MLS-C01 Exploratory Data Analysis Practice Test 26 — 15 Questions

Question 1

Which TWO techniques can be used to detect multicollinearity among numerical features during exploratory data analysis? (Choose two.)

Accepted Answer

Compute a correlation matrix and look for pairs with absolute correlation > 0.8.. Option B is correct because computing a Pearson correlation matrix on numerical features and flagging pairs whose absolute correlation exceeds a threshold such as 0.8 is a standard, direct way to spot strong linear relationships that indicate multicollinearity. Option D is correct because the Variance Inflation Factor quantifies how much a feature's variance is inflated by its linear dependence on the other features, with VIF values above roughly 5 or 10 signaling problematic multicollinearity. Option A is not a detection technique per se: PCA loadings describe variance structure and dimensionality but do not measure collinearity between original features. Option C is wrong because a t-test compares means between groups, not the linear association between two continuous features. Option E is wrong because the chi-square test of independence applies to categorical variables, not numerical features.

Answer

Apply Principal Component Analysis (PCA) and examine loadings.

Answer

Perform a t-test between each pair of features.

Answer

Use a chi-square test of independence.

Question 2

A data scientist runs a SageMaker notebook and uses pandas to explore a dataset. The dataset contains 500,000 rows and 20 columns, including a 'timestamp' column. After loading the data into a DataFrame, the memory usage is unexpectedly high. What is the most likely cause?

Accepted Answer

The default data types inferred by pandas are unnecessarily large for the actual data ranges.. Pandas infers dtypes from the data it reads, and by default it uses int64 for integers and object for strings, which can be far larger than necessary. For 500,000 rows and 20 columns, this can balloon memory usage dramatically — for example, an int64 column uses 8 bytes per value, while an int8 would use 1. Downcasting dtypes is the standard fix.

Answer

The DataFrame created an index column on the timestamp field, doubling memory usage.

Answer

The DataFrame only loaded a sample of the data, but the sample size was too large.

Answer

The CSV file was compressed, and pandas inflated it in memory.

Question 3

A data scientist is using Amazon SageMaker Data Wrangler to perform exploratory data analysis on a dataset. The dataset contains a feature 'age' with values ranging from 0 to 120. The data scientist wants to detect outliers. Which built-in transform in Data Wrangler is most appropriate for this task?

Accepted Answer

Handle Outliers. The 'Handle Outliers' transform in SageMaker Data Wrangler is purpose-built for detecting and treating numeric outliers, offering methods such as standard deviation, robust z-score (MAD), and IQR-based detection. Since 'age' is a continuous numeric feature spanning 0-120, this transform directly identifies values that fall outside statistical bounds. It also lets the data scientist choose to clip, remove, or impute those outliers before model training.

Answer

Scale and Normalize

Answer

Handle Missing

Answer

Encode Categorical

Question 4

A data scientist is analyzing a dataset with numerical features and a binary target variable. The data scientist creates a pairplot and notices that one feature has a bimodal distribution when colored by the target class. What does this observation suggest?

Accepted Answer

The feature is likely predictive of the target.. A bimodal distribution of a feature when colored by the target class suggests the feature separates the two classes into distinct modes — each class clusters around a different peak. This indicates the feature carries discriminative signal and is likely predictive of the target. In a pairplot, this appears as two distinct humps in the feature's histogram, one per class.

Answer

The feature is irrelevant and should be removed.

Answer

The feature contains outliers that need to be removed.

Answer

The feature has missing values that need to be imputed.

Question 5

A data scientist is performing EDA on a dataset of 1 million images stored in Amazon S3. Each image is 100x100 pixels in RGB format. The data scientist wants to compute the mean pixel value per channel across the entire dataset. Which approach is most efficient?

Accepted Answer

Use Amazon SageMaker Processing with a custom Python script that iterates over S3 objects and aggregates pixel values.. (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.

Answer

Use Amazon Athena with a SQL query on the image metadata stored in a CSV file.

Answer

Use AWS Glue ETL to read images and compute the mean.

Answer

Use a SageMaker notebook instance with a large instance type to load all images into memory and compute the mean.

Question 6

A data scientist is analyzing a dataset containing customer reviews. The data scientist wants to understand the most common words used in positive and negative reviews. Which AWS service is most suitable for this task?

Accepted Answer

Amazon Comprehend. Amazon Comprehend is a fully managed NLP service that provides entity recognition, sentiment analysis, key phrase extraction, and topic modeling — exactly what is needed to identify the most common words/phrases in positive versus negative reviews. It handles tokenization, stop-word removal, and language detection out of the box, so the data scientist does not need to build custom NLP pipelines. Comprehend's 'DetectKeyPhrases' and 'DetectSentiment' APIs can be combined to bucket reviews by sentiment and then surface dominant terms.

Answer

Amazon Rekognition

Answer

Amazon Polly

Answer

Amazon Transcribe

Question 7

A data scientist loads a large dataset from Amazon S3 into a pandas DataFrame using a SageMaker notebook. The dataset contains a mix of numeric and categorical features. The data scientist wants to quickly check for missing values. Which pandas function is most appropriate?

Accepted Answer

df.isnull().sum(). Df.isnull().sum() returns the count of missing values per column. Option A is wrong because df.info() provides column data types and non-null counts, but not missing value counts directly. Option B is wrong because df.describe() only summarizes numeric columns. Option C is wrong because df.shape returns the dimensions, not missing values.

Answer

df.info()

Answer

df.describe()

Answer

df.shape

Question 8

A data scientist is exploring a dataset with 50 features and a binary target. The data scientist computes the correlation matrix and finds that two features, X1 and X2, have a correlation coefficient of 0.95. Which TWO actions should the data scientist consider? (Choose 2.)

Accepted Answer

Remove one of the highly correlated features from the dataset.. Option B is correct because a correlation coefficient of 0.95 between X1 and X2 indicates strong multicollinearity, and removing one of the redundant features reduces redundancy and potential instability in models such as linear regression without losing much information. Option C is correct because PCA transforms the original correlated features into uncorrelated principal components, effectively addressing multicollinearity while retaining most of the variance across the 50-feature set. Option A is not appropriate because log transformations address skewness or nonlinearity, not linear correlation between two features. Option D is incorrect because creating an interaction term between two already highly correlated features would exacerbate multicollinearity rather than resolve it. Option E is irrelevant because the scenario describes correlated features, not missing values, so imputation does not address the correlation issue.

Answer

Apply a log transformation to X1 and X2.

Answer

Create an interaction term between X1 and X2.

Answer

Impute missing values for X1 and X2.

Question 9

A data scientist is performing EDA on a dataset stored in Amazon S3 using Amazon Athena. The dataset is partitioned by date, and each partition contains CSV files. The data scientist notices that some queries return zero rows for partitions that should have data. Which THREE steps should the data scientist take to troubleshoot? (Choose 3.)

Accepted Answer

Verify that the CSV files exist in the S3 bucket for the specific partition.. Option A is correct because if the underlying CSV objects are missing or stored under a different S3 prefix than the partition location defined in the table, Athena will scan the partition and return zero rows, so verifying object existence and prefix layout is a fundamental first troubleshooting step. Option B is correct because Athena relies on the AWS Glue Data Catalog for partition metadata; if new date partitions were written to S3 but never registered, queries against those partitions return no data, and MSCK REPAIR TABLE (or ALTER TABLE ADD PARTITION) updates the catalog so the partitions become queryable. Option D is correct because a WHERE clause comparing a string-typed partition or column against a numeric literal (or vice versa) can silently match nothing, so checking column data types in the table definition against the values being filtered is essential. Option C is not appropriate here because converting CSV to Parquet is a performance and cost optimization, not a troubleshooting step for missing rows. Option E is not appropriate because a LIMIT clause only caps the number of returned rows and does not trigger partition discovery or repair catalog metadata.

Answer

Convert the CSV files to Parquet format.

Answer

Re-run the query with a LIMIT clause to force partition discovery.

Question 10

A data scientist is analyzing a dataset with a mix of numerical and categorical features. The target variable is binary. The data scientist wants to visualize the distribution of a numerical feature across the two target classes. Which TWO visualization techniques are appropriate? (Choose 2.)

Accepted Answer

Overlapping histograms with transparency. Option C (Overlapping histograms with transparency) is correct because it directly shows the distribution of a numerical feature for each of the two binary target classes, and using transparency (alpha) lets both class distributions be seen even where they overlap. Option D (Side-by-side boxplots) is correct because boxplots summarize a numerical feature's distribution (median, quartiles, whiskers, outliers) separately for each target class, making class-wise comparison straightforward. Option A (Heatmap of the correlation matrix) is not appropriate here because correlation heatmaps are for showing pairwise relationships among many variables, not the distribution of one numerical feature across classes, and correlation is also unsuitable for categorical features. Option B (Stacked bar chart of the feature binned) is not the best fit because it requires binning the numerical feature and emphasizes counts/proportions per bin rather than the continuous distribution shape. Option E (Scatter plot with color-coded classes) is not appropriate because a scatter plot needs two numerical axes; with only one numerical feature and a binary target, it does not visualize the feature's distribution across classes.

Answer

Heatmap of the correlation matrix

Answer

Stacked bar chart of the feature binned

Answer

Scatter plot with color-coded classes

Question 11

A data scientist runs the AWS CLI command shown in the exhibit to list objects larger than 100 KB in an S3 bucket. The data scientist wants to understand the size distribution of these files. What is the most significant limitation of this approach for EDA?

Accepted Answer

The command may return incomplete results if there are more than 1000 objects.. The AWS CLI command likely uses the 'list-objects-v2' operation, which returns up to 1000 objects per request. If the bucket contains more than 1000 objects that match the criteria, the results will be incomplete unless pagination is used. This is the most significant limitation for EDA because it can lead to an inaccurate understanding of the size distribution.

Answer

The command only returns objects larger than 100 KB, not equal to.

Answer

The command uses the wrong query syntax and will fail.

Answer

The command does not return the file names, only sizes.

Question 12

A data engineer is performing exploratory data analysis on a dataset stored in Amazon S3 using AWS Glue DataBrew. The dataset contains a column 'age' with missing values. DataBrew's profile shows that the column has 5% missing values, a mean of 45, and a standard deviation of 15. Which imputation strategy should the engineer recommend to minimize bias if the missing data is Missing at Random (MAR)?

Accepted Answer

Use multiple imputation to generate several plausible values and combine results. Multiple imputation is the recommended strategy for Missing at Random (MAR) data because it generates several plausible values for each missing entry and combines the results, preserving the variability and relationships in the data. This minimizes bias compared to single-value imputation methods.

Answer

Replace missing values with the mean (45)

Answer

Remove rows with missing 'age' values

Answer

Replace missing values with the median

Question 13

During exploratory data analysis, a data scientist notices that the Pearson correlation coefficient between two continuous variables is 0.85. What does this indicate?

Accepted Answer

A strong positive linear relationship. A correlation coefficient of 0.85 indicates a strong positive linear relationship. Option A is wrong because correlation does not imply causation. Option C is wrong because the coefficient is positive and strong, not negative and weak. Option D is wrong because a coefficient of 0.85 indicates a strong relationship, not no relationship.

Answer

A causal relationship between the two variables

Answer

A weak negative linear relationship

Answer

No relationship between the variables

Question 14

A machine learning engineer is analyzing a dataset that contains a categorical feature 'country' with 200 unique values. The target variable is binary. The engineer wants to use this feature in a linear model. Which encoding method should be applied during EDA to prepare the data for modeling, considering the high cardinality?

Accepted Answer

Target encoding with cross-validation. Target encoding with cross-validation is suitable for high-cardinality categorical features because it replaces each category with the mean of the target variable for that category, reducing dimensionality while capturing the relationship with the target. Cross-validation prevents data leakage and overfitting, which is crucial when the feature has many unique values like 200 countries.

Answer

Label encoding

Answer

Frequency encoding

Answer

One-hot encoding

Question 15

A data analyst is performing exploratory data analysis on a dataset with 100 features. The analyst wants to identify which features contribute most to the variance in the data. Which technique should the analyst use?

Accepted Answer

Principal Component Analysis (PCA). PCA is a dimensionality reduction technique that identifies the principal components — linear combinations of features — that capture the maximum variance in the data. It directly answers the analyst's goal of finding which features contribute most to variance. The resulting components are ranked by explained variance ratio.

Answer

K-means clustering

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Linear Discriminant Analysis (LDA)