MLS-C01 Practice Exam 58 — 20 Questions

Question 1

A data scientist is exploring a dataset containing customer transactions. The dataset has a column 'transaction_amount' with values ranging from $0.01 to $10,000. Which EDA step is most appropriate to detect skewed distribution?

Accepted Answer

Create a histogram of transaction amounts. Option C is correct because a histogram or density plot reveals skewness visually. Option A is wrong because count plot is for categorical data. Option B is wrong because scatter plot shows relationship between two variables. Option D is wrong because heatmap shows correlation, not skewness.

Answer

Create a count plot of transaction amounts

Answer

Create a correlation heatmap of all numeric columns

Answer

Create a scatter plot of transaction amount vs. customer age

Question 2

A data scientist is analyzing a dataset with 500 features and 10,000 rows. The target variable is binary. After training a logistic regression model, the coefficients show many non-zero values but the model has low accuracy on the test set. Which EDA step should the data scientist perform next to improve model performance?

Accepted Answer

Use correlation analysis or mutual information to select the most relevant features.. Option B is correct because feature selection helps reduce noise and overfitting, improving model accuracy. Option A is wrong because scaling does not reduce the number of features. Option C is wrong because PCA may lose interpretability and is not directly aimed at reducing overfitting due to irrelevant features. Option D is wrong because more data does not necessarily address the issue of irrelevant features.

Answer

Apply Principal Component Analysis (PCA) to reduce dimensionality.

Answer

Collect more training data to improve generalization.

Answer

Normalize the features using StandardScaler.

Question 3

A data scientist is analyzing a dataset with missing values. Which technique is most appropriate for imputing missing values in a numerical feature that follows a normal distribution?

Accepted Answer

Mean imputation. Mean imputation is suitable for normally distributed data as it preserves the mean. Median is robust to outliers, not normality. Mode is for categorical data. Standard deviation is not an imputation method. KNN imputation is non-parametric.

Answer

Standard deviation imputation

Answer

Mode imputation

Answer

Median imputation

Question 4

Which THREE of the following are appropriate data visualization techniques for exploring the relationship between two numerical variables?

Accepted Answer

Scatter plot. Scatter plot, hexbin plot, and pair plot are designed for bivariate numerical relationships. Bar chart is for categorical. Box plot is for numerical vs categorical.

Answer

Box plot

Answer

Bar chart

Question 5

During EDA, a data scientist plots the distribution of a feature and sees a bimodal pattern. What does this likely indicate?

Accepted Answer

The data may contain two distinct groups.. Option C is correct because bimodal distribution often indicates two underlying subpopulations. Option A is wrong because missing values cause spikes, not bimodal. Option B is wrong because outliers cause tails, not two peaks. Option D is wrong because scaling does not create bimodality.

Answer

The feature has missing values.

Answer

The feature contains outliers.

Answer

The feature needs to be standardized.

Question 6

A machine learning engineer is analyzing a dataset with a large number of features (p >> n). The engineer suspects that many features are irrelevant. Which THREE methods are suitable for feature selection during exploratory data analysis? (Choose THREE.)

Accepted Answer

Remove features with variance below a threshold (e.g., <0.01). Option A is correct because correlation-based feature selection removes highly correlated features. Option C is correct because mutual information measures relevance to the target. Option E is correct because Variance Threshold removes low-variance features. Option B is wrong because PCA is a dimensionality reduction technique, not a feature selection method. Option D is wrong because Lasso regression is a modeling technique, not typically used in EDA.

Answer

Fit a Lasso regression model and select features with non-zero coefficients

Answer

Apply Principal Component Analysis (PCA) and select top components

Question 7

A data scientist is performing EDA on a dataset of customer churn. The dataset includes a categorical feature 'Region' with 100 unique values. What is the best way to encode this feature for a tree-based model?

Accepted Answer

Use the feature as a categorical variable directly in the tree-based model. Option C is correct because tree-based models can handle high-cardinality categorical features natively without encoding; many implementations (e.g., LightGBM, CatBoost) support categorical features directly. Option A is wrong because one-hot encoding creates 100 columns, causing sparsity. Option B is wrong because label encoding imposes ordinality. Option D is wrong because frequency encoding may cause target leakage if using target encoding without proper cross-validation.

Answer

Replace each category with its frequency in the dataset

Answer

Label encode the feature (assign integers 0-99)

Answer

One-hot encode the feature

Question 8

A team is exploring a dataset with missing values in multiple columns. They want to decide whether to drop rows or impute values. Which approach is most appropriate for exploratory data analysis?

Accepted Answer

Analyze the missing data pattern using visualizations and summary statistics. Option A is correct because during EDA, it is important to first understand the pattern and extent of missing data before deciding on treatment. Option B is wrong because dropping rows without analysis may discard valuable data. Option C is wrong because imputing without understanding the missing mechanism may introduce bias. Option D is wrong because EDA does not require using a specific AWS service.

Answer

Impute missing values with the mean of each column

Answer

Drop all rows with missing values to ensure data quality

Answer

Use Amazon SageMaker Data Wrangler to automatically impute missing values

Question 9

The exhibit shows an IAM policy for a SageMaker notebook. A data scientist wants to use the notebook to run an Athena query and then load the results into a pandas DataFrame. Which action is NOT possible with this policy?

Accepted Answer

Read the Athena query results from the output S3 location. Option C is correct because the policy allows s3:GetObject only on the 'my-training-data' bucket, but Athena writes results to a different S3 location (specified in the query configuration). Without permission to read from that output bucket, the notebook cannot load the results. Option A is wrong because Athena actions are allowed. Option B is wrong because listing the training data bucket is allowed. Option D is wrong because reading individual objects from the training data bucket is allowed.

Answer

Start an Athena query execution

Answer

Read a specific object from the my-training-data bucket

Answer

List objects in the my-training-data bucket

Question 10

A data scientist is performing EDA on a dataset with 100 features. They want to identify which features are most predictive of the target using a model-agnostic method. Which technique should they use?

Accepted Answer

Permutation feature importance. Option A is correct because permutation importance is model-agnostic and measures feature importance by shuffling. Option B is wrong because SHAP values are model-specific. Option C is wrong because L1 regularization is model-specific. Option D is wrong because correlation is bivariate.

Answer

Pearson correlation matrix

Answer

L1 regularization

Answer

SHAP values

Question 11

A machine learning engineer is evaluating a dataset for building a fraud detection model. The dataset has 1 million transactions, but only 500 are fraudulent. The engineer wants to understand the distribution of fraudulent vs. non-fraudulent transactions over time. Which EDA visualization is most suitable?

Accepted Answer

Line plot of daily fraud rate and non-fraud rate. Option D is correct because a time series line plot with two lines (fraud vs. non-fraud) shows temporal patterns. Option A is wrong because bar chart of counts per day is less effective for two categories. Option B is wrong because scatter plot with 1 million points is overwhelming. Option C is wrong because box plot shows distribution per time period but not temporal trend.

Answer

Bar chart of transaction count per day with colors for fraud status

Answer

Scatter plot of transactions over time colored by fraud status

Answer

Box plot of transaction amount per month grouped by fraud status

Question 12

An ML engineer is performing EDA on a dataset of customer transactions. The dataset has 1 million rows and 20 columns, including a 'transaction_amount' column. The engineer notices that 5% of the transaction amounts are negative, which are data entry errors. The rest are positive. Which approach is most appropriate for handling these negative values during EDA?

Accepted Answer

Remove rows with negative transaction amounts from the dataset.. Option D is correct because the negative values are errors and likely distort the distribution; removing them is straightforward and valid. Option A is wrong because taking absolute values would incorrectly treat errors as legitimate high values. Option B is wrong because negative values are not missing, so imputation is not appropriate. Option C is wrong because capping may still retain erroneous values.

Answer

Impute the negative values with the median of positive transaction amounts.

Answer

Take the absolute value of the negative transaction amounts.

Answer

Cap the negative values at zero.

Question 13

During EDA of a dataset for a regression problem, a data scientist notices that the target variable has a right-skewed distribution. Which THREE transformations are appropriate to address this skewness? (Choose THREE.)

Accepted Answer

Log transformation. Options A, C, and E are correct. Log transformation, Box-Cox, and Yeo-Johnson are common transformations for right-skewed data. Option B is wrong because StandardScaler only standardizes, does not reduce skewness. Option D is wrong because MinMax scaling does not affect skewness.

Answer

StandardScaler (z-score normalization)

Answer

Min-Max scaling

Question 14

A data analyst is using AWS Glue to catalog datasets for exploratory analysis. The analyst wants to understand the schema and data types. Which TWO tools can the analyst use to view the schema of a table in the AWS Glue Data Catalog? (Choose TWO.)

Accepted Answer

Amazon Athena. Option B is correct because Athena can query the INFORMATION_SCHEMA to view table schemas. Option D is correct because the Glue console displays table schemas directly. Option A is wrong because QuickSight is a visualization tool, not for schema viewing. Option C is wrong because S3 does not store schemas; it stores objects. Option E is wrong because Redshift returns data, not schema from Glue Catalog directly.

Answer

Amazon Redshift query editor

Answer

Amazon QuickSight

Answer

Amazon S3 console

Question 15

Which TWO of the following are appropriate techniques for detecting outliers in a univariate continuous dataset? (Select TWO.)

Accepted Answer

Z-score method. Options B and D are correct. IQR-based outlier detection identifies points beyond 1.5*IQR from quartiles. Z-score method flags points beyond a threshold (e.g., 3) from mean. Option A is wrong because clustering is multivariate. Option C is wrong because box plots visualize outliers but are not a detection technique per se; they use IQR. Option E is wrong because correlation is bivariate.

Answer

Box plot visualization

Answer

Pearson correlation coefficient

Answer

K-means clustering

Question 16

A data scientist is performing EDA on a large dataset (10 TB) stored in S3. They need to compute summary statistics for each column. Which approach is most cost-effective and efficient?

Accepted Answer

Use Amazon Athena with SQL queries. Option D is correct because Amazon Athena uses a serverless query engine that scales automatically and charges per query based on data scanned, making it cost-effective for large datasets. Option A is wrong because downloading to SageMaker Studio may incur high data transfer costs and require significant local storage. Option B is wrong because AWS Glue Spark jobs have overhead and are more suited for complex ETL. Option C is wrong because Amazon EMR requires provisioning clusters and is more expensive for simple statistics.

Answer

Use an AWS Glue ETL job with PySpark to compute statistics

Answer

Download the dataset to an Amazon SageMaker Studio notebook and use pandas

Answer

Launch an Amazon EMR cluster and use Spark SQL

Question 17

The exhibit shows a data quality report for a column named 'age'. Which potential data issue should be investigated further?

Accepted Answer

The minimum age of 0 and maximum age of 120 may be outliers. Option D is correct because an age of 0 and 120 are likely data entry errors and should be investigated. Option A is wrong because 2.3% missing is relatively low and may be acceptable. Option B is wrong because the mean and median are close. Option C is wrong because 85 unique values for age is reasonable.

Answer

The mean and median are significantly different

Answer

The missing value rate of 2.3% is too high

Answer

The number of unique values (85) is too high

Question 18

Which TWO statements about handling missing data during EDA are correct? (Select TWO.)

Accepted Answer

If data are missing completely at random (MCAR), listwise deletion yields unbiased estimates.. Options B and C are correct. Option A is wrong because MICE is multivariate imputation, not necessarily safest. Option D is wrong because listwise deletion can introduce bias. Option E is wrong because mean imputation reduces variance.

Answer

Dropping columns with >50% missing values is always recommended.

Answer

Mean imputation preserves the variance of the original distribution.

Answer

Multiple imputation (MICE) is always the safest method regardless of missing data mechanism.

Question 19

A data scientist is working with a dataset that has missing values in 30% of rows for a categorical feature 'city'. Which EDA step should be performed before deciding on imputation?

Accepted Answer

Check if missingness is related to other features or random. Option B is correct because analyzing the missing pattern (e.g., MCAR, MAR, MNAR) guides imputation strategy. Option A is wrong because mode imputation is a method, not a diagnostic step. Option C is wrong because label encoding is for modeling, not imputation decision. Option D is wrong because dropping rows may lose data; need to understand missingness first.

Answer

Impute missing values with the mode of the column

Answer

Drop all rows with missing values

Answer

Encode the city feature using label encoding

Question 20

A data scientist is exploring a dataset and wants to understand the distribution of a continuous feature. Which visualization is most appropriate for identifying skewness and potential outliers?

Accepted Answer

Box plot. Option C is correct because a box plot explicitly shows median, quartiles, and outliers. Option A is wrong because scatter plots show relationships between two variables, not distribution. Option B is wrong because bar charts are for categorical data. Option D is wrong because heatmaps show correlations, not distribution.

Answer

Bar chart

Answer

Scatter plot

Answer

Heatmap