MLS-C01 Practice Exam 57 — 20 Questions

Question 1

A machine learning engineer is performing exploratory data analysis on a dataset containing customer transaction records. The dataset includes a column 'transaction_date' with timestamps. The engineer wants to derive features such as day of the week, hour, and month for modeling. Which AWS service can be used directly to extract these features without writing custom code?

Accepted Answer

AWS Glue ETL with built-in timestamp transforms. Option B is correct because AWS Glue provides built-in transforms in its ETL jobs to parse timestamps and extract date/time components. Option A is wrong because Athena is a query engine and can extract date parts using SQL, but that requires writing SQL queries, not a no-code solution. Option C is wrong because SageMaker Data Wrangler is a visual tool that can create features, but it requires a SageMaker Studio environment. Option D is wrong because QuickSight is a visualization tool, not for feature engineering.

Answer

Amazon Athena with SQL date functions

Answer

Amazon QuickSight

Answer

Amazon SageMaker Data Wrangler

Question 2

A data scientist is exploring a dataset with 500 features and 100,000 observations for a regression problem. The scientist notices that many features are highly correlated with each other. Which technique should the scientist use to reduce multicollinearity and improve model interpretability during exploratory data analysis?

Accepted Answer

Calculate Variance Inflation Factor (VIF) for each feature and remove those with VIF > 10.. Option A is correct because Variance Inflation Factor (VIF) is a standard metric to detect multicollinearity, and features with high VIF can be removed. Option B is wrong because PCA creates new features that are linear combinations, reducing interpretability. Option C is wrong because Lasso can be used for feature selection but is a modeling step, not exploratory analysis. Option D is wrong because mutual information measures dependency but not specifically multicollinearity.

Answer

Compute mutual information between each feature and the target, and keep only the top 50 features.

Answer

Apply Principal Component Analysis (PCA) to reduce the feature space.

Answer

Use Lasso regression to select features with non-zero coefficients.

Question 3

A machine learning team is analyzing a dataset with a target variable that is highly imbalanced (99% negative class, 1% positive class). They want to understand the distribution and relationships before modeling. Which exploratory data analysis technique is most appropriate to visualize the imbalance and guide resampling strategy?

Accepted Answer

Bar chart of class frequencies and a correlation heatmap. Option D is correct because a bar chart of class counts clearly shows the imbalance, and a correlation heatmap helps understand feature relationships with the target. Option A is wrong because a scatterplot matrix is for continuous variables, not for a binary target. Option B is wrong because box plots show distribution of continuous features by class, but not the imbalance itself. Option C is wrong because a confusion matrix is for model evaluation, not for initial data exploration.

Answer

Confusion matrix on a sample of the data

Answer

Scatterplot matrix of all features colored by class

Answer

Box plots of each feature grouped by the target class

Question 4

A data analyst is using Amazon SageMaker Studio to perform exploratory data analysis on a dataset stored in S3. The analyst wants to generate summary statistics and visualizations quickly. Which built-in feature of SageMaker Studio should the analyst use?

Accepted Answer

SageMaker Data Wrangler. Option C is correct because SageMaker Data Wrangler is a visual data preparation tool integrated into SageMaker Studio that provides summary statistics, histograms, and correlation matrices without code. Option A is wrong because SageMaker Autopilot automates model building, not EDA. Option B is wrong because SageMaker Clarify is for bias detection and explainability. Option D is wrong because SageMaker Ground Truth is for labeling.

Answer

SageMaker Ground Truth

Answer

SageMaker Autopilot

Answer

SageMaker Clarify

Question 5

A data scientist is working with a dataset containing geospatial coordinates (latitude and longitude) of customer locations. The scientist wants to engineer features such as distance to the nearest store, and cluster customers into regions. Which AWS service is best suited for performing geospatial analysis and clustering during exploratory data analysis?

Accepted Answer

Amazon SageMaker with custom Python scripts using scikit-learn and Geopy. Option B is correct because Amazon SageMaker provides built-in algorithms like K-Means for clustering, and the scientist can use custom code with libraries like Geopy to compute distances. Option A is wrong because Amazon Athena with PostGIS is for querying geospatial data, not for clustering. Option C is wrong because Amazon Location Service is for maps and location tracking, not for analytical clustering. Option D is wrong because AWS Glue is for ETL, not for analysis and clustering.

Answer

Amazon Athena with PostGIS extensions

Answer

AWS Glue with geospatial transforms

Answer

Amazon Location Service

Question 6

A data scientist is analyzing a dataset with 10,000 rows and 50 columns. The target variable is binary. Which technique is most appropriate for identifying the most important features for predicting the target?

Accepted Answer

Train a Random Forest classifier and use feature_importances_. Option A is correct because Random Forest feature importance is a well-known method for ranking features in classification tasks. Option B is wrong because PCA is unsupervised and does not use the target. Option C is wrong because K-means is clustering, not feature selection. Option D is wrong because t-SNE is for visualization, not feature importance.

Answer

Use t-SNE to reduce dimensionality and inspect clusters

Answer

Run K-means clustering and examine cluster centroids

Answer

Apply PCA and select components with highest variance

Question 7

A machine learning engineer is analyzing a dataset with a mix of categorical and numerical features. The engineer wants to understand the correlation between categorical features and the target variable. Which statistical test is most appropriate for measuring association between a categorical feature and a binary target?

Accepted Answer

Chi-squared test of independence. Option C is correct because the Chi-squared test of independence is used to determine if there is a significant association between two categorical variables, which is applicable here. Option A is wrong because Pearson correlation is for continuous variables. Option B is wrong because ANOVA is for comparing means across groups, but assumes continuous target. Option D is wrong because Mutual Information can be used but is not a statistical test with a p-value.

Answer

Pearson correlation coefficient

Answer

ANOVA (Analysis of Variance)

Answer

Mutual information

Question 8

A data analyst is exploring a dataset and wants to identify outliers in a numerical feature. Which visualization technique is most effective for detecting outliers?

Accepted Answer

Box plot. Option A is correct because a box plot explicitly shows quartiles and potential outliers as points beyond the whiskers. Option B is wrong because a histogram shows distribution but outliers may be in low-frequency bins. Option C is wrong because a scatter plot shows relationship between two variables, not univariate outliers. Option D is wrong because a line chart is for time series.

Answer

Line chart

Answer

Histogram

Answer

Scatter plot

Question 9

A team is performing exploratory data analysis on a dataset containing 10 million records stored in Amazon S3. They want to sample the data efficiently to build a representative subset for initial modeling. Which sampling method should they use to minimize bias and ensure the sample reflects the population distribution?

Accepted Answer

Stratified random sampling. Option D is correct because stratified random sampling ensures that each subgroup (stratum) is proportionally represented, which is important for imbalanced data. Option A is wrong because simple random sampling may miss rare subgroups. Option B is wrong because systematic sampling can introduce bias if there is periodicity. Option C is wrong because reservoir sampling is for streaming data, not for static datasets.

Answer

Simple random sampling

Answer

Systematic sampling

Answer

Reservoir sampling

Question 10

A data scientist is exploring a dataset and finds that the variance of a feature is 0. What should be done with this feature?

Accepted Answer

Remove the feature from the dataset. Option C is correct because zero variance means the feature is constant and provides no information for modeling; it should be removed. Option A is wrong because scaling does not change constant values. Option B is wrong because imputation is for missing values, not constant. Option D is wrong because interaction with a constant feature remains constant.

Answer

Create interaction terms with other features

Answer

Apply Min-Max scaling to normalize the feature

Answer

Impute missing values using the mean

Question 11

A company stores customer transaction data in Amazon S3. A data scientist needs to perform exploratory data analysis using Amazon SageMaker. The dataset is 500 GB in CSV format. Which approach is most cost-effective and time-efficient for initial data profiling?

Accepted Answer

Use Amazon S3 Select to sample rows directly from S3. Option D is correct because Amazon S3 Select can query a subset of rows from S3 without loading the entire dataset, enabling quick profiling. Option A is wrong because loading full data is expensive and slow. Option B is wrong because Glue ETL processes full dataset. Option C is wrong because converting to Parquet adds overhead for initial profiling.

Answer

Load the entire dataset into a SageMaker notebook instance and use pandas

Answer

Convert the data to Parquet format and then use Athena to query

Answer

Use AWS Glue ETL to transform the data and then analyze in Athena

Question 12

A data scientist is analyzing a dataset and notices that the distribution of a continuous feature is heavily right-skewed. Which transformation is most likely to make the distribution more symmetric?

Accepted Answer

Log transformation (natural log). Option A is correct because log transformation is commonly used to reduce right skewness. Option B is wrong because square transformation increases skewness. Option C is wrong because Min-Max scaling does not change shape. Option D is wrong because one-hot encoding is for categorical features.

Answer

Min-Max scaling

Answer

One-hot encoding

Answer

Square transformation

Question 13

A data scientist receives the above error during model training. What is the most likely cause?

Accepted Answer

The training data contains missing or infinite values.. Option B is correct. The error indicates NaN or infinite values in the input data. Option A is wrong because the error is about data, not hyperparameters. Option C is wrong because the error is not about memory. Option D is wrong because the error is not about data format.

Answer

The learning rate is too high.

Answer

The data format is incorrect; expected CSV but received JSON.

Answer

The instance type lacks sufficient memory.

Question 14

A data scientist is performing exploratory data analysis on a dataset containing customer transactions. The dataset has a column 'transaction_date' with timestamps in string format. Which AWS service can be used to parse the timestamps and extract features like day of week and hour?

Accepted Answer

AWS Glue. Option C is correct because AWS Glue provides built-in transformations to parse timestamps and extract date/time features. Option A is wrong because Amazon Athena is a query service, not a transformation service. Option B is wrong because Amazon SageMaker Studio is an IDE, not a data transformation service. Option D is wrong because AWS Data Pipeline is a workflow orchestration service, not a timestamp parsing tool.

Answer

Amazon Athena

Answer

Amazon SageMaker Studio

Answer

AWS Data Pipeline

Question 15

A machine learning engineer is analyzing a dataset with high cardinality categorical features. They want to reduce the number of categories by grouping rare categories into an 'Other' category. Which Amazon SageMaker processing job capability is best suited for this task?

Accepted Answer

Amazon SageMaker Processing. Option D is correct because Amazon SageMaker Processing jobs can run custom scripts that can handle high cardinality categorical features using libraries like pandas. Option A is wrong because SageMaker Data Wrangler is a visual tool, but it may not be as flexible for custom grouping logic. Option B is wrong because SageMaker Autopilot is for automated ML, not for custom data processing. Option C is wrong because AWS Glue Studio is a visual ETL tool, but it may not be as tightly integrated with SageMaker.

Answer

Amazon SageMaker Data Wrangler

Answer

AWS Glue Studio

Answer

Amazon SageMaker Autopilot

Question 16

A data scientist is using Amazon SageMaker to perform exploratory data analysis on a dataset with missing values and outliers. Which TWO actions should the scientist take to understand the data quality? (Choose TWO.)

Accepted Answer

Use histograms to visualize the distribution of each numerical feature. Option A is correct because generating summary statistics helps identify missing counts and outliers via min/max. Option D is correct because visualizing distributions with histograms helps spot outliers and skewness. Option B is wrong because a correlation matrix does not directly show missing values or outliers. Option C is wrong because a confusion matrix is for classification models, not for data exploration. Option E is wrong because a scatterplot matrix shows pairwise relationships, not missing values.

Answer

Build a scatterplot matrix to visualize pairwise relationships

Answer

Plot a confusion matrix to assess class separation

Answer

Create a correlation matrix to identify redundant features

Question 17

During EDA, a data scientist finds that two features have a Pearson correlation coefficient of 0.95. What is the primary concern when using these features together in a linear regression model?

Accepted Answer

Multicollinearity will make coefficient estimates unstable. Option B is correct because high correlation indicates multicollinearity, which can destabilize coefficient estimates in linear regression. Option A is wrong because overfitting is not directly caused by correlation. Option C is wrong because high correlation does not cause underfitting. Option D is wrong because heteroscedasticity is unrelated to correlation.

Answer

The model will underfit because of redundant information

Answer

Heteroscedasticity will be introduced

Answer

The model will overfit due to redundant features

Question 18

A data analyst needs to visualize the distribution of a numerical feature in a dataset. Which AWS service can be used to create a histogram directly from data stored in S3 without writing code?

Accepted Answer

Amazon QuickSight. Option B is correct because Amazon QuickSight is a BI service that can connect to S3 data and create histograms without coding. Option A is wrong because Amazon SageMaker Studio requires code or notebook. Option C is wrong because Amazon Athena outputs query results, not visualizations directly. Option D is wrong because AWS Glue is for ETL, not visualization.

Answer

Amazon Athena

Answer

Amazon SageMaker Studio

Answer

AWS Glue

Question 19

The exhibit shows the result of an Athena query. What does the value '5000' represent?

Accepted Answer

The number of rows where col1 is NULL. Option B is correct because the query counts rows where col1 IS NULL, and the result '5000' is that count. Option A is wrong because the query is not selecting distinct values. Option C is wrong because the query counts NULL rows, not non-NULL. Option D is wrong because total row count was not queried.

Answer

The total number of rows in the table

Answer

The number of rows where col1 is not NULL

Answer

The number of distinct values in col1

Question 20

During EDA, a data scientist notices that a numeric feature 'age' has outliers beyond 3 standard deviations. What is the most appropriate first step?

Accepted Answer

Investigate the source of the outliers. Option C is correct because investigating the outliers helps determine if they are data errors or valid. Option A is wrong because removing without investigation may lose valuable data. Option B is wrong because winsorizing is a transformation, not first step. Option D is wrong because modeling with noise is not prudent.

Answer

Use the feature as-is in the model

Answer

Apply a log transformation to the feature

Answer

Remove all rows with outlier values