MLS-C01 Practice Exam 61 — 20 Questions

Question 1

A machine learning team is analyzing feature importance in a dataset with many categorical features. They plan to use a tree-based model. Which encoding method should they use to handle high-cardinality categorical features without creating too many dummy variables?

Accepted Answer

Target encoding. Option C is correct because target encoding replaces categories with the mean of the target, which is efficient and works well with tree models. Option A is wrong because one-hot encoding creates many columns for high cardinality. Option B is wrong because label encoding imposes ordinality. Option D is wrong because frequency encoding may not capture predictive information.

Answer

One-hot encoding

Answer

Label encoding

Answer

Frequency encoding

Question 2

Which TWO of the following are appropriate techniques for handling missing data during exploratory data analysis? (Select TWO.)

Accepted Answer

Impute missing values using mean or median for numerical features. Options A and C are correct. Visualizing missing data patterns (A) helps understand the missing mechanism. Using imputation methods like mean/median (C) is common during EDA. Option B is wrong because deleting all rows with missing values may discard too much data. Option D is wrong because ignoring missing values can lead to errors. Option E is wrong because replacing with -1 can distort data.

Answer

Ignore missing values and proceed with modeling

Answer

Replace missing values with -1 to indicate missing

Answer

Delete all rows with any missing values

Question 3

A data scientist is exploring a dataset with a column 'transaction_date'. They want to create features for day of week and month. What is the correct AWS service to schedule a recurring ETL job for this transformation?

Accepted Answer

AWS Glue. Option C is correct because AWS Glue is a serverless ETL service. Option A is wrong because SageMaker is for ML modeling. Option B is wrong because Athena is for querying. Option D is wrong because Lambda is for serverless functions, but not a full ETL service.

Answer

Amazon Athena

Answer

Amazon SageMaker

Answer

AWS Lambda

Question 4

A data scientist is analyzing server logs stored in Amazon CloudWatch Logs. The above snippet shows three log entries. They want to count the number of 500 errors per minute using CloudWatch Logs Insights. Which query should they use?

Accepted Answer

fields @timestamp, @message | filter @message like /500/ | stats count() by bin(1m). Option B is correct because the query filters status 500, parses the timestamp, and counts per minute. Option A is wrong because it filters error messages, not status codes. Option C is wrong because it filters by fields that may not exist. Option D is wrong because it uses sort without stats.

Answer

fields @timestamp, status | filter status = 500 | stats count() by bin(1m)

Answer

fields @timestamp, @message | filter @message like /ERROR/ | stats count() by bin(1m)

Answer

fields @timestamp, @message | filter @message like /500/ | sort @timestamp desc

Question 5

A data scientist is exploring a dataset with 50 features. Which TWO EDA techniques are most effective for detecting multicollinearity?

Accepted Answer

Scatter plots of each feature pair. Option A is correct because scatter plots of feature pairs can reveal linear relationships. Option C is correct because correlation matrix with heatmap quantifies pairwise correlations. Option B is wrong because histogram shows distribution, not relationship. Option D is wrong because VIF is a statistical test, but the question asks for EDA techniques (visual). Option E is wrong because box plot shows univariate distribution.

Answer

Box plots of each feature

Answer

Variance Inflation Factor (VIF) analysis

Answer

Histograms of each feature

Question 6

A data scientist is working with a dataset containing text reviews. The goal is to classify sentiment. During EDA, they compute the word frequency distribution. They notice that the most frequent words are common stop words like 'the', 'and', 'a'. Which action should they take to improve the feature representation for modeling?

Accepted Answer

Remove the stop words from the text before creating the bag-of-words representation.. Option B is correct because removing stop words focuses on content words that carry sentiment. Option A is wrong because adding more stop words would remove even more potentially useful words. Option C is wrong because stemming reduces words to root forms but does not address stop words. Option D is wrong because n-grams capture phrases but still include stop words.

Answer

Use n-grams instead of unigrams to capture phrase patterns.

Answer

Add more stop words to the default list to remove even more common words.

Answer

Apply stemming to reduce words to their root forms.

Question 7

A data scientist is working with a dataset that contains a feature with many outliers. Which transformation should the scientist apply to reduce the impact of outliers?

Accepted Answer

Log transformation. Log transformation compresses the range of values and reduces the impact of outliers. Standardization (z-score) does not reduce outlier impact. Min-max scaling is sensitive to outliers. Square root transformation is less effective than log for large outliers. Binning loses information.

Answer

Min-max scaling

Answer

Standardization (z-score)

Answer

Binning

Question 8

A data scientist is exploring a dataset with 1,000 features and only 200 samples. The goal is to build a binary classifier. Which technique should be used first during exploratory data analysis to reduce dimensionality and avoid overfitting?

Accepted Answer

Use principal component analysis (PCA) to reduce dimensions.. Option C is correct because PCA is an unsupervised dimensionality reduction technique suitable for high-dimensional data with few samples. Option A is wrong because feature selection based on correlation may miss interactions. Option B is wrong because L1 regularization is model-dependent and not part of EDA. Option D is wrong because t-SNE is for visualization, not feature reduction for modeling.

Answer

Compute pairwise correlations and remove highly correlated features.

Answer

Apply L1 regularization (Lasso) to select features.

Answer

Use t-SNE to visualize clusters and reduce dimensions.

Question 9

A data analyst is working with a time series dataset that shows increasing variance over time. To stabilize the variance before modeling, which transformation is most appropriate?

Accepted Answer

Log transformation. Option A is correct because the log transformation is commonly used to stabilize variance when variance increases with the mean. Box-Cox (B) is more general but requires positive data. Differencing (C) is for trend/seasonality, not variance. Min-max scaling (D) does not stabilize variance.

Answer

First-order differencing

Answer

Box-Cox transformation

Answer

Min-max scaling

Question 10

A data engineer is preparing a dataset for training a binary classification model. The target variable is highly imbalanced (95% negative, 5% positive). The engineer needs to split the data into training and test sets while maintaining the class distribution in both sets. Which method should the engineer use?

Accepted Answer

Use stratified random sampling to split the data. Option D is correct because stratified random sampling ensures the proportion of classes is preserved in both training and test sets. Option A is wrong because simple random sampling may result in uneven distribution. Option B is wrong because oversampling should be done after splitting to avoid data leakage. Option C is wrong because k-fold cross-validation is not a split method.

Answer

Use k-fold cross-validation and then split the data

Answer

Oversample the minority class first, then do a random split

Answer

Perform a simple random 80/20 split

Question 11

A data scientist is analyzing a dataset where the target variable is highly imbalanced (1% positive class). They are performing EDA. Which metric is most appropriate for evaluating class separation in the feature space?

Accepted Answer

Area Under the ROC Curve (AUC-ROC). Option D is correct because AUC-ROC is robust to class imbalance and measures separability. Option A is wrong because accuracy is misleading on imbalanced data. Option B is wrong because F1 score is a model evaluation metric, not for EDA. Option C is wrong because log loss is a probabilistic metric.

Answer

Accuracy

Answer

F1 score

Answer

Log loss

Question 12

A data scientist is analyzing a dataset with 100 features and 10,000 samples. The target variable is highly imbalanced (1% positive class). Which exploratory data analysis step is most critical before model training?

Accepted Answer

Plot the histogram of the target variable. Option C is correct because understanding the distribution of the target variable is essential for imbalanced datasets to choose appropriate sampling techniques or evaluation metrics. Option A is wrong because correlation analysis is less critical than target distribution. Option B is wrong because PCA is a dimensionality reduction technique not primarily for EDA. Option D is wrong because missing value imputation is important but not the most critical for imbalance.

Answer

Apply PCA and visualize the first two principal components

Answer

Compute pairwise correlation matrix among all features

Answer

Impute missing values using mean imputation

Question 13

A machine learning team is analyzing a dataset with 10,000 rows and 200 features. They suspect data leakage due to time-based features. Which THREE EDA checks should they perform?

Accepted Answer

Plot distribution of each feature in training vs. test sets. Option A is correct because feature correlation with target in training vs. test sets may indicate leakage. Option C is correct because time-based split (chronological) can reveal if future data leaks into training. Option D is correct because distribution differences between train and test sets can indicate leakage (e.g., train has future data). Option B is wrong because clustering is not directly helpful for leakage detection. Option E is wrong because PCA is for dimensionality reduction, not leakage detection.

Answer

Apply PCA and check if first two components separate train/test

Answer

Perform k-means clustering on the whole dataset

Question 14

After loading a dataset into a pandas DataFrame, a data scientist runs df.info() and sees that a column 'income' has object dtype. What does this indicate, and what EDA step should be taken?

Accepted Answer

The column contains strings; convert to numeric using pd.to_numeric() and investigate non-convertible values.. Option C is correct because object dtype typically indicates string or mixed types; converting to numeric allows mathematical operations. Option A is wrong because object dtype does not necessarily indicate missing values. Option B is wrong because object dtype is not automatically numeric. Option D is wrong because normalization is done after conversion.

Answer

The column has missing values; impute them.

Answer

Normalize the column to a 0-1 range.

Answer

The column is already numeric; proceed.

Question 15

A data scientist is analyzing application logs in JSON format. Based on the exhibit, which EDA insight is most valuable for troubleshooting?

Accepted Answer

There is a recurring NullPointerException error.. Option B is correct because the repeated NullPointerException suggests a recurring issue that needs immediate attention. Option A is wrong because connection timeout appears only once. Option C is wrong because there are multiple timestamps. Option D is wrong because WARN level appears only once.

Answer

All logs occurred at the same timestamp.

Answer

There is a connection timeout issue.

Answer

Most logs are at WARN level.

Question 16

A company uses Amazon SageMaker to train a regression model. After training, the data scientist notices that the training loss decreases but validation loss increases after a few epochs. Which EDA technique could have helped predict this behavior?

Accepted Answer

Plot learning curves showing training and validation loss over epochs. Option B is correct because plotting learning curves (training and validation loss vs. epochs) would show overfitting. Option A is wrong because confusion matrix is for classification. Option C is wrong because box plots show outliers but not overfitting. Option D is wrong because residual plots are for linear regression assumptions.

Answer

Create box plots of each feature to identify outliers

Answer

Generate residual plots to check heteroscedasticity

Answer

Plot confusion matrix on the validation set

Question 17

During EDA, a data scientist notices that a numeric feature 'age' has values ranging from 0 to 150, but expects adult ages between 18-100. Which TWO steps should the scientist take to investigate?

Accepted Answer

Compute summary statistics (min, max, percentiles). Option B is correct because box plots show outliers. Option D is correct because summary statistics (min, max, percentiles) reveal extreme values. Option A is wrong because removing outliers before understanding context is premature. Option C is wrong because log transformation changes scale but does not help identify outliers. Option E is wrong because mean imputation would distort distribution.

Answer

Remove all rows with age > 100

Answer

Apply log transformation to normalize the distribution

Answer

Impute age values outside 18-100 with the mean

Question 18

A data scientist is performing EDA on a dataset with 100 features. They want to reduce dimensionality by removing highly correlated features. Which TWO approaches are appropriate? (Choose TWO.)

Accepted Answer

Compute a correlation matrix and remove one feature from each pair with correlation >0.95.. Options A and D are correct. Option A: Removing features with correlation >0.95 directly reduces redundancy. Option D: Using PCA creates uncorrelated components. Option B is wrong because L1 regularization is a modeling technique, not EDA. Option C is wrong because feature importance from tree-based models is not specifically for removing correlated features.

Answer

Use feature importance from a random forest to select top features.

Answer

Remove features with low variance using VarianceThreshold.

Answer

Apply L1 regularization (Lasso) during model training to zero out coefficients of correlated features.

Question 19

Which TWO of the following are common techniques for detecting outliers in a numerical feature?

Accepted Answer

Interquartile Range (IQR). Z-score and IQR are standard outlier detection methods. PCA can detect outliers but is not a common direct method. Chi-square is for categorical association. Standard deviation alone is not a method.

Answer

Chi-square test

Answer

Standard deviation

Answer

Principal Component Analysis (PCA)

Question 20

A data scientist is performing EDA and observes that a feature 'purchase_amount' has many zeros and a long tail of positive values. What type of model would be appropriate for this target variable?

Accepted Answer

Zero-inflated negative binomial regression.. Option A is correct because zero-inflated models handle excess zeros. Option B is wrong because linear regression assumes normal distribution. Option C is wrong because Poisson regression is for count data without excess zeros. Option D is wrong because logistic regression is for binary outcomes.

Answer

Linear regression after log transformation.

Answer

Logistic regression on binary indicator of purchase.

Answer

Poisson regression.