires writing SQL queries. Option C (QuickSight) needs exporting.

Option D (EMR) is overkill.

The most critical EDA step for a highly imbalanced target variable is to examine its distribution. Therefore, plotting the histogram of the target variable (D) reveals the imbalance and guides decisions on resampling or evaluation metrics. Option A (PCA) is primarily for dimensionality reduction and not essential for understanding target balance.

Option B (correlation matrix) examines feature relationships, not target distribution. Option C (mean imputation) addresses missing values, which is important but not the most critical for handling class imbalance.

A log transformation is commonly applied to right-skewed positive data to reduce skewness and make the distribution more symmetric. Option A is wrong because standardization (Z-score) centers and scales the data but does not change the shape of the distribution. Option B is wrong because a square transformation would increase skewness for right-skewed data.

Option D is wrong because min-max scaling rescales the data to a fixed range but does not alter the distribution's skewness.

Converting the string to a date type in the query allows Athena to use partition pruning if the table is partitioned by date, reducing scanned data. Option A is wrong because creating a view does not reduce data scanned; CAST still processes all rows. Option B is wrong because using CAST in the SELECT statement still scans all data.

Option C is wrong because converting to Parquet is beneficial but not the most direct for the given task.

Logarithmic transformation compresses the long tail of right-skewed data, making the distribution more symmetrical. Option A (Box-Cox with lambda=2) is actually a square transformation, which would exacerbate right skewness. Option C (standardization) only centers and scales the data without altering the shape.

Option D (square root) can reduce moderate right skew but is less effective than log for severe skewness.

A 1 MB CSV file likely contains many rows, but querying returns only one row, which indicates that the table's partition mapping is incorrect. Athena uses partitions to minimize data scanned; if the partition definition does not match the actual data location, the query may only read (or miss) certain partitions, resulting in fewer rows. Option B is wrong because a 1 MB file is too large to contain only one row, as typical CSV rows are much smaller.

Option C is irrelevant: whether the table is external does not affect row count. Option D is wrong because Athena supports CSV format.

Mutual information can capture any kind of dependency (including nonlinear) between features and target. Option A (ANOVA) compares means across groups but assumes linearity. Option B (Pearson correlation) only captures linear relationships.

Option D (Chi-squared test) is for categorical features, not suitable for the mix of numeric and categorical features.

Athena counts rows from data files; if files are empty or contain only headers, they contribute 0 rows. With 1500 files and only 1000 rows, it is plausible that many files are empty or header-only, especially if the data pipeline produces such files. Option A is incorrect because Athena still parses lines even with a delimiter mismatch, treating each line as a row (though columns may be incorrect).

Option B is incorrect because schema mismatch typically causes query errors, not silent skipping. Option D is false because Athena does not skip files based on size limits.

Options B and E are correct. A histogram displays the frequency distribution of a single numeric variable by binning the data, and a box plot shows the five-number summary (minimum, first quartile, median, third quartile, maximum) to visualize spread and outliers. Option A (heatmap) is typically used for two numeric variables to show density or correlation.

Option C (bar chart) is for categorical data, not numeric distribution. Option D (scatter plot) visualizes the relationship between two numeric variables, not the distribution of one.

Log transformation is commonly used to reduce right skewness and make the distribution approximately normal. Option B (min-max scaling) is incorrect because it does not change the shape of the distribution. Option C (one-hot encoding) is incorrect because it is used for categorical variables, not for transforming continuous skewed data.

Option D (standardization) is incorrect because it does not change the shape of the distribution.

C is correct because stratified sampling preserves the original class proportion (99:1) in the sample, which is important for exploratory data analysis on imbalanced data without artificially altering the distribution. Option A (remove all negatives) loses all negative data, preventing analysis of majority class patterns. Option B (random sample) may result in insufficient positive examples due to imbalance.

Option D (up-sample minority) would change the distribution and could lead to misleading visualizations and statistics during EDA.

Isolation Forest is an ensemble method that isolates anomalies effectively in high-dimensional spaces without assuming any specific distribution. Option A is wrong because Z-score assumes a normal distribution. Option B is wrong because IQR is univariate and does not capture multivariate interactions.

Option C is wrong because DBSCAN is primarily a clustering algorithm and is not specifically designed for outlier detection, though it can identify outliers as noise; however, Isolation Forest is more robust for this purpose.

The ETag can be used for integrity checking, as it is an MD5 hash of the object content. Option B is wrong because ContentLength 1048576 corresponds to 1 MB, not 1 GB. Option C is wrong because S3 versioning is indicated by VersionId, not ETag.

Option D is wrong because the output does not provide any information about preprocessing; the presence of metadata like 'preprocessed' is not shown.

SPICE (Super-fast, Parallel, In-memory Calculation Engine) is Amazon QuickSight's in-memory engine that caches data, enabling fast query performance without losing accuracy. Importing the dataset into SPICE speeds up histogram computation while preserving full data. Option A is incorrect because using Athena with a limit clause reduces the number of rows, losing accuracy.

Option B is incorrect because reducing bins changes histogram granularity, not necessarily improving performance and potentially losing detail. Option C is incorrect because sampling reduces accuracy by excluding data points.

'object' dtype in pandas typically indicates string or mixed types. The appropriate EDA step is to attempt conversion to numeric using pd.to_numeric() and investigate non-convertible values to handle data quality issues. Option A is incorrect because object dtype does not specifically indicate missing values; missing values can appear in any dtype.

Option C is premature; conversion should precede normalization. Option D is incorrect because object dtype is not numeric.

The ANOVA F-statistic measures the ratio of between-group variance to within-group variance, directly quantifying separability. Option B is wrong because 'variance ratio' is not the standard name; the correct metric is the F-statistic. Option C is wrong because the chi-square test is for categorical features, not continuous ones like feature X.

Option D is wrong because mutual information measures dependency but does not specifically test separability in terms of variance between groups.

Dropping one of the highly correlated features reduces redundancy and mitigates multicollinearity. Option A (PCA) creates orthogonal components, which addresses multicollinearity but reduces interpretability; dropping a feature is more straightforward. Option B (Min-Max scaling) does not address collinearity at all.

Option D is wrong because adding polynomial features increases correlation.

Options B and C are correct. Visualizing the pattern of missingness helps determine if data is missing at random, which is a key EDA step. Understanding the missing data mechanism (MCAR, MAR, MNAR) is important for selecting an appropriate imputation strategy.

Option A is incorrect because missing values should be addressed during EDA, not deferred to model training. Option D is incorrect because listwise deletion can introduce bias if data is not MCAR. Option E is incorrect because mean imputation reduces the variance of the imputed variable.

The IQR method, where outliers are defined as points below Q1 - 1.5*IQR or above Q3 + 1.5*IQR, is appropriate for univariate outlier detection as it does not assume a specific distribution and is robust to extreme values. PCA (A) is a dimensionality reduction technique, not for outlier detection. Mahalanobis distance (C) is for multivariate outliers.

Z-score with threshold 3 (D) assumes normality and is sensitive to extreme outliers.

Checking partition metadata (e.g., using SHOW PARTITIONS or querying information_schema) ensures that queries are applying partition pruning, which reduces the amount of data scanned. Option A is incorrect: converting to Parquet improves columnar scan efficiency but does not directly address partition misuse. Option B is incorrect: S3 Select filters data at the object level, but Athena already pushes down filters; this does not fix a lack of partition pruning.

Option C is incorrect: increasing workers improves parallelism but does not reduce scanned data if partitions are not pruned.

The most appropriate because a histogram with overlaid densities, colored by group, directly shows the distribution of each group and can reveal whether the two modes correspond to different groups. Option A (box plot) displays summary statistics but not the shape or modality. Option B (bar chart) is for categorical data, not continuous.

Option D (scatter plot) is for two continuous variables, not for examining a single distribution.

SMOTE (Synthetic Minority Over-sampling Technique) is a popular method for handling imbalanced datasets by generating synthetic samples for the minority class. Option A (PCA) is wrong because dimensionality reduction does not address class imbalance. Option B (removing outliers) is wrong because it may worsen imbalance and is not a standard technique for imbalance.

Option C (cross-validation) is a model evaluation technique, not a solution for imbalance. Option D (feature scaling) does not affect class distribution.

The policy includes s3:GetObject and s3:ListBucket, so it allows reading objects. However, if the S3 bucket uses server-side encryption with AWS KMS (SSE-KMS), the SageMaker execution role must also have the kms:Decrypt permission to decrypt the object. Without this permission, the read operation fails even though the S3 permissions are correct.

Therefore, the most likely cause is that the bucket uses SSE-KMS and the policy lacks kms:Decrypt, making option C correct.

Converting Unix epoch timestamps to datetime objects allows extraction of the day-of-week using functions like .dt.dayofweek() in pandas. Option B is wrong because converting to string and splitting into date and time does not directly give day-of-week patterns and loses temporal properties. Option C is wrong because min-max scaling is used for normalizing numerical features, not for extracting temporal patterns.

Option D is wrong because binning into 1-hour intervals captures hourly patterns, not day-of-week patterns.

Mazon QuickSight (Option C) because it is a business intelligence (BI) service that can connect directly to data stored in Amazon S3 and create visualizations such as histograms without requiring any coding. Amazon Athena (Option A) is an interactive query service that returns raw query results, not visualizations. Amazon SageMaker Studio (Option B) is a machine learning IDE that typically requires writing code or using notebooks to generate plots.

AWS Glue (Option D) is a serverless data integration service for ETL operations, not for visualization.

Binning the transaction amount and computing fraud rates per bin can reveal non-linear relationships that correlation might miss. Option A is wrong because concluding non-predictiveness based solely on correlation ignores potential non-linear patterns. Option B is premature since feature importance from a random forest model is typically used after feature engineering, not during initial EDA.

Option D is a data transformation that addresses skewness but does not directly help understand the relationship with the target; it would be more appropriate as a preprocessing step.

(Amazon SageMaker Processing with a custom Python script) is the most efficient because it can distribute the computation across multiple instances, processing images in parallel without loading all into memory at once. This is ideal for a large dataset of 1 million images. Option B (Athena) is designed for querying structured data, not image processing.

Option C (AWS Glue ETL) is for ETL on tabular data, not image processing. Option D (SageMaker notebook with large instance) would require loading all images into memory, which is not feasible for 1 million images.

Mutual information captures any kind of dependency, including non-linear. Option A is wrong because a correlation matrix heatmap only shows pairwise linear correlations among features, not feature-target relationships. Option B is wrong because PCA is for dimensionality reduction and does not directly assess feature-target relationships.

Option D is wrong because Pearson correlation only measures linear relationships, missing non-linear ones.

Checking for class imbalance is critical because it can cause a model to predict the majority class and still achieve high accuracy, but fail on the minority class in unseen data. Option A (correlation matrix) is wrong because it helps with multicollinearity, not class imbalance. Option B (log transformation) is wrong because it addresses skewness in features, not class imbalance.

Option D (heatmap of missing values) is wrong because it shows missing data patterns, not class imbalance.

Amazon Athena allows running SQL queries directly on data in S3, and the COUNT and CASE statements can compute missing value percentages efficiently without moving data. Option A is wrong because Amazon SageMaker Notebook requires manual coding and is less efficient for quick checks. Option B is wrong because Amazon QuickSight is a visualization tool, not for direct SQL-based analysis.

Option D is wrong because AWS Glue Crawler only catalogs metadata, not performing data analysis.

(Histogram) is correct because a histogram displays the frequency distribution of a continuous variable by grouping data into bins. Option A (Box plot) is incorrect because it shows summary statistics (median, quartiles, outliers) but not the full distribution. Option B (Bar chart) is incorrect because bar charts are for categorical data, not continuous.

Option D (Scatter plot) is incorrect because it shows the relationship between two continuous variables, not the distribution of a single variable.

Mean imputation is suitable for normally distributed data as it preserves the mean. Median is robust to outliers, not normality. Mode is for categorical data.

Standard deviation is not an imputation method. KNN imputation is non-parametric.

One-hot encoding 'zip_code' with 100 unique values creates 100 binary features. When combined with other features, the total number of features can be large relative to the number of training samples, leading to the curse of dimensionality. This causes the model to overfit the training data and generalize poorly to the validation set.

While multicollinearity among one-hot encoded columns is often low due to their binary nature, the primary issue here is the high dimensionality relative to sample size. Option D correctly identifies this as the most likely cause.

Domain knowledge and documentation are the most reliable ways to understand the meaning of zeros. Option A is wrong because statistical methods cannot distinguish missing vs actual zero without context. Option B is wrong because metadata may not have this detail.

Option C is wrong because comparing to other columns might be misleading.

Multicollinearity indicates high correlation between predictors. Option B: Compute a correlation matrix and look for pairs with absolute correlation > 0.8 directly reveals linear dependencies. Option D: Variance Inflation Factor (VIF) measures how much the variance of a coefficient is inflated due to collinearity; VIF > 5 or 10 suggests multicollinearity.

Option A (PCA) reduces dimensionality but does not directly detect collinearity. Option C (t-test) tests mean differences, not associations. Option E (chi-square) tests categorical independence, not applicable to numerical features.

Target encoding (also known as mean encoding) replaces each category with the mean of the target variable for that category. This preserves the predictive signal by directly encoding the relationship between the category and the target, while reducing the dimensionality to a single continuous feature. Hash encoding can cause collisions and loss of information.

One-hot encoding creates too many dummy variables for high cardinality features. Label encoding imposes an arbitrary ordinal relationship that may not exist and can mislead models.

Creating a separate 'Missing' category preserves the missingness pattern and avoids data loss or bias from imputation for categorical features. Option A is incorrect because mean imputation is for numerical features, not categorical. Option C is incorrect because dropping all rows with missing values may discard valuable data and reduce sample size.

Option D is incorrect because mode imputation may introduce bias if missingness is not random.

The most appropriate approach. Negative quantities represent returns, which should be removed when analyzing purchase behavior for customer segmentation. Imputing missing 'CustomerID' and 'ProductID' with a placeholder like 'Unknown' retains data without guessing categorical values.

Option A is incorrect because mean imputation is not valid for categorical 'CustomerID'. Option B is incorrect because removing all rows with missing values would discard valuable data. Option C is incorrect because negative quantities are meaningful returns, not errors, and replacing them distorts the data.

The Chi-squared test of independence is used to determine if there is a significant association between two categorical variables, which is applicable here. Option A is wrong because Pearson correlation is for continuous variables. Option B is wrong because ANOVA is for comparing means across groups, but assumes continuous target.

Option D is wrong because Mutual Information can be used but is not a statistical test with a p-value.

A correlation matrix heatmap allows systematic identification of groups of highly correlated features by visually highlighting high pairwise correlations. Option B is incorrect because histograms show univariate distributions, not relationships between features. Option C is incorrect because scatter plots for each pair would be time-consuming and not systematic for 100 features.

Option D is incorrect because box plots show outliers, not correlations.