MLS-C01 Exploratory Data Analysis Practice Test 33 — 15 Questions

Question 1

A data engineer must run exploratory analysis on a 2 TB dataset of JSON event logs in Amazon S3. The logs are newline-delimited JSON with inconsistent optional fields, and the engineer needs ad hoc SQL queries plus automatic schema discovery without managing any servers. Which solution meets these requirements with the least operational effort?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Create an AWS Glue crawler to populate the Data Catalog, then query the logs with Amazon Athena.. A Glue crawler plus Athena is the minimal-effort combination because both are serverless. The crawler handles schema inference and partition registration for semi-structured JSON, and Athena queries the catalog tables in place without copying data. EMR and Redshift add cluster management or data loading, while the Parquet conversion job adds an ETL pipeline that is unnecessary for exploratory querying.

Answer

Use AWS Glue Studio to build an ETL job that writes the logs to Parquet, then query with Amazon Athena.

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Launch an Amazon EMR cluster with Hive, define an external table over the S3 prefix, and query with HiveQL.

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Load the JSON logs into Amazon Redshift with COPY and query them with Redshift SQL.

Question 2

A machine learning engineer is preparing a dataset for a binary classification model. The dataset contains 50 numeric features and one binary target. During exploratory data analysis, the engineer wants to identify which features are most strongly associated with the target, but suspects that some features have non-linear relationships with the target and that a few features are highly correlated with each other. Which approach is most appropriate to quantify feature-target associations while accounting for non-linearity and also detect multicollinearity?

Accepted Answer

Compute the mutual information between each feature and the target, and compute the variance inflation factor (VIF) for each feature.. Mutual information is a non-parametric measure that captures any dependency between a feature and the target, including non-linear patterns, making it ideal for the stated suspicion. Variance inflation factor quantifies multicollinearity by measuring how much the variance of a coefficient is inflated due to correlation with other features. Using both together provides a comprehensive EDA view without needing to train a model.

Answer

Compute the Pearson correlation coefficient between each feature and the target, and compute the variance inflation factor (VIF) for each feature.

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Train a random forest classifier and extract feature importances, then compute pairwise Pearson correlations among all features to detect multicollinearity.

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Perform principal component analysis (PCA) on the features and retain components with eigenvalues greater than 1, then interpret the loadings to identify important features.

Question 3

A data scientist is exploring a SageMaker Studio tabular dataset held in Amazon S3 and wants a quick, no-code way to see per-column summary statistics, inferred data types, and the percentage of missing values before building features. The team has no ETL pipeline yet and wants the fastest path to a visual profile. Which approach BEST meets these requirements with the least effort?

Accepted Answer

Run Amazon SageMaker Data Wrangler and use its Data Quality and Insights Report to profile the columns.. A no-code profiling report that infers data types, computes per-column statistics, and quantifies missing values is exactly what the Data Quality and Insights Report in SageMaker Data Wrangler provides from within Studio. It imports S3 data directly and produces a visual summary without custom ETL or SQL, matching the low-effort requirement. Storage-level metrics and manual query tools do not inspect column contents.

Answer

Write an AWS Glue ETL job with a DynamicFrame that calls the Glue DataBrew profile API, then load the results into Amazon Athena.

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Enable S3 Storage Lens advanced metrics on the bucket and export the metrics to Amazon QuickSight for column-level analysis.

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Use Amazon Athena to run SELECT COUNT(*) and GROUP BY queries against the S3 data and read the result grid.

Question 4

A data scientist is doing EDA on a binary classification dataset with a 98:2 class imbalance. During EDA she computes summary statistics and notices the minority class has far fewer rows in the training split than in the raw data. She wants to verify whether the class ratio was preserved when the dataset was split. Which approach is most appropriate?

Accepted Answer

Compute the class distribution on the full dataset and on each split, then compare the minority-class proportion across the training, validation, and test splits.. Verifying stratification means directly measuring the class proportion in each partition against the full dataset. Comparing minority-class percentages across the raw data and each split shows immediately whether the ratio was preserved or skewed. Model retraining, resampling, and feature correlation all describe other properties and cannot confirm that the split maintained the class balance.

Answer

Increase the number of epochs and re-evaluate the model's F1 score on the validation set to see whether performance stabilizes.

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Compute the correlation matrix of all features and check for features that correlate strongly with the target.

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Apply SMOTE to the training split and compare the resulting model's ROC-AUC with the baseline.

Question 5

A data scientist is performing EDA on a dataset with a highly skewed numerical feature. They want to apply a transformation to make the distribution more symmetric before modeling. Which transformation is most appropriate?

Accepted Answer

Log transformation. A log transformation is the most appropriate for reducing high right skew because it compresses large values more than small ones, making the distribution more symmetric. While Box-Cox is more flexible, it requires tuning and positive data; log is simpler and often effective. Square root is too mild, and standardization does not alter distribution shape.

Answer

Square root transformation

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Box-Cox transformation

Answer

Standardization

Question 6

A data scientist is performing exploratory data analysis on a SageMaker Studio notebook with a housing dataset. Several continuous features are heavily right-skewed, the target price column has a long tail, and a subset of rows contains missing values in multiple columns. The scientist wants to assess relationships between features and the target while preparing the data for a linear model. Which two actions are most appropriate? (Choose two.)

Accepted Answer

Visualize pairwise relationships with a scatterplot matrix and inspect the correlation matrix to detect nonlinearity and multicollinearity.. Transforming skewed features and the target makes correlation and linear coefficients reflect genuine relationships rather than being driven by extreme values, and visual plus correlation-based inspection exposes nonlinearity and redundancy that summary numbers conceal. Dropping incomplete rows, standardizing before correlation, and silent mean imputation either discard information, change nothing, or distort the statistics being examined.

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Drop every row containing any missing value before computing any statistics.

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Standardize all features to zero mean and unit variance before computing the correlation matrix.

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Replace all missing values with the column mean and proceed without recording which values were imputed.

Question 7

A data scientist is profiling a 200-column tabular dataset in Amazon SageMaker Data Wrangler before training. Several numeric columns contain extreme values, one column has 60% missing entries, and the target is continuous. The scientist wants to use Data Wrangler's built-in analyses to identify data-quality problems that could bias the model, without writing custom code. (Choose two.)

Accepted Answer

Run a Multicolumn Summary analysis to review per-column statistics such as missing counts, min, max, and quantiles across the 200 columns.. Two built-in analyses cover the stated problems. Multicolumn Summary reports per-column statistics, including missing counts and distributional extremes, so it finds both the 60% missing column and the outlier-heavy columns. Target Leakage compares each feature against the continuous target and flags suspiciously predictive columns that would bias evaluation. Quick Model, scatter plots, and target-only histograms do not report missingness or per-column outlier magnitude.

Answer

Run a Scatter Plot analysis on the continuous target to identify which numeric columns have extreme values.

Answer

Run a Quick Model analysis and use the reported feature impact to detect which columns contain missing values.

Answer

Run a Histogram analysis on the target column only, since target distribution determines whether feature outliers matter.

Question 8

A data scientist is doing EDA on a SageMaker notebook against a large Parquet dataset in S3 and notices that a numeric feature has a highly skewed distribution with a long right tail. The team wants to quantify the asymmetry and reduce its influence before training a linear model. (Choose two.)

Accepted Answer

Compute the skewness statistic on the feature and compare it to a threshold such as an absolute value greater than 1.. Quantifying asymmetry with the skewness statistic gives an objective basis for action, and applying a logarithmic or Box-Cox transformation compresses the long right tail to make the feature more symmetric. Together they measure the problem and correct it before linear modeling. Standardization, median imputation, and one-hot encoding change scale, fill gaps, or restructure the variable without altering its skewed shape.

Answer

One-hot encode the numeric feature into binary indicator columns for each observed value.

Answer

Standardize the feature to zero mean and unit variance using the training set statistics.

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Impute missing values in the feature with the column median before measuring its distribution.

Question 9

A data scientist notices that one numerical feature in a SageMaker training dataset is heavily right-skewed and contains a small number of extreme high values. Before modeling, the scientist wants to reduce the influence of those extremes while preserving relative ordering of observations. Which transformation should the scientist apply to this feature?

Accepted Answer

Apply a logarithmic transformation to the feature values.. The scenario requires reducing the influence of extreme values in a right-skewed continuous feature while keeping relative order. A logarithmic transformation is monotonic and compresses the upper tail most strongly, which directly lessens the leverage of large observations without changing how observations rank against each other. Scaling approaches only change units, and encoding approaches are meant for categorical data, so neither addresses skew or outlier influence.

Answer

Apply one-hot encoding to split the feature into binary indicator columns.

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Apply min-max scaling to rescale the feature into the range zero to one.

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Apply z-score standardization to center the feature at zero with unit variance.

Question 10

A data scientist is performing EDA on a dataset with 50 features and 10,000 observations. They suspect multicollinearity among some features. Which method is most appropriate to detect and quantify multicollinearity?

Accepted Answer

Variance Inflation Factor (VIF). VIF is the most appropriate method because it provides feature-specific quantification of multicollinearity, detecting both pairwise and multivariate collinearity. It is widely used and interpretable, with clear thresholds. Other methods either only detect pairwise relationships or do not directly quantify collinearity for individual features.

Answer

Condition number

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Correlation matrix

Answer

Principal Component Analysis (PCA)

Question 11

A data scientist is exploring a dataset where two continuous features have a strong monotonic but clearly nonlinear relationship. Pearson correlation between them is near zero, yet the scatter plot shows a consistent increasing curve. The scientist wants a single numeric summary that quantifies the strength of this monotonic association. Which statistic should the scientist compute?

Accepted Answer

Spearman rank correlation coefficient between the two features.. A near-zero Pearson correlation combined with an obvious increasing curve signals a monotonic but nonlinear relationship. Rank-based correlation measures association through ordering rather than linear fit, so it captures the curve as a strong positive value. The other statistics either measure only linear relationships or are designed for categorical data, making them unsuitable for summarizing the monotonic association the scientist observed.

Answer

Chi-square test statistic computed on binned versions of the two features.

Answer

Pearson correlation coefficient computed on the raw feature values.

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Covariance between the two features after subtracting their means.

Question 12

A machine learning engineer is exploring a dataset with 50 numerical features and 10,000 observations. The engineer suspects that several features are highly collinear and wants to identify which features are redundant before training a linear model. The engineer decides to compute the Variance Inflation Factor (VIF) for each feature. After computing VIF, the engineer finds that one feature has a VIF of 12.5. What should the engineer do next based on standard EDA practices?

Accepted Answer

Remove the feature with the highest VIF and recompute VIF for the remaining features iteratively.. When VIF exceeds 10, the feature is highly collinear with others. The recommended action is to remove the feature with the highest VIF and recompute VIFs for the remaining features iteratively, because removing one feature can reduce VIF for others. This process helps achieve a set of features with acceptable multicollinearity, leading to more stable linear models.

Answer

Apply Principal Component Analysis (PCA) to all features and then compute VIF on the principal components.

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Keep the feature because a VIF of 12.5 is within the acceptable range for datasets with more than 10 features.

Answer

Standardize the feature with high VIF and then recompute VIF, as scaling resolves multicollinearity.

Question 13

A data scientist is performing EDA on a dataset with a categorical feature that has high cardinality (e.g., zip codes). They want to encode it for a machine learning model. Which two encoding techniques are most appropriate to reduce dimensionality while preserving information? (Choose two.)

Accepted Answer

Hashing encoding. Target encoding and hashing encoding are both effective for high-cardinality categorical features. Target encoding reduces dimensionality by using target statistics, capturing predictive information, while hashing encoding maps categories to a fixed lower-dimensional space, handling new categories and reducing memory. One-hot and label encoding do not reduce dimensionality effectively, and frequency encoding may lose predictive signal.

Answer

Frequency encoding

Answer

Label encoding

Answer

One-hot encoding

Question 14

A data scientist is performing exploratory data analysis on a dataset stored in Amazon S3 and wants to use Amazon SageMaker Data Wrangler to identify and handle missing values. The scientist needs to generate a report that shows the percentage of missing values per column and also wants to impute missing values using the mean for numerical features. Which two actions should the scientist take in Data Wrangler to achieve these goals? (Choose two.)

Accepted Answer

Apply the 'Handle Missing Values' transform and select the 'Impute' option with mean strategy for numerical columns.. To identify and handle missing values in SageMaker Data Wrangler, the scientist should first generate the Data Quality and Insights Report to see missing value percentages per column, and then apply the Handle Missing Values transform with the mean imputation strategy for numerical features. These two steps provide both the diagnostic view and the corrective action.

Answer

Use the 'Data Quality and Insights Report' to automatically impute missing values with the mean.

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Use the 'Data Quality and Insights Report' to generate a histogram of missing values.

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Apply the 'Handle Missing Values' transform and select the 'Drop' option for all columns.

Question 15

A data scientist is exploring a dataset in Amazon SageMaker Studio and wants to quickly detect which columns contain missing values and how missingness relates to other columns, all within the notebook. Which SageMaker Studio capability should she use?

Accepted Answer

Open the Data Wrangler data flow and run the Data Quality and Insights report, which surfaces missing-value counts and anomalies.. The Data Wrangler Data Quality and Insights report is the Studio-native capability that inventories missing values per column and highlights related data-quality issues such as anomalies and imbalance. It produces a readable report inside the notebook environment without requiring a training job or model, unlike Debugger, Clarify, and Autopilot, which serve training diagnostics, bias analysis, and model automation respectively.

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Use the Studio debugger profiler to inspect the training job's tensor distributions for missing entries.

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Run a SageMaker Clarify bias report on the dataset to identify columns with missing values.

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Use the Studio Autopilot job to generate candidate models and infer missing columns from model feature importance.