MLS-C01 Exploratory Data Analysis Practice Test 34 — 15 Questions

Question 1

A data engineer is performing EDA on a dataset stored in Amazon S3 where one categorical column has thousands of distinct values. Summary statistics show the column is heavily imbalanced, with a few categories covering most rows and a long tail of rare categories. Before feature engineering, the engineer wants a quick visual and statistical summary of the category frequencies without writing custom code. Which approach is MOST suitable?

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

Accepted Answer

Generate a Data Quality and Insights Report in SageMaker Data Wrangler to inspect the column's category distribution and imbalance.. The Data Quality and Insights Report profiles categorical columns by reporting distinct values and frequency distributions, which directly exposes the heavy imbalance and long tail described. It runs from SageMaker Data Wrangler with no custom code, matching the engineer's requirement. Crawlers, access logs, and CloudWatch Logs Insights describe schema or operational activity rather than the internal value distribution of a column.

Answer

Use Amazon CloudWatch Logs Insights to query the category frequencies from the S3 objects.

Answer

Create an AWS Glue crawler and rely on the generated Data Catalog table schema to reveal category frequencies.

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Enable Amazon S3 server access logging and analyze the request patterns for the objects.

Question 2

A data scientist is analyzing a dataset of customer churn with a binary target variable. The dataset contains 20 features, including both numerical and categorical variables. The scientist wants to identify which features have the strongest relationship with the target and also detect potential non-linear associations. The scientist decides to compute the mutual information between each feature and the target. After computing mutual information scores, the scientist finds that a categorical feature with 50 unique categories has the highest score. What is the most appropriate next step to ensure the mutual information score is not misleading?

Accepted Answer

Compute the mutual information using cross-validation or a holdout set to account for overfitting due to high cardinality.. Mutual information scores can be inflated for features with many unique values because the estimator can fit noise. To obtain a reliable estimate, the scientist should compute mutual information using cross-validation or a holdout set. This method evaluates the score on unseen data, mitigating overfitting and providing a more accurate assessment of the feature's true relationship with the target.

Answer

Discretize the categorical feature into fewer bins to reduce the number of unique values before computing mutual information.

Answer

Apply one-hot encoding to the categorical feature and then recompute mutual information.

Answer

Use the chi-square test instead, as it is not affected by high cardinality.

Question 3

A data scientist is analyzing a binary classification dataset and observes that one feature has a highly skewed distribution with several extreme values. The scientist wants a visualization that shows how the central tendency and spread of this feature differ between the two classes without being dominated by the extreme observations. Which visualization is most appropriate?

Accepted Answer

A pair of box plots of the feature, one for each class.. The requirement is a per-class comparison of central tendency and spread that resists distortion from extreme values. Box plots summarize each class using the median and interquartile range, which are not pulled by outliers, and placing the two boxes side by side makes the differences easy to read. Alternative charts either lose the per-class distributional detail or are dominated by the skewed tail.

Answer

A pie chart of the class proportions in the dataset.

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A scatter plot of the feature against the row index.

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A single histogram of the feature with all classes overlaid.

Question 4

A data scientist is analyzing a dataset with two numerical features, 'age' and 'income', and suspects a non-linear relationship. The scientist computes the Pearson correlation coefficient and finds it to be near zero. However, a scatter plot reveals a clear U-shaped pattern. Which statement best explains this discrepancy?

Accepted Answer

Pearson correlation only detects linear relationships, so a non-linear pattern can result in a near-zero coefficient.. Pearson correlation quantifies linear association only. When a relationship is non-linear, such as U-shaped, the linear correlation can be near zero even though the variables are strongly dependent. The scatter plot provides a more complete view. Therefore, the discrepancy is explained by the limitation of Pearson correlation to detect non-linear patterns.

Answer

The scatter plot is misleading because it suggests a relationship that the correlation coefficient correctly refutes.

Answer

The Pearson correlation coefficient is incorrectly computed because it requires normally distributed variables.

Answer

Pearson correlation is robust to outliers, so the near-zero value must be due to a calculation error.

Question 5

A machine learning specialist is performing exploratory data analysis on a dataset with a continuous target variable. The specialist suspects that the relationship between a key numerical feature and the target is not linear but monotonic. The dataset also contains several categorical features. Which visualization technique is most appropriate to assess the monotonic relationship between the numerical feature and the target while also allowing for the inspection of potential interactions with a categorical feature?

Accepted Answer

A line plot of the mean target value across bins of the numerical feature, with separate lines for each level of the categorical feature.. Binning the numerical feature and plotting the mean target per bin, with separate lines for each category, directly visualizes the functional form of the relationship. Monotonicity is evident if the mean target consistently increases or decreases across bins. Separate lines reveal whether the relationship differs by category, indicating interactions. This method is robust to noise and clearly communicates trends.

Answer

A heatmap of the correlation matrix between all numerical features and the target.

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A scatter plot of the numerical feature versus the target, with points colored by the categorical feature.

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A box plot of the numerical feature grouped by the categorical feature, with the target represented by the size of the boxes.

Question 6

A data scientist has a 40 GB Parquet dataset in Amazon S3 partitioned by date. She must run interactive EDA queries that join it with a 5 GB reference table of product categories, but she wants to avoid provisioning any cluster and wants results in seconds rather than minutes. Which approach BEST meets these requirements?

Accepted Answer

Create an Amazon Athena workgroup, register the Parquet prefix and the reference table in the AWS Glue Data Catalog, and query with Athena SQL.. Athena is the serverless query service that uses the AWS Glue Data Catalog to query S3 data in place, supports Parquet columnar pruning and partition elimination, and can join the partitioned fact data with the smaller reference table. It requires no cluster provisioning and returns interactive results, matching every stated constraint.

Answer

Load both datasets into Amazon DynamoDB tables and run PartiQL queries from the console for the joins.

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Launch an Amazon EMR cluster with Apache Spark, copy both datasets to HDFS, and run Spark SQL joins interactively.

Answer

Use AWS Glue crawlers and then query the data through Amazon Redshift Spectrum on an existing ra3 cluster.

Question 7

A data scientist is performing exploratory data analysis on a dataset stored in Amazon S3 and wants to detect and understand missing values before training a model. Which two actions are appropriate for this goal? (Choose two.)

Accepted Answer

Generate a data quality report with Amazon SageMaker Data Wrangler that summarizes missing-value counts and percentages per column.. Detecting and understanding missing values requires first quantifying how much is missing per column and then examining the pattern of missingness across rows and features. A data quality report provides the counts and percentages, while a missingness pattern view reveals whether the gaps are random or systematic. Premature imputation or row deletion erases that information, and type conversion for empty strings corrupts the data.

Answer

Delete every row that contains at least one missing value from the dataset immediately.

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Replace every missing value with zero across all columns before any inspection.

Answer

Convert all columns to the string data type so missing entries can be represented as empty strings.

Question 8

A data scientist is examining a SageMaker training dataset and suspects that a feature's missing values are not random but depend on the target class. They want to determine whether the missingness itself carries predictive signal before deciding how to impute. Which EDA step BEST supports this decision?

Accepted Answer

Segment the data by the missing-value indicator and compare the target distribution and other feature statistics across the two groups.. Comparing target rates and feature statistics between rows where the value is missing and rows where it is present tests whether missingness is informative. If the two groups differ, the missing-value indicator should be retained as a feature and imputation chosen accordingly. Mean imputation, listwise deletion, and KNN imputation all mask or remove the pattern rather than measuring it.

Answer

Apply K-nearest neighbors imputation using only the features with the highest variance and retrain the model.

Answer

Replace all missing values with the column mean and then compute the correlation matrix of the imputed features.

Answer

Drop every row containing a missing value and then profile the remaining complete cases.

Question 9

A data analyst is exploring a dataset containing information about customer orders. The dataset includes a column 'order_date' with timestamps and a column 'order_total' with numerical values. The analyst wants to visualize how the average order total changes over time to identify trends and seasonality. Which type of plot is most appropriate for this exploratory analysis?

Accepted Answer

A line plot with time on the x-axis and average order total on the y-axis.. A line plot with time on the x-axis and average order total on the y-axis is the most appropriate because it aggregates data over time and connects points to reveal trends and seasonality. It provides a clear visual of how the average changes continuously, making it easy to spot patterns. Other plots either do not aggregate, lack the time dimension, or treat time as discrete.

Answer

A box plot of order_total grouped by month.

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A histogram of order_total values.

Answer

A scatter plot with order_date on the x-axis and order_total on the y-axis.

Question 10

A data scientist is performing exploratory data analysis on a large dataset stored in Amazon S3. The dataset contains missing values in several columns. The scientist wants to understand the pattern of missingness to decide on an imputation strategy. Which two techniques are appropriate for identifying and visualizing missing data patterns? (Choose two.)

Accepted Answer

Create a missingno matrix plot to visualize the location of missing values across the dataset.. A missingno matrix plot visualizes the exact locations of missing values, making patterns like block missingness immediately apparent. Computing pairwise correlations of missingness indicators quantifies whether missing values in different columns tend to occur together, revealing systematic relationships. Together, they provide both visual and quantitative insights into missing data patterns, which are essential for choosing an imputation strategy.

Answer

Train a generative adversarial network (GAN) to impute missing values and then analyze the imputed values.

Answer

Perform a t-test comparing the distribution of a numerical feature for rows with and without missing values in another column.

Answer

Use a bar chart of the mean values of each column to infer missing data patterns.

Question 11

A data scientist is exploring a dataset with a categorical feature 'city' that has 100 unique values. Before encoding, the scientist wants to reduce the number of categories by grouping rare cities into an 'Other' category. Which approach should the scientist use to determine which cities are considered rare?

Accepted Answer

Group cities that appear in less than a certain percentage of the total rows, such as 1%.. To reduce high cardinality, the scientist should group categories that are infrequent. A common rule is to combine categories that appear in less than a small percentage of the data, such as 1%, into an 'Other' category. This reduces the number of unique values while preserving the most frequent categories, which are likely more informative and stable.

Answer

Group cities that have the lowest mean target value.

Answer

Group cities that have the highest variance in the target variable.

Answer

Group cities that are not present in the validation set.

Question 12

A data scientist is performing exploratory data analysis on a dataset stored in Amazon S3. The dataset contains sensitive customer information and must remain encrypted at rest with a customer-managed AWS KMS key. The scientist needs to run a Jupyter notebook in Amazon SageMaker Studio to compute summary statistics and generate visualizations without downloading the data. Which approach allows the scientist to access the data securely while meeting the encryption requirement?

Accepted Answer

Attach an IAM role to the SageMaker Studio notebook instance that grants s3:GetObject and kms:Decrypt permissions for the specific S3 bucket and KMS key, then read the data directly in the notebook using the AWS SDK for Python (Boto3).. The correct approach is to attach an IAM role to the SageMaker Studio notebook instance with permissions for s3:GetObject and kms:Decrypt, then use Boto3 to read the data directly. This ensures the data remains encrypted at rest and is decrypted only in memory during analysis, meeting the security requirement. Other options either fail to handle KMS permissions correctly or introduce unnecessary steps.

Answer

Use Amazon Athena from within the SageMaker Studio notebook to query the S3 data. Athena automatically handles KMS decryption and returns results as a pandas DataFrame without additional permissions.

Answer

Copy the dataset to an Amazon EBS volume attached to the SageMaker Studio notebook instance, then decrypt it using the KMS key. The EBS volume inherits the same encryption as the S3 bucket.

Answer

Use SageMaker Studio's built-in integration with AWS Glue DataBrew to read the S3 data directly, because DataBrew automatically decrypts KMS-encrypted objects.

Question 13

A data scientist examines a 20-feature dataset and finds that two features have a Pearson correlation of 0.98, but a scatter plot shows a clear curved (nonlinear) relationship and the features are heavily right-skewed. The scientist wants a single numeric summary that captures monotonic association and is robust to the skew. Which statistic should be reported?

Accepted Answer

Spearman's rank correlation coefficient between the two features.. Spearman's rank correlation is the appropriate summary because it ranks the data before correlating, making it robust to right skew and capable of detecting any monotonic, including nonlinear, association. It yields one interpretable number between -1 and 1 that aligns with the curved scatter plot the scientist observed.

Answer

The Pearson correlation coefficient computed on the raw skewed values.

Answer

The covariance between the two features after mean-centering only.

Answer

The coefficient of determination (R-squared) from a simple linear regression of one feature on the other.

Question 14

A data scientist is running exploratory data analysis on a 12 GB Parquet dataset stored in Amazon S3. They need low-latency, interactive querying from a SageMaker Studio notebook to compute summary statistics and join a small reference table, without loading the full dataset into memory. The dataset is already cataloged in the AWS Glue Data Catalog. Which approach should the scientist use?

Accepted Answer

Run an Amazon Athena query against the AWS Glue Data Catalog table and read the result set into the notebook with the Athena JDBC driver.. Querying the S3 data in place through Athena with the Glue Data Catalog keeps the heavy scan server-side and returns only the small aggregated result to the notebook, which is exactly what low-latency interactive EDA over a large Parquet dataset requires. The other approaches either pull the whole dataset into memory, add cluster provisioning delay, or misuse a serving-oriented feature store.

Answer

Create an Amazon EMR cluster with a Hue interface and run Apache Hive queries, then export the results back to the notebook through S3.

Answer

Register the dataset as an Amazon SageMaker Feature Store feature group and read it back with the SageMaker SDK get_record API for each row.

Answer

Download all Parquet objects to the notebook's attached Amazon EBS volume with the AWS CLI, then load them into a pandas DataFrame.

Question 15

A data scientist is preparing a dataset for a binary classification model. During EDA, they notice that one numerical feature has a highly skewed distribution with a long right tail. They decide to apply a transformation to make the distribution more symmetric. Which transformation is MOST appropriate for reducing right skewness?

Accepted Answer

Logarithmic transformation. A logarithmic transformation is the most effective for reducing strong right skewness because it compresses large values more than small ones, pulling the tail in and making the distribution more symmetric. Standardization and min-max normalization do not alter skewness, and square root is weaker than log for highly skewed data.

Answer

Min-max normalization

Answer

Standardization (z-score normalization)

Answer

Square root transformation