MLS-C01 Mock Exam 71 — 25 Questions

Question 1

A team is using Amazon SageMaker Data Wrangler to perform exploratory data analysis on a large dataset stored in S3. The dataset contains missing values, outliers, and categorical variables with high cardinality. The team wants to understand data distributions and relationships before modeling. Which combination of Data Wrangler features should they use?

Accepted Answer

Generate a data quality report, view histograms, and create scatter plots for selected features.. Option D is correct because Data Wrangler's data quality report provides summary statistics and missing value analysis, and the histogram visualization shows distributions. Scatter plots reveal relationships between variables. Option A is incorrect because Data Wrangler does not include correlation heatmaps directly. Option B is incorrect because imputation and one-hot encoding are transformations, not EDA steps. Option C is incorrect because handling missing values is part of data preparation, not initial EDA.

Answer

Drop rows with missing values and visualize box plots for numerical features.

Answer

Use imputation to handle missing values and one-hot encoding for categorical features.

Answer

Generate a data quality report and a correlation heatmap.

Question 2

A data analyst is exploring a dataset with a binary target variable. Which TWO visualizations are most useful for understanding the relationship between a numerical feature and the target?

Accepted Answer

Histogram with overlaid target classes. Options A and D are correct. A: A box plot grouped by target class shows distribution differences. D: A histogram with overlaid target classes shows how the feature distribution differs. Option B is incorrect because a scatter plot is for two numerical variables. Option C is incorrect because a bar chart is for categorical features. Option E is incorrect because a pie chart is for proportions, not relationships.

Answer

Pie chart of the feature

Answer

Bar chart of the feature

Answer

Scatter plot of the feature versus target

Question 3

Which TWO actions are appropriate when handling missing data in a dataset for machine learning? (Select TWO.)

Accepted Answer

Use a machine learning model to predict missing values based on other features. Options A and D are correct. Imputing with mean/median is a common technique, and using a model to predict missing values is also valid. Option B is wrong because dropping all rows with missing values can discard too much data. Option C is wrong because filling with zeros may not be appropriate for all features. Option E is wrong because removing the feature entirely may lose important information.

Answer

Drop all rows that contain any missing value

Answer

Remove the feature entirely if it contains missing values

Answer

Fill missing values with zero

Question 4

A data scientist wants to understand the statistical relationship between two categorical variables in a dataset. Which test is most appropriate?

Accepted Answer

Chi-squared test. Option B is correct because the chi-squared test tests independence between categorical variables. Option A is wrong because ANOVA is for continuous vs categorical. Option C is wrong because Pearson correlation is for continuous variables. Option D is wrong because t-test compares means of two groups.

Answer

Pearson correlation coefficient

Answer

Student's t-test

Answer

ANOVA test

Question 5

A company's dataset contains a feature 'zip_code' with 500 unique values. The data scientist wants to use this feature in a linear model. Which EDA step is most important before feature engineering?

Accepted Answer

Compute the frequency of each zip code. Because zip codes are categorical with high cardinality, analyzing the frequency distribution helps decide how to group or encode them (e.g., target encoding). Option A is wrong because histograms are for continuous variables. Option C is wrong because correlation is for numeric features. Option D is wrong because missing value proportion is unrelated to cardinality handling.

Answer

Check the proportion of missing values

Answer

Plot a histogram of the feature

Answer

Calculate the correlation between zip code and the target

Question 6

A data scientist is analyzing a dataset with 500 features and notices that many features are highly correlated. Which AWS service can be used to automatically reduce dimensionality by identifying and removing redundant features before training a model?

Accepted Answer

Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler provides built-in transformations including correlation analysis and dimensionality reduction. Option A is wrong because QuickSight is for visualization, not feature reduction. Option B is wrong because Glue is for ETL but lacks automatic dimensionality reduction. Option D is wrong because Athena is a query service.

Answer

AWS Glue

Answer

Amazon QuickSight

Answer

Amazon Athena

Question 7

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.. Option C is correct because using SageMaker Processing with a custom script can distribute the computation across multiple instances, making it efficient for large datasets. Option A is wrong because loading all images into memory on a single notebook instance is not feasible. Option B is wrong because Athena is designed for structured data, not images. Option D is wrong because AWS Glue is for ETL on tabular data, not image processing.

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 8

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. Option C is correct because multiple imputation provides unbiased estimates under MAR by accounting for uncertainty. Option A is wrong because mean imputation reduces variance and can bias relationships. Option B is wrong because median imputation is robust but still single imputation. Option D is wrong because dropping rows reduces sample size and may introduce bias if missingness is related to other variables.

Answer

Replace missing values with the mean (45)

Answer

Remove rows with missing 'age' values

Answer

Replace missing values with the median

Question 9

During EDA, a data scientist generates a pairplot of the dataset and observes that two features have a Pearson correlation coefficient of 0.95. Which TWO conclusions can the scientist draw from this observation? (Choose 2)

Accepted Answer

The two features may be multicollinear. Options B and C are correct because a high correlation indicates a strong linear relationship and suggests multicollinearity. Option A is wrong because correlation does not imply causation. Option D is wrong because a high positive correlation means they move together, not opposite. Option E is wrong because correlation measures linear relationship, not independence.

Answer

The two features move in opposite directions

Answer

The two features are statistically independent

Answer

One feature causes the other

Question 10

A data scientist uses Amazon QuickSight to visualize a dataset and observes that a numerical feature has a skewness of 2.5 and a kurtosis of 8. Which transformation should they apply to make the distribution more normal?

Accepted Answer

Apply a log transformation.. Option B is correct because a skewness of 2.5 indicates right skew, and a log transformation is commonly used to reduce skewness. Option A is incorrect because standardization does not change distribution shape. Option C is incorrect because Min-Max scaling does not change skewness. Option D is incorrect because a Box-Cox transformation requires positive data and is a more general solution, but log is simpler and often sufficient; however, Box-Cox is also valid. In the context of this question, log is the most direct answer.

Answer

Standardize the feature using Z-score normalization.

Answer

Apply a Box-Cox transformation with lambda=0.5.

Answer

Apply Min-Max scaling to the range [0,1].

Question 11

A data scientist is using Amazon SageMaker to train a model. The training dataset contains missing values in several features. The data scientist wants to impute missing values using the median of each feature. Which approach is most appropriate?

Accepted Answer

Compute the median of each feature on the training set only, then impute both training and test sets using that median. Option B is correct because the median should be computed on the training set only to avoid data leakage, then applied to both training and test sets. Option A is wrong because imputing with zero may not be appropriate. Option C is wrong because computing median on the entire dataset and then splitting causes data leakage. Option D is wrong because dropping rows with missing values may discard useful data and is not imputation.

Answer

Drop all rows that contain missing values

Answer

Compute the median on the entire dataset, then split into training and test sets

Answer

Impute missing values with zero for all features before splitting

Question 12

A data scientist is performing feature engineering on a dataset with high cardinality categorical features (e.g., ZIP codes with thousands of unique values). Which technique is most effective for reducing dimensionality while preserving predictive power?

Accepted Answer

Target encoding. Option D is correct because target encoding (mean encoding) replaces categories with the mean of the target variable, which captures predictive signal and reduces cardinality. Option A is wrong because one-hot encoding creates many columns, leading to high dimensionality. Option B is wrong because label encoding implies ordinality that may not exist. Option C is wrong because hashing can cause collisions and loss of information.

Answer

Hash encoding

Answer

One-hot encoding

Answer

Label encoding

Question 13

A data scientist ran an AWS Glue ETL job that failed with the error shown. What is the most likely cause?

Accepted Answer

The DataFrame does not have a column named 'age'. Option C is correct because the error indicates the column 'age' is not found in the input data, which has columns [id, name, salary]. Option A is wrong because the error is about a missing column, not a mismatch. Option B is wrong because schema evolution would add column, not cause error. Option D is wrong because there is no indication of data type issue.

Answer

The CSV file has a header mismatch

Answer

The schema is evolving incorrectly

Answer

The data type of 'age' is incompatible

Question 14

A data scientist uses SageMaker Studio to run EDA on a dataset with 500 features. The goal is to reduce dimensionality before modeling. Which EDA technique should the data scientist use to understand the variance explained by each feature?

Accepted Answer

Scree plot of principal components. A Scree plot from PCA shows the eigenvalues or variance explained by each principal component, helping decide how many components to retain. Option A is wrong because a heatmap of correlations shows pairwise relationships, not variance. Option C is wrong because a histogram shows distribution. Option D is wrong because a box plot shows summary statistics.

Answer

Histogram of the target variable

Answer

Heatmap of feature correlations

Answer

Box plot of each feature

Question 15

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 A is correct because high correlation indicates multicollinearity; removing one feature reduces redundancy. Option C is correct because PCA can create uncorrelated components. Option B is wrong because adding interaction terms would increase multicollinearity. Option D is wrong because correlation does not imply missing values. Option E is wrong because log transformation does not address correlation between features.

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 16

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). Option A is correct because PCA is a dimensionality reduction technique that identifies the directions (principal components) that maximize variance. Option B is wrong because t-SNE is for visualization and does not provide variance contributions. Option C is wrong because LDA is supervised and requires labels. Option D is wrong because K-means is clustering, not variance analysis.

Answer

K-means clustering

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Linear Discriminant Analysis (LDA)

Question 17

Refer to the exhibit. A data scientist examines a sample of data and notices that all columns are numeric. The scientist wants to check for multicollinearity. Which statistic should be computed from this sample?

Accepted Answer

Correlation matrix (Pearson). Option A is correct because the correlation matrix shows pairwise Pearson correlations, which can indicate high collinearity. Option B is wrong because VIF requires more variables than observations. Option C is wrong because chi-square is for categorical. Option D is wrong because covariance alone is scale-dependent.

Answer

Chi-square test of independence

Answer

Variance Inflation Factor (VIF)

Answer

Covariance matrix

Question 18

Which TWO actions are appropriate during exploratory data analysis when you discover that a categorical feature has 50 unique values (high cardinality)?

Accepted Answer

Group rare categories into a single 'Other' category.. Options B and D are correct. B: Grouping rare categories into an 'Other' category reduces cardinality while preserving information. D: Using feature hashing can transform high-cardinality categorical features into a fixed-size vector. Option A is incorrect because one-hot encoding creates many columns, which can be problematic. Option C is incorrect because dropping the feature may lose important information. Option E is incorrect because label encoding implies ordinality, which may not exist.

Answer

Apply one-hot encoding to create 50 dummy variables.

Answer

Apply label encoding to assign integers to each category.

Answer

Drop the feature entirely.

Question 19

A data scientist is building a regression model to predict house prices. The dataset includes a feature 'zip_code' with 1,000 unique values. What is the best way to handle this categorical feature in the exploratory data analysis phase?

Accepted Answer

Apply target encoding using the mean house price per zip code. Option A is correct because target encoding (mean encoding) captures the relationship between the category and the target, and is suitable for high-cardinality features. Option B is wrong because one-hot encoding would create too many dummy variables. Option C is wrong because label encoding implies ordinality which is not present. Option D is wrong because frequency encoding may not capture price variation well.

Answer

One-hot encode the zip_code feature

Answer

Replace zip_code with the frequency of each zip code in the dataset

Answer

Use label encoding: assign each zip code a unique integer

Question 20

A data scientist is exploring a dataset containing customer transactions. They want to create a feature that captures the average purchase amount per customer over the last 30 days. Which approach is most efficient in Amazon SageMaker Processing?

Accepted Answer

Use PySpark with window functions in SageMaker Processing. Option D is correct because using PySpark in SageMaker Processing with window functions is efficient for grouped time-series aggregations. Option A is wrong because iterating over rows is inefficient in Python. Option B is wrong because SQL in Athena may be simpler but requires moving data. Option C is wrong because pandas may not scale to large datasets.

Answer

Use Amazon Athena SQL query with GROUP BY

Answer

Use a Python script with a for loop to calculate per customer

Answer

Use pandas groupby and rolling functions