MLS-C01 Mock Exam 73 — 25 Questions

Question 1

A company runs a real-time fraud detection system using Amazon SageMaker. The model is deployed as a SageMaker endpoint and receives predictions within milliseconds. Recently, the model's accuracy has degraded due to data drift. The data scientists want to monitor the model's performance continuously. What is the most effective way to detect data drift?

Accepted Answer

Enable Amazon SageMaker Model Monitor to capture inference data and compare it against a baseline dataset. Option C is correct because SageMaker Model Monitor can automatically detect data drift by comparing incoming data against a baseline. Option A is wrong because CloudWatch Logs Insights can query logs but not automatically detect drift. Option B is wrong because storing predictions in S3 and using Athena is batch-oriented and not automated. Option D is wrong because CloudWatch anomaly detection is generic and not specialized for ML model drift.

Answer

Store all incoming requests in Amazon S3 and use Amazon Athena to run periodic SQL queries for drift detection

Answer

Set up Amazon CloudWatch anomaly detection on the endpoint's invocation count and latency metrics

Answer

Use Amazon CloudWatch Logs Insights to analyze inference logs and set custom alarms

Question 2

Which TWO statements about data leakage in machine learning are correct? (Select TWO.)

Accepted Answer

Using the target variable to filter features before splitting leads to data leakage. Options A and C are correct. Scaling before splitting is a classic source of data leakage. Using target information to filter features is also leakage. Option B is wrong because proper cross-validation prevents leakage if done correctly. Option D is wrong because time-based split is a valid way to prevent leakage in time series. Option E is wrong because SMOTE should be applied after splitting to avoid leakage.

Answer

Applying SMOTE after splitting the dataset prevents data leakage

Answer

Using cross-validation eliminates all possible data leakage

Answer

For time series data, using a random train-test split is recommended to avoid data leakage

Question 3

A data scientist is performing exploratory data analysis on a dataset with missing values. Which TWO approaches are appropriate for handling missing data in a way that retains as much data as possible?

Accepted Answer

Use a model that handles missing values natively, such as XGBoost. Correct options: A and C. Option A (imputation) retains all rows and fills missing values. Option C (use algorithms that handle missing values) avoids deletion. Option B is wrong because dropping rows reduces data. Option D is wrong because dropping columns loses features. Option E is wrong because replacing with a constant may bias.

Answer

Replace missing values with a constant like -999

Answer

Drop columns with high missing percentage

Answer

Drop rows with any missing value

Question 4

A data scientist uses Amazon SageMaker Data Wrangler to perform EDA on a large dataset stored in S3. The data scientist notices that the target variable is highly imbalanced. Which SageMaker Data Wrangler transform can be used to address this during data preparation?

Accepted Answer

SMOTE (Synthetic Minority Over-sampling). SMOTE is available in SageMaker Data Wrangler to generate synthetic samples for the minority class. Option A is wrong because one-hot encoding is for categorical features. Option B is wrong because standard scaling normalizes numeric features. Option D is wrong because principal component analysis reduces dimensionality.

Answer

Standard scaling

Answer

Principal component analysis (PCA)

Answer

One-hot encoding

Question 5

A data scientist needs to profile a large dataset in Amazon S3 to understand its schema, data types, and quality. Which AWS service can automatically generate a data profile with statistics and visualizations?

Accepted Answer

AWS Glue DataBrew. AWS Glue DataBrew provides data profiling capabilities. Option B is wrong because Athena is a query service. Option C is wrong because Redshift is a data warehouse. Option D is wrong because QuickSight is for visualization after data is prepared.

Answer

Amazon Athena

Answer

Amazon QuickSight

Answer

Amazon Redshift

Question 6

A data scientist is performing EDA on a dataset with 500,000 rows and 20 columns. The dataset contains missing values in some columns. Which TWO approaches are appropriate for handling missing data during EDA? (Choose 2)

Accepted Answer

Create an indicator column to flag whether the value was missing, then impute with a placeholder. Options C and D are correct because imputation with median/mode is robust, and flagging missingness with an indicator variable preserves information. Option A is wrong because listwise deletion can introduce bias and reduce sample size. Option B is wrong because mean imputation is sensitive to outliers. Option E is wrong because forward fill is for time series, not general EDA.

Answer

Use forward fill to propagate the last observed value

Answer

Remove all rows with any missing value (listwise deletion)

Answer

Impute missing values with the mean of each column

Question 7

A data scientist is performing exploratory data analysis on a dataset with mixed data types: numerical, categorical, and text. They want to use Amazon SageMaker Data Wrangler to create a quick visualization dashboard. Which set of transformations should they apply in Data Wrangler to handle all data types appropriately?

Accepted Answer

Use the built-in analysis: summary statistics for numerical, word cloud for text, and frequency for categorical.. Option D is correct because Data Wrangler's built-in analysis includes summary statistics for numerical features, word clouds for text, and frequency counts for categorical features. These are appropriate for initial EDA. Option A is incorrect because PCA is for dimensionality reduction, not EDA. Option B is incorrect because TF-IDF is a feature engineering step, not EDA. Option C is incorrect because clustering is a modeling step, not EDA.

Answer

Convert all features to numerical using one-hot encoding and then create a scatter matrix.

Answer

Apply TF-IDF vectorization to text and then run k-means clustering.

Answer

Use PCA to reduce dimensionality and then visualize the first two components.

Question 8

A data scientist is working on a binary classification problem with a highly imbalanced dataset (1% positive class). They have applied oversampling using SMOTE and trained a logistic regression model. The model achieves 99% accuracy on the test set, but the recall for the positive class is only 5%. What is the most likely cause?

Accepted Answer

SMOTE was applied before splitting the data into training and test sets. Option D is correct because if SMOTE was applied before splitting, synthetic samples leak information from the test set into the training set, leading to overoptimistic accuracy but poor generalization. Option A is wrong because logistic regression can handle balanced data, though it may not capture complex patterns. Option B is wrong because accuracy is a poor metric for imbalanced data, but the low recall indicates a problem beyond metric choice. Option C is wrong because while L2 regularization might help, it would not cause such a discrepancy between accuracy and recall.

Answer

The model is overfitting due to lack of regularization

Answer

Accuracy is not a suitable metric for imbalanced data

Answer

Logistic regression is inappropriate for imbalanced datasets

Question 9

A data scientist needs to understand the distribution of a continuous variable in a large dataset stored in Amazon S3. Which AWS service is most appropriate for quickly generating summary statistics and visualizations?

Accepted Answer

Amazon QuickSight. Option A is correct because Amazon QuickSight integrates with S3 and can generate histograms and summary statistics without needing to move data. Option B is wrong because SageMaker is for model building, not quick ad-hoc analysis. Option C is wrong because Athena is for querying, not visualization. Option D is wrong because Glue is for ETL, not analysis.

Answer

AWS Glue

Answer

Amazon Athena

Answer

Amazon SageMaker Studio

Question 10

A data scientist is exploring a dataset with skewed numerical features. Which THREE transformations can help make the features more normally distributed?

Accepted Answer

Yeo-Johnson transformation. Correct options: A, B, D. Log transform (A), Box-Cox (B), and Yeo-Johnson (D) are common for normalizing skewed data. Option C is wrong because standardization (Z-score) does not change distribution shape. Option E is wrong because min-max scaling does not fix skewness.

Answer

Min-max scaling

Answer

Standardization (Z-score)

Question 11

A data scientist is performing EDA on a time series dataset of daily sales. The data scientist observes a pattern that repeats every 7 days. Which characteristic of the time series is being observed?

Accepted Answer

Seasonality. A pattern that repeats at a fixed frequency (every 7 days) is called seasonality. Option A is wrong because trend is a long-term increase or decrease. Option C is wrong because autocorrelation measures correlation with lagged values, not a repeating pattern. Option D is wrong because stationarity refers to constant mean/variance over time.

Answer

Stationarity

Answer

Autocorrelation

Answer

Trend

Question 12

A data scientist is performing EDA on a dataset with 1 million rows and 50 features. The dataset includes a column 'user_id' with unique identifiers, a column 'event_date' with timestamps, and other columns. Which TWO actions should the data scientist take to understand data quality issues?

Accepted Answer

Analyze missing value patterns across columns. Checking for duplicate rows and analyzing missing values are fundamental steps in EDA. Option B is wrong because dropping user_id before analysis may lose information. Option C is wrong because training a model is not part of EDA. Option D is wrong because PCA is not for data quality.

Answer

Drop the 'user_id' column to reduce dimensionality

Answer

Use PCA to reduce dimensions and visualize

Answer

Train a random forest model to identify feature importance

Question 13

A data scientist is evaluating feature engineering options for a dataset containing a categorical variable 'education_level' with values: High School, Bachelor, Master, PhD. The target variable is continuous. Which THREE encoding methods are appropriate for this ordinal categorical variable? (Choose 3)

Accepted Answer

One-hot encoding. Options A, B, and E are correct because label encoding preserves ordinality, target encoding captures the relationship with the target, and one-hot encoding is a safe fallback. Option C is wrong because binary encoding assumes nominal categories. Option D is wrong because hash encoding loses interpretability and may cause collisions.

Answer

Hash encoding (using feature hashing)

Answer

Binary encoding (convert to binary representation)

Question 14

During exploratory data analysis, a machine learning engineer finds that a dataset has a significant number of missing values in a categorical feature with 10 levels. Which approach should they take to handle these missing values before modeling?

Accepted Answer

Create a new category labeled 'Missing' for missing values.. Option C is correct because creating a separate 'Missing' category preserves the missingness pattern and avoids data loss or bias from imputation for categorical features. Option A is incorrect because dropping rows with missing values may discard valuable data. Option B is incorrect because mean imputation is for numerical features, not categorical. Option D is incorrect because mode imputation may introduce bias if missingness is not random.

Answer

Impute missing values with the mean of the feature.

Answer

Drop all rows with missing values.

Answer

Impute missing values with the mode of the feature.

Question 15

A data scientist needs to analyze a dataset stored in Amazon S3 as CSV files. The dataset contains 100 columns, and the data scientist wants to quickly understand the distribution of each column, including missing values, data types, and basic statistics. Which AWS service is best suited for this task?

Accepted Answer

AWS Glue DataBrew. Option A is correct because AWS Glue DataBrew provides visual data profiling and preparation without writing code. Option B is wrong because Amazon Athena is an interactive query service, not a profiling tool. Option C is wrong because Amazon QuickSight is for visualization, not data profiling. Option D is wrong because SageMaker Data Wrangler is for feature engineering within SageMaker, but DataBrew is simpler for initial exploration.

Answer

Amazon SageMaker Data Wrangler

Answer

Amazon QuickSight

Answer

Amazon Athena

Question 16

A data scientist is analyzing a dataset with missing values. The missing data is not random and is correlated with other features. Which imputation method is most appropriate to minimize bias?

Accepted Answer

Multiple imputation using MICE. Option B is correct because Multiple Imputation by Chained Equations (MICE) accounts for relationships between features and preserves variability. Option A is wrong because mean imputation can bias estimates when data is not missing completely at random. Option C is wrong because dropping rows reduces sample size and may introduce bias. Option D is wrong because last observation carried forward is for time series.

Answer

Last observation carried forward

Answer

Listwise deletion

Answer

Mean imputation

Question 17

A data scientist is analyzing a dataset with high multicollinearity. Which TWO techniques can help identify and address multicollinearity?

Accepted Answer

Use Principal Component Analysis (PCA). Correct options: A and D. VIF (A) quantifies multicollinearity; PCA (D) creates orthogonal components. Option B is wrong because correlation matrix only shows pairwise correlations. Option C is wrong because Lasso does feature selection but does not identify multicollinearity. Option E is wrong because RFE is for feature selection, not multicollinearity detection.

Answer

Plot a correlation matrix

Answer

Apply Lasso regression

Answer

Use Recursive Feature Elimination (RFE)

Question 18

During EDA, a data scientist finds that a feature has a skewness value of 2.5. What does this indicate about the data distribution?

Accepted Answer

The distribution is right-skewed. A skewness > 1 indicates a highly right-skewed (positive skew) distribution, meaning the tail extends to the right. Option A is wrong because left skew is negative. Option B is wrong because symmetric distributions have skewness near 0. Option D is wrong because skewness describes shape, not the presence of outliers specifically.

Answer

The distribution is symmetric

Answer

The distribution is left-skewed

Answer

The distribution has no outliers

Question 19

A data scientist is analyzing a dataset with a binary target variable. The dataset has 50,000 rows and 200 features. The data scientist wants to identify which features are most predictive. Which TWO methods are appropriate for feature selection during EDA?

Accepted Answer

Mutual information. Chi-squared test is for categorical features and mutual information can handle both. Option C is wrong because PCA is unsupervised. Option D is wrong because k-means is clustering. Option E is wrong because LSTM is a model, not for EDA.

Answer

LSTM neural network

Answer

K-means clustering

Answer

Principal Component Analysis (PCA)

Question 20

Refer to the exhibit. A data scientist plans to read this CSV file into memory for exploratory data analysis using pandas. The instance has 8 GB of RAM. What is the MOST likely issue the scientist will encounter?

Accepted Answer

The file is too large to load into memory on this instance. Option B is correct because the file size is approximately 1 GB (1073741824 bytes = 1 GB), and pandas typically requires 3-5x the file size in memory for CSV parsing, which would exceed the 8 GB RAM. Option A is wrong because the file is accessible (HTTP 200). Option C is wrong because the content type is text/csv, which is correct. Option D is wrong because the metadata indicates 10 million rows, but the file size is the main issue.

Answer

The file contains too many rows for pandas to handle

Answer

The file is not in CSV format despite the ContentType

Answer

The file is not accessible because of insufficient permissions