MLS-C01 Exploratory Data Analysis Practice Test 32 — 15 Questions

Question 1

A data scientist is performing exploratory data analysis on a dataset with a highly imbalanced binary target variable (1% positive). The scientist wants to understand the distribution of a numerical feature 'transaction_amount' across the two target classes. Which two visualizations are most appropriate for this analysis? (Choose two.)

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

Accepted Answer

Violin plot of 'transaction_amount' grouped by the target class.. To compare the distribution of a numerical feature across two target classes, grouped box plots and violin plots are ideal. They show summary statistics and density, respectively, allowing the scientist to see differences in median, spread, and shape between the positive and negative classes. Scatter plots, pie charts, and ungrouped histograms do not effectively convey this comparison, especially with imbalanced data.

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Scatter plot of 'transaction_amount' versus the target class.

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Pie chart of the target class distribution.

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Histogram of 'transaction_amount' for the entire dataset without grouping.

Question 2

A data scientist is exploring a credit-risk dataset where the target is binary and only 1.5% of records are positive. A colleague reports that a numeric feature has a Pearson correlation of 0.02 with the target and recommends dropping it. Before dropping it, the scientist wants a quick, paper-based check of whether the feature could still be informative. Which approach is MOST appropriate?

Accepted Answer

Compute a chi-square test between the feature and the target after binning the feature into deciles. A near-zero Pearson coefficient rules out only linear association. Binning the numeric feature into deciles and applying a chi-square test of independence against the binary target reveals threshold or non-monotonic effects, which are common in rare-event credit-risk data. Dropping on correlation alone, computing a class ratio, or relying on model gain importance all fail to test the feature's standalone association efficiently.

Answer

Replace the feature with the ratio of positive to negative records and recompute correlation

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Train a SageMaker XGBoost model and drop the feature if its gain-based importance ranks last

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Drop the feature because a Pearson correlation near zero proves it carries no information about the target

Question 3

A data scientist is exploring a dataset with a highly skewed numerical feature representing customer income. The feature has a long right tail and several extreme outliers. The scientist wants to apply a transformation that reduces skewness and makes the distribution more symmetric for subsequent modeling. Which transformation is MOST appropriate?

Accepted Answer

Logarithmic transformation (log(x + 1)). Logarithmic transformation is a variance-stabilizing technique that compresses the upper tail, reducing right skewness and mitigating the influence of outliers. It is well-suited for positive, skewed data such as income. Standardization and min-max scaling only change location and scale, not shape, while binning discards information and does not create a symmetric continuous distribution.

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Min-max scaling to [0, 1]

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Binning into equal-width bins

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Standardization (z-score normalization)

Question 4

A data scientist has a cleaned dataset stored as a single CSV in Amazon S3 and wants quick, code-free summary statistics, distributions, and correlation insights before starting feature engineering. The scientist prefers a visual interface inside SageMaker Studio over writing pandas code. Which SageMaker capability should be used?

Accepted Answer

Amazon SageMaker Data Wrangler data quality and insights report. The data quality and insights report in Data Wrangler is purpose-built for visual, code-free profiling of a dataset, producing summary statistics, distributions, missing-value analysis, and anomaly or leakage detection. Clarify, Model Monitor, and Experiments all operate around models, endpoints, or training runs, so none of them profiles a standalone CSV in the way this scenario demands.

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Amazon SageMaker Clarify explainability report

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Amazon SageMaker Experiments trial component charts

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Amazon SageMaker Model Monitor data quality monitoring schedule

Question 5

A data scientist is performing EDA on a dataset where the target is binary and one predictor is a categorical column with 1,200 distinct levels, most appearing fewer than ten times. The scientist wants a numeric representation that preserves information while keeping the feature dimensionality manageable for a downstream linear model. Which encoding approach is most appropriate?

Accepted Answer

Apply target (mean) encoding with out-of-fold computation and smoothing, mapping each level to a regularized statistic.. Out-of-fold target encoding with smoothing reduces the high-cardinality categorical column to a single numeric feature while retaining the level-to-target relationship. Computing the statistic out of fold prevents target leakage into training rows, and smoothing shrinks rare-level values toward the global mean so that levels with very few observations do not produce wildly unstable encodings that a linear model would overfit.

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One-hot encode all 1,200 levels and rely on L1 regularization to zero out the uninformative columns.

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Hash the categorical values into 50 buckets and one-hot encode the resulting bucket identifiers.

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Use frequency encoding, replacing each level with its count in the dataset.

Question 6

A machine learning engineer is exploring a dataset with a highly skewed numerical feature 'income'. The feature has a long right tail and several extreme outliers. She wants to apply a transformation to make the distribution more symmetric before feeding it into a linear regression model. Which transformation should she apply using Amazon SageMaker Data Wrangler?

Accepted Answer

Log transformation. For a right-skewed feature with outliers, a log transformation is effective at reducing skewness and stabilizing variance. It compresses large values and expands smaller ones, making the distribution more symmetric. This helps linear models that assume normally distributed errors. Scaling methods like standard, min-max, or robust scaling only change the scale, not the shape, so they do not address skewness.

Answer

Standard scaler

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Robust scaler

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Min-max scaler

Question 7

A data analyst has a 12 GB Parquet dataset in Amazon S3 and needs to run interactive exploratory queries from an Amazon SageMaker Studio notebook. The analyst wants to query the data with SQL-like syntax, avoid loading the entire dataset into memory, and minimize cost without provisioning a persistent cluster. Which approach should the analyst use?

Accepted Answer

Register the data in the AWS Glue Data Catalog and query it with Amazon Athena directly from the notebook.. The requirements point to a serverless, SQL-capable engine that reads Parquet in place and scans only needed columns. Athena, with metadata in the AWS Glue Data Catalog, satisfies all of them: no cluster, pay-per-scan pricing, SQL syntax, and columnar pruning on Parquet. The other approaches either load everything into memory or require provisioned compute.

Answer

Load the full Parquet dataset into a pandas DataFrame in the notebook and run groupby operations.

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Provision an Amazon EMR cluster with Spark, then connect the Studio notebook to the cluster's Livy endpoint.

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Copy the dataset into an Amazon Redshift cluster and query it with the Redshift Data API.

Question 8

A data analyst is using Amazon SageMaker Data Wrangler to explore a dataset stored in Amazon S3. The analyst wants to quickly identify missing values, outliers, and the distribution of each feature before building a model. Which Data Wrangler feature should the analyst use to obtain this information in a single view?

Accepted Answer

Data quality and insights report. The data quality and insights report in SageMaker Data Wrangler is specifically designed to generate a comprehensive EDA summary, including missing values, outliers, and feature distributions, in one view. Other options are either for bias detection, post-modeling evaluation, or feature importance, none of which provide the requested broad data overview.

Answer

Bias report

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Feature importance plot

Answer

Confusion matrix

Question 9

A data engineer is performing EDA on a 300 GB Parquet dataset in Amazon S3 and wants to run interactive SQL from a SageMaker Studio notebook, iterating quickly on queries while other teams also query the same data. The engineer wants results returned to the notebook as a DataFrame and wants to avoid moving or copying the data. Which solution best fits?

Accepted Answer

Use the Athena JDBC driver from the notebook to submit SQL, then read the result set into a pandas DataFrame.. Athena reads Parquet directly from S3 with no data movement and exposes a JDBC endpoint that notebooks can query, returning paginated results that load cleanly into pandas. Columnar Parquet plus partition pruning limits scanned bytes, which keeps interactive queries fast and cheap, and because the service is serverless and shared, multiple teams can query the same catalog tables concurrently without provisioning anything.

Answer

Start an Amazon EMR cluster, run Spark SQL, and write results back to S3 for the notebook to read.

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Create an Amazon Redshift cluster, load the Parquet files with COPY, and connect from the notebook.

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Run aws s3 sync to copy the Parquet files into the notebook's Amazon EBS volume, then query with pandas.

Question 10

A data scientist is exploring a dataset of customer transactions stored in Amazon S3 as Parquet files. The dataset contains a numerical feature 'transaction_amount' with many outliers, and the scientist wants a robust measure of central tendency and a robust measure of spread that are not influenced by extreme values. Which pair of statistics should the scientist compute?

Accepted Answer

Median and interquartile range (IQR). The median and interquartile range are robust statistics because they rely on the order of values rather than the actual magnitudes, so extreme transaction amounts do not distort them. In exploratory data analysis, when a numerical feature has outliers, these measures give a more representative summary than mean and standard deviation or variance.

Answer

Mean and variance

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Mean and standard deviation

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Mode and range

Question 11

A data scientist is exploring a marketing dataset in Amazon SageMaker Studio and wants to identify which of 60 numeric features are most strongly associated with a binary conversion label, while ignoring any pair of features that are nearly redundant with each other. The dataset fits comfortably in memory. Which combination of techniques is most appropriate for this exploratory step?

Accepted Answer

Compute point-biserial or mutual-information scores between each feature and the binary label, then drop one feature from each pair whose absolute Pearson correlation exceeds a threshold.. Because the label is binary and the predictors are continuous, point-biserial correlation or mutual information is the appropriate association measure, with mutual information adding sensitivity to nonlinear relationships. Pairwise correlation filtering then removes near-duplicate features so the importance ranking reflects distinct signals. Regression, clustering, and PCA each mismatch the scenario by assuming the wrong target type or by ignoring the label entirely.

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Fit a linear regression on the label and inspect the p-values of each coefficient.

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Apply principal component analysis and select the components with the largest explained variance as the most predictive features.

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Run k-means clustering with k equal to the number of features and inspect cluster centroids to rank feature importance.

Question 12

A data scientist is analyzing a dataset with missing values in several columns. She wants to visualize the pattern of missingness to decide on an imputation strategy. Which visualization should she create using Amazon SageMaker Data Wrangler?

Accepted Answer

Missing value heatmap. SageMaker Data Wrangler includes a 'Missing Value' analysis that produces a heatmap-like visualization, showing which cells are missing. This helps identify patterns such as missingness correlated across columns or rows. Understanding these patterns informs whether to use simple imputation, model-based imputation, or drop missing data. Other visualizations like histograms, scatter plots, and box plots do not depict missingness.

Answer

Scatter plot

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Box plot

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Histogram

Question 13

A data analyst has a 5 GB CSV dataset in Amazon S3 and wants to visualize feature distributions, missing-value patterns, and pairwise scatter plots during EDA without writing code. She has no existing AWS Glue Data Catalog tables. Which AWS service should she use?

Accepted Answer

Amazon SageMaker Data Wrangler, importing the CSV directly from S3 into a data flow and using its built-in analysis visualizations.. Data Wrangler is purpose-built for no-code exploratory analysis: it imports CSV directly from S3 without a Data Catalog dependency, and its analysis view renders histograms and a missing-value heatmap. The other services either require schema registration first, are dashboard-oriented, or add a separate profiling job lifecycle, so they do not match the direct, visual, code-free requirement.

Answer

Amazon Athena, creating an external table over the S3 prefix and running SELECT queries to inspect distributions.

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AWS Glue crawler plus the Glue DataBrew console, running a profile job on the crawled table.

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Amazon QuickSight, connecting to the S3 bucket as a data source and building analysis dashboards.

Question 14

A data scientist is performing EDA on a dataset with missing values in several numerical features. They want to visualize the pattern of missingness across features to decide on an imputation strategy. Which plot is most appropriate?

Accepted Answer

A missingno matrix plot. A missingno matrix plot is specifically designed to visualize missing data patterns. It provides a clear, binary representation of missingness across features and observations, enabling data scientists to identify whether missing values are random or systematic, which informs imputation strategies. Other plots either ignore missing values or do not focus on them.

Answer

A pair plot

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A box plot

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A correlation heatmap

Question 15

A data scientist is exploring a dataset of 80,000 customer records in Amazon SageMaker Data Wrangler. The 'region' column has 6 categories, the 'plan_type' column has 3 categories, and the target 'churned' is binary. The scientist wants to visually determine whether churn rate varies across combinations of region and plan_type before engineering interaction features. Which Data Wrangler analysis best supports this?

Accepted Answer

A Categorical Target Distribution analysis with churned as the target and both region and plan_type as grouping columns.. The scientist needs churn rate broken out by the cross of two categorical features. Data Wrangler's Categorical Target Distribution analysis takes a target and grouping columns and reports the target's distribution within each group, so using churned with region and plan_type surfaces the two-way interaction pattern. Histograms, scatter plots, and column summaries do not cross-tabulate a binary target by two categoricals.

Answer

A Scatter Plot analysis with region on the x-axis and plan_type on the y-axis.

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A Histogram analysis on the churned column, grouped by plan_type.

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A Multicolumn Summary analysis across region, plan_type, and churned.