MLS-C01 Exploratory Data Analysis Practice Test 23 — 15 Questions

Question 1

A data scientist is analyzing a dataset with 100,000 observations and 50 features. The scientist uses a Jupyter notebook on Amazon SageMaker. During EDA, the scientist runs a command to check for missing values and notices that 20% of the data in one feature is missing. The missing values are not random; they are correlated with another feature. Which imputation method is MOST appropriate?

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

Accepted Answer

Multiple imputation by chained equations (MICE). MICE uses multiple imputation based on other features, accounting for the correlation between the missing feature and another feature. Option A is wrong because median imputation ignores the correlation and simply fills with the median, which does not leverage relationships between features. Option B is wrong because listwise deletion removes rows with missing data, which reduces sample size and can introduce bias if missingness is not completely random. Option C is wrong because mean imputation, like median imputation, ignores correlations and can distort relationships.

Answer

Median imputation

Answer

Listwise deletion (remove rows with missing values)

Answer

Mean imputation

Question 2

A data scientist is performing EDA on a dataset with 1,000 features and 10,000 rows. The target is binary. The scientist wants to reduce dimensionality while preserving information related to the target. Which TWO methods are appropriate?

Accepted Answer

L1-regularized logistic regression. L1-regularized logistic regression (C) is correct because the L1 penalty drives many of the 1,000 coefficients to exactly zero, performing embedded feature selection that keeps only the features contributing to predicting the binary target. Mutual information-based feature selection (D) is correct because it directly scores each feature by its statistical dependence on the binary target, allowing the scientist to retain the most target-relevant features while discarding the rest. PCA (A) is unsupervised and maximizes variance rather than target-related information, so it can discard directions that matter for classification. Autoencoders (B) are also unsupervised compression methods that optimize reconstruction, not target preservation. t-SNE (E) is a nonlinear visualization technique for embedding into 2-3 dimensions and is not a feature-selection or dimensionality-reduction method suitable for preserving target information.

Answer

Principal Component Analysis (PCA)

Answer

Autoencoders

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Question 3

A data scientist is exploring a dataset with mixed data types (numeric, categorical, text). The dataset has 5 million rows. The scientist wants to understand the relationships between variables and identify potential data quality issues. Which THREE tools are suitable for this analysis?

Accepted Answer

AWS Glue DataBrew. AWS Glue DataBrew (A) is correct because it is a visual data preparation tool that provides over 250 pre-built transformations and profiling features to detect data quality issues like missing values, duplicates, and outliers across mixed data types without writing code. Amazon SageMaker Data Wrangler (C) is correct because it offers a visual interface for data exploration, with built-in analysis templates (e.g., scatter plots, histograms, correlation matrices) and a Data Quality and Insights report that surfaces anomalies and target leakage on large datasets. Amazon Athena (D) is correct because it lets the scientist run ad-hoc SQL queries directly against data in Amazon S3, enabling aggregation, joins, and statistical summaries to explore relationships among numeric, categorical, and text columns at scale. AWS Data Pipeline (B) is not suitable because it is a workflow orchestration service for moving and transforming data on schedules, not an interactive exploration or profiling tool. Amazon Kinesis Data Analytics (E) is not suitable because it is designed for real-time stream processing with SQL or Apache Flink, not for exploratory analysis of a static 5-million-row dataset.

Answer

AWS Data Pipeline

Answer

Amazon Kinesis Data Analytics

Question 4

A data scientist is working with a dataset that contains geolocation coordinates (latitude and longitude) and timestamps. The scientist wants to visualize the data to check for spatial and temporal patterns. Which TWO AWS services can be used for this visualization?

Accepted Answer

Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler (option B) is correct because it provides built-in data visualization and analysis capabilities, including scatter plots and histograms, that let a data scientist explore spatial (latitude/longitude) and temporal (timestamp) patterns during data preparation. Amazon QuickSight (option D) is correct because it is a BI visualization service that supports geospatial charts (e.g., point maps using latitude/longitude) and time-series visuals with date fields, making it suitable for examining spatial and temporal trends. Amazon Comprehend (option A) is a natural-language-processing service for text, not geospatial or temporal visualization. Amazon Rekognition (option C) performs image and video analysis, which is unrelated to coordinate/timestamp data. AWS Glue (option E) is a serverless ETL and data catalog service; it can prepare data but does not itself provide the requested visualization.

Answer

Amazon Comprehend

Answer

Amazon Rekognition

Answer

AWS Glue

Question 5

An ML engineer runs the AWS CLI command above to list files in a training data bucket. The engineer notices that the three CSV files have different sizes but the same number of columns. What is the MOST likely cause of the size variation?

Accepted Answer

The files contain a different number of rows.. When CSV files share the same schema (same number of columns) but differ in byte size, the most likely explanation is that they contain different numbers of rows. File size scales primarily with row count and the length of values in each row, so a file with 10,000 rows will be larger than one with 5,000 rows even with identical columns.

Answer

The files are compressed with different algorithms.

Answer

Some files have duplicate headers.

Answer

The files have different column data types.

Question 6

A DevOps engineer runs the CloudWatch Logs Insights query shown above on the log group for an ML training job. The result shows a spike in ERROR messages at a specific hour. What should the engineer do next to identify the root cause?

Accepted Answer

Modify the query to display the actual @message for the hour with the spike.. Modifying the query to include the @message field for the specific hour with the spike allows the engineer to see the actual error messages, which is the direct way to identify the root cause. Option B is incorrect because removing the filter would show all messages, which is less targeted. Option C is incorrect because changing the bin to 5m might be too granular and not helpful without examining the messages. Option D is incorrect because increasing the limit does not help identify the cause without seeing the messages.

Answer

Remove the filter on ERROR to see all messages.

Answer

Change the bin to 5m to see more detailed spikes.

Answer

Increase the limit to 50 to see more hours.

Question 7

A data scientist runs a SQL query on an Amazon Athena table and notices that the query scans a large amount of data. Which approach would reduce the amount of data scanned without changing the SQL logic?

Accepted Answer

Partition the table on a column that is frequently used in WHERE clauses.. Partitioning an Athena table on a column frequently used in WHERE clauses allows Athena to prune partitions and read only the relevant data, drastically reducing the amount of data scanned. This is because Athena uses the partition metadata in the AWS Glue Data Catalog to skip entire partitions that do not match the query predicate. Since the SQL logic remains unchanged, the query still returns the same results but scans less data, lowering cost and improving performance.

Answer

Convert the data from CSV to JSON format.

Answer

Store the data in Parquet format without partitioning.

Answer

Use GZIP compression on the data files.

Question 8

A machine learning team is building a fraud detection model. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraudulent). Which EDA technique is most important to apply before modeling?

Accepted Answer

Create a stratified train-test split to preserve the class distribution.. With a 99.9%/0.1% class imbalance, a random train-test split can easily produce a test set with zero or very few fraud examples, making evaluation meaningless. A stratified split preserves the class ratio in both training and test sets, ensuring the minority class is represented proportionally so metrics like precision, recall, and AUC-PR are meaningful. This is the most critical EDA/preprocessing step before modeling an imbalanced fraud dataset.

Answer

Normalize all numerical features to have zero mean and unit variance.

Answer

Remove outliers from the dataset using the IQR method.

Answer

Perform correlation analysis to remove highly correlated features.

Question 9

A data scientist is exploring a dataset containing customer transaction records. The target variable is 'churn' (1 = churned, 0 = not churned). Which TWO actions should the scientist take to understand the data distribution and prepare for modeling?

Accepted Answer

Plot the frequency of the target variable to check for class imbalance.. Option C is correct because plotting the frequency of the target variable 'churn' is a standard exploratory data analysis step that reveals class imbalance, which directly affects modeling choices such as resampling, class weights, or appropriate evaluation metrics like F1-score or AUC-PR instead of accuracy. Option D is correct because checking for missing values in each column and deciding on an imputation strategy is essential data preparation; missing data can bias results or break algorithms, and the imputation method (mean, median, mode, or model-based) must be chosen before training. Option A is not appropriate at this stage because PCA is a dimensionality reduction technique typically applied after preprocessing and encoding, and it can obscure interpretability during initial exploration. Option B is premature because training a gradient boosting model to find important features is a modeling step, not an initial distribution-understanding or preparation action. Option E, while a valid preprocessing step for categorical variables, is not one of the two best actions for understanding data distribution and general preparation, and it should follow missing-value handling and target inspection.

Answer

Apply Principal Component Analysis (PCA) to reduce dimensionality.

Answer

Train a gradient boosting model to identify important features.

Answer

Convert categorical variables into one-hot encoded vectors.

Question 10

A data scientist is analyzing a large dataset of images stored in Amazon S3. The dataset is used to train a computer vision model. Which THREE EDA steps are appropriate for this image dataset?

Accepted Answer

Compute the distribution of image dimensions (height and width).. Option A is correct because computing the distribution of image height and width is a core EDA step for computer vision datasets, revealing whether images are uniform or require resizing/augmentation before training. Option B is correct because checking for corrupted or unreadable image files (e.g., truncated JPEGs or invalid PNG headers) prevents training failures and silent data loss. Option D is correct because visualizing a sample of images per class verifies label correctness and exposes mislabeled or ambiguous examples that would degrade model performance. Option C is not appropriate because decomposing timestamps for seasonality applies to time series forecasting, not static image training data. Option E is not appropriate because tokenization and stop word removal are NLP text preprocessing techniques, and image filenames carry no meaningful linguistic signal for model training.

Answer

Decompose the time series of image timestamps to detect seasonality.

Answer

Perform tokenization and stop word removal on image filenames.

Question 11

Refer to the exhibit. A data scientist runs the AWS CLI command shown and gets the output. The scientist wants to create an Athena table over all log files in the 'logs/2023/' prefix, including files smaller than 1000 bytes. Which approach achieves this?

Accepted Answer

Create the table using LOCATION 's3://my-bucket/logs/2023/' which includes all files under that prefix.. Creating an Athena table with the LOCATION pointing to 's3://my-bucket/logs/2023/' will include all objects under that prefix, regardless of size. The CLI command's --query parameter only filters the output of the list-objects command, but does not impose any restriction on the data or the bucket. Option B is incorrect because a WHERE clause in Athena can only filter rows after the table is defined; it cannot include or exclude files from being read. Option C is incorrect because S3 does not have a built-in size restriction on a bucket; the CLI command is just a client-side query. Option D is incorrect because the CLI command is independent; the table can be created directly without needing to modify the CLI command.

Answer

Create the table and add a WHERE clause to include small files.

Answer

Ask the S3 team to remove the size restriction on the bucket.

Answer

Modify the CLI command to remove the size filter and re-run it before creating the table.

Question 12

A data scientist is working with a dataset that contains both numerical and categorical features. The target variable is continuous. Which TWO EDA techniques should the scientist use to understand relationships between features and the target?

Accepted Answer

Create scatter plots of numerical features against the target variable.. Option C is correct because with a continuous target variable, scatter plots of each numerical feature against the target directly reveal the shape, direction, and strength of the relationship (linear, nonlinear, or none), which is exactly the goal of bivariate EDA. Option D is correct because box plots of the continuous target grouped by each categorical feature's categories show how the target's distribution (median, spread, outliers) shifts across groups, exposing categorical-feature/target relationships. Option A is wrong because a confusion matrix evaluates classification predictions and requires discrete class labels, not a continuous target. Option B is wrong because the silhouette score measures clustering quality/cohesion-separation, not feature-target relationships. Option E is wrong because a histogram of the target is a univariate analysis that describes the target's own distribution and does not relate any feature to the target.

Answer

Generate a confusion matrix for the target variable.

Answer

Compute the silhouette score for each feature.

Answer

Plot a histogram of the target variable.

Question 13

A data scientist is analyzing a dataset with many missing values. The scientist wants to decide on an imputation strategy. Which THREE considerations are important for choosing the imputation method?

Accepted Answer

The mechanism of missingness (MCAR, MAR, MNAR).. Option A is correct because the missingness mechanism (MCAR, MAR, or MNAR) determines whether simple imputation like mean/mode is unbiased or whether model-based/multiple imputation is required; MNAR in particular needs explicit modeling of the missingness. Option C is correct because the proportion of missing values per feature dictates feasibility: low percentages tolerate mean/median/mode imputation, while high percentages (e.g., >40-50%) may warrant dropping the feature or using iterative/multiple imputation. Option D is correct because feature distribution affects the choice of statistic: mean imputation is reasonable for roughly normal data, whereas median or model-based imputation is preferred for skewed distributions to avoid distorting the central tendency. Option B is not a primary driver of imputation method choice; class imbalance concerns modeling/resampling, not how to fill missing cells. Option E is also not a direct criterion, since random-forest feature importance is a downstream modeling output and does not itself determine an appropriate imputation strategy.

Answer

The class imbalance of the target variable.

Answer

The feature importance according to a random forest model.

Question 14

A data scientist is performing EDA on a time series dataset of daily website visits. The scientist wants to identify any seasonality patterns. Which visualization is most appropriate?

Accepted Answer

Line plot with day on x-axis and visits on y-axis, highlighting weekends.. A line plot with day on the x-axis and visits on the y-axis, with weekends highlighted, can reveal weekly seasonality patterns. Option A (correlation matrix with lags) can detect autocorrelation but is not a direct visualization of seasonality. Option B (scatter plot vs day of month) could show monthly patterns but is less effective for daily seasonality and does not preserve time order as clearly as a line plot. Option C (histogram) shows distribution, not temporal patterns. Therefore, option D is best.

Answer

Correlation matrix of visits with lagged versions of itself.

Answer

Scatter plot of visits against the day of the month.

Answer

Histogram of daily visit counts.

Question 15

Refer to the exhibit. A data scientist queries the table with 'SELECT COUNT(*) FROM mytable' in Athena and gets a result of 1000 rows. However, the scientist knows there are 1500 data files in the S3 location. What is the most likely reason for the discrepancy?

Accepted Answer

Some files may be empty or contain only headers, so they contribute 0 rows.. Athena's COUNT(*) returns the number of rows actually parsed from the underlying data, not the number of files in S3. Empty files or files containing only a header row contribute zero data rows to the result set, so 500 of the 1500 files could easily be empty or header-only while still being counted as objects in the S3 prefix. This is the most common benign explanation for a row-count shortfall versus file-count expectations.

Answer

Some files may use a different delimiter (e.g., tab) and are not parsed correctly, resulting in zero rows from those files.

Answer

The table schema does not match the data, causing some files to be skipped.

Answer

Athena skips files larger than a certain size to prevent scanning too much data.