MLS-C01 Mock Exam 72 — 25 Questions

Question 1

A data scientist is analyzing a dataset with 50 features and 10,000 samples. After generating a correlation matrix, they notice several pairs of features have correlation coefficients above 0.95. What should the data scientist do to prepare the data for linear regression?

Accepted Answer

Remove one feature from each highly correlated pair.. Option B is correct because high correlation between features indicates multicollinearity, which can destabilize linear regression coefficients. Removing one feature from each highly correlated pair reduces redundancy. Option A is wrong because dropping all correlated features may discard useful information. Option C is wrong because standardizing does not address multicollinearity. Option D is wrong because PCA creates new features that lose interpretability.

Answer

Apply PCA to reduce dimensionality to 10 components.

Answer

Drop all features with correlation above 0.95.

Answer

Standardize all features using StandardScaler.

Question 2

Which THREE techniques are commonly used to detect multicollinearity in a dataset during exploratory data analysis?

Accepted Answer

Eigenvalue analysis from PCA. Options A, B, and D are correct. A: Correlation matrix shows pairwise correlations; high values indicate collinearity. B: Variance Inflation Factor (VIF) quantifies how much a feature is explained by others. D: Eigenvalues from PCA can indicate multicollinearity if some are near zero. Option C is incorrect because a scatter matrix shows pairwise relationships but not a multicollinearity measure. Option E is incorrect because a heatmap of missing values is unrelated to multicollinearity.

Answer

Heatmap of missing values

Answer

Scatter matrix of all features

Question 3

A data scientist is analyzing a dataset for a binary classification problem. The dataset has 10,000 samples and 200 features. After splitting into training (80%) and test (20%), the data scientist trains a decision tree classifier and achieves 100% accuracy on the training set but only 55% on the test set. Which step should the data scientist take first to address this issue?

Accepted Answer

Prune the decision tree to reduce complexity. Option D is correct because the large discrepancy between training and test accuracy indicates overfitting, and pruning the decision tree (e.g., limiting max_depth) reduces overfitting. Option A is wrong because more features may worsen overfitting. Option B is wrong because more data may help but is not the first step; also data is limited. Option C is wrong because cross-validation is a technique to evaluate model performance but does not directly fix overfitting; pruning does.

Answer

Use cross-validation to evaluate model performance

Answer

Collect more training data

Answer

Add more features to the model

Question 4

A data scientist is working with a dataset that has imbalanced classes (1% positive). They want to explore the data before modeling. Which visualization technique is most appropriate to understand the distribution of features with respect to the target class?

Accepted Answer

Parallel coordinates plot. Option B is correct because parallel coordinates plot can show feature patterns for minority vs majority class in high dimensions. Option A is wrong because scatter plot matrices become cluttered with many features. Option C is wrong because histograms are univariate and do not show interaction. Option D is wrong because box plots are univariate.

Answer

Box plots grouped by class

Answer

Histograms overlaid by class

Answer

Scatter plot matrix

Question 5

A data scientist runs a logistic regression and obtains a model with 95% accuracy on the training set. However, the model performs poorly on the test set. Which exploratory data analysis step should have been performed to identify this issue?

Accepted Answer

Checking for class imbalance in the target variable. Checking for class imbalance is critical because it can cause a model to predict the majority class and still achieve high accuracy, but fail on the minority class in unseen data. Option A is wrong because log transformation is for skewness, not class imbalance. Option C is wrong because a correlation matrix helps with multicollinearity. Option D is wrong because missing value heatmaps show missing data patterns.

Answer

Generating a correlation matrix of features

Answer

Log transformation of skewed features

Answer

Creating a heatmap of missing values

Question 6

A data scientist wants to identify outliers in a dataset. Which TWO techniques are commonly used for outlier detection during EDA?

Accepted Answer

Box plot. Option A (box plot) identifies outliers as points beyond whiskers. Option C (Z-score) flags points with |Z| > 3. Option B is wrong because bar charts are for categorical data. Option D is wrong because Pearson correlation measures linear relationship. Option E is wrong because heatmaps show correlations.

Answer

Heatmap

Answer

Bar chart

Answer

Pearson correlation coefficient

Question 7

A data scientist is performing EDA on a dataset with 1,000 features. The goal is to select the most important features for a regression model. Which technique can be used to rank feature importance quickly?

Accepted Answer

Calculate the correlation coefficient of each feature with the target. Correlation analysis with the target variable is a quick way to rank features. Option B is wrong because PCA is unsupervised and does not rank features by importance to target. Option C is wrong because t-SNE is for visualization. Option D is wrong because k-means is clustering.

Answer

Use t-SNE to visualize feature relationships

Answer

Run k-means clustering and use cluster centroids

Answer

Apply Principal Component Analysis (PCA) and examine component loadings

Question 8

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.. Option D is correct because the CLI command may not return all objects if there are more than 1000, as S3 list-objects paginates by default. Option A is wrong because the command returns size and key, which are sufficient. Option B is wrong because the command does include objects exactly 100 KB. Option C is wrong because the query syntax is correct for filtering.

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 9

In exploratory data analysis, a data scientist notices that the distribution of a continuous variable is bimodal. The scientist suspects that the two modes correspond to two different groups in the data. Which visualization is MOST appropriate to confirm this suspicion?

Accepted Answer

Histogram with overlaid densities by group. Option A is correct because a histogram with hue (color) for each group can show the separate distributions. Option B is wrong because a box plot shows summary statistics but not the shape. Option C is wrong because a scatter plot is for two continuous variables. Option D is wrong because a bar chart is for categorical data.

Answer

Box plot

Answer

Bar chart

Answer

Scatter plot

Question 10

A machine learning engineer notices that the target variable in a regression dataset has a long-tailed distribution. Which visualization technique is most appropriate to assess the distribution before applying a log transformation?

Accepted Answer

Histogram with density curve. Option C is correct because a histogram or density plot clearly shows the shape and spread of the distribution, including long tails. Option A is incorrect because box plots show quartiles and outliers but not the full distribution shape. Option B is incorrect because scatter plots show relationships between two variables, not univariate distribution. Option D is incorrect because bar charts are for categorical data.

Answer

Bar chart

Answer

Box plot

Answer

Scatter plot

Question 11

A data scientist is analyzing a dataset with 1,000 features. They suspect many features are redundant and want to reduce dimensionality before training a model. Which technique is most appropriate for identifying the most important features?

Accepted Answer

Apply principal component analysis (PCA) and select the top components. Option B is correct because principal component analysis (PCA) is a dimensionality reduction technique that identifies the principal components capturing the most variance. Option A is wrong because correlation matrix only shows pairwise linear relationships, not importance. Option C is wrong because regularization can shrink coefficients but is not a dedicated dimensionality reduction technique. Option D is wrong because random forests can provide feature importance but are not a dimensionality reduction technique per se.

Answer

Use L1 regularization (Lasso) to shrink coefficients to zero

Answer

Train a random forest and remove features with low importance

Answer

Compute the correlation matrix and remove features with high correlation

Question 12

Which THREE techniques are commonly used to detect outliers in a dataset? (Select THREE.)

Accepted Answer

Interquartile range (IQR). Options A, B, and D are correct. Z-score and IQR are standard statistical methods, and isolation forest is a machine learning algorithm for anomaly detection. Option C is wrong because PCA is for dimensionality reduction, not outlier detection, though it can be used in some contexts but is not common. Option E is wrong because k-means clustering is for clustering, not specifically for outlier detection.

Answer

k-means clustering

Answer

Principal component analysis (PCA)

Question 13

A data scientist is analyzing a dataset with a time series component. They suspect there is a weekly seasonality. Which technique should they use to confirm this?

Accepted Answer

Compute autocorrelation function (ACF). Option C is correct because autocorrelation function (ACF) can show peaks at lag 7 indicating weekly seasonality. Option A is wrong because line plot can show patterns but is subjective. Option B is wrong because moving average smooths data and may hide seasonality. Option D is wrong because spectral analysis is for frequency, but ACF is simpler.

Answer

Plot the time series line chart

Answer

Perform Fourier transform

Answer

Compute a 7-day moving average

Question 14

A data scientist is analyzing a dataset with many features and wants to identify which features are most correlated with the target variable. Which EDA technique should be used?

Accepted Answer

Correlation matrix. A correlation matrix shows pairwise correlations between all numeric features and the target. Option A is wrong because scatter plots can only show one pair at a time. Option B is wrong because histograms show distributions, not correlations. Option D is wrong because box plots show distributions per category, not correlations.

Answer

Box plots grouped by target

Answer

Scatter plot matrix

Answer

Histogram of each feature

Question 15

Refer to the exhibit. A data scientist runs the AWS CLI command shown to explore the contents of an S3 bucket. The command returns an empty array. However, the data scientist knows there are objects larger than 1000 bytes in the bucket. What is the most likely reason for the empty result?

Accepted Answer

The AWS CLI is not configured with the correct region for the bucket. The command uses backticks incorrectly; in CLI, the correct syntax is --query "Contents[?Size > `1000`]" but the backticks are not valid for numeric comparison in JMESPath. The proper syntax is Size > `1000` with backticks? Actually, JMESPath uses backticks for literal values. The command appears correct. However, the issue might be that the objects are under a different prefix or the bucket is in a different region. But the most likely reason is that the command is missing the --region parameter if the bucket is not in the default region. Option C is correct. Option A is wrong because the syntax is correct. Option B is wrong because the query syntax is valid. Option D is wrong because the command lists objects.

Answer

The query syntax is incorrect; backticks should not be used

Answer

The command should use list-objects instead of list-objects-v2

Answer

The --query parameter is not supported by list-objects-v2

Question 16

A data scientist is performing EDA on a dataset with 10 million rows. The dataset has a column 'income' with outliers. The data scientist wants to detect and handle outliers. Which THREE approaches are appropriate?

Accepted Answer

Calculate z-scores and flag values beyond 3 standard deviations. IQR method, z-score, and visualization like box plots are standard for outlier detection. Option D is wrong because min-max scaling does not handle outliers. Option E is wrong because one-hot encoding is for categorical data.

Answer

Apply min-max scaling to the column

Answer

Convert the column to one-hot encoding

Question 17

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. Option D is correct because target encoding (or mean encoding) replaces each category with the mean of the target, which is suitable for high cardinality in linear models but requires careful validation to avoid overfitting. Option A is wrong because one-hot encoding would create 199 dummy variables, leading to high dimensionality. Option B is wrong because label encoding imposes an arbitrary ordinal relationship. Option C is wrong because frequency encoding may not capture the relationship with the target.

Answer

Label encoding

Answer

Frequency encoding

Answer

One-hot encoding

Question 18

Refer to the exhibit. A data scientist is setting up an IAM policy for EDA on a data lake. The scientist needs to run exploratory SQL queries using Amazon Athena and save results to a new S3 bucket. What is a critical missing permission in this policy?

Accepted Answer

s3:PutObject on the output S3 bucket. Option B is correct because Athena writes query results to an S3 bucket, which requires s3:PutObject permission on the output bucket. Option A is wrong because the policy includes necessary Athena permissions. Option C is wrong because glue:GetTable is already included. Option D is wrong because s3:ListBucket is included.

Answer

s3:ListBucket on the output bucket

Answer

glue:GetDatabase

Answer

athena:StopQueryExecution

Question 19

A data scientist wants to identify outliers in a dataset with 1,000 samples and 5 numerical features. Which technique is most appropriate for univariate outlier detection?

Accepted Answer

Interquartile range (IQR) method. Option C is correct because the IQR method identifies outliers as points outside 1.5*IQR from the quartiles, which is robust to non-normal distributions. Option A is incorrect because Z-score assumes normality and is sensitive to extreme outliers. Option B is incorrect because Mahalanobis distance is for multivariate outliers. Option D is incorrect because PCA is for dimensionality reduction, not outlier detection.

Answer

Principal component analysis (PCA)

Answer

Mahalanobis distance

Answer

Z-score with a threshold of 3

Question 20

During exploratory data analysis, a data scientist notices that a feature has a highly skewed distribution. Which transformation is most likely to make the distribution approximately normal?

Accepted Answer

Log transformation. Option C is correct because log transformation is commonly used to reduce right skewness. Option A is wrong because standardization does not change distribution shape. Option B is wrong because min-max scaling does not change shape. Option D is wrong because one-hot encoding is for categorical variables, not continuous.

Answer

Min-max scaling

Answer

One-hot encoding

Answer

Standardization (z-score)