MLS-C01 Exploratory Data Analysis Practice Test 7 — 15 Questions

Question 1

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 2

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 3

Which THREE are common techniques for detecting outliers in a univariate dataset? (Select THREE.)

Accepted Answer

Z-score. Options A, C, and D are correct. Option B is wrong because DBSCAN is a multivariate clustering method. Option E is wrong because Cook's distance is for regression diagnostics.

Answer

Cook's distance

Answer

DBSCAN clustering

Question 4

Which TWO are appropriate visualizations for exploring the distribution of a single numeric variable? (Select TWO.)

Accepted Answer

Histogram. Options B and D are correct. Option A is wrong because scatter plot is for two variables. Option C is wrong because heatmap shows correlation. Option E is wrong because bar chart is for categorical data.

Answer

Heatmap

Answer

Bar chart

Answer

Scatter plot

Question 5

A data scientist runs the above AWS CLI command and gets the output. The object size is 1 GB. They try to open the CSV file in Amazon Athena but get an error. What is the most likely cause?

Accepted Answer

The file exceeds the maximum CSV file size that Athena can query without partitioning. Option B is correct because Athena has a 100 MB per file limit for CSV queries without partitioning. Option A is wrong because gzip compression is supported. Option C is wrong because 1 GB is large but Athena can handle larger with proper partitioning. Option D is wrong because CSV is supported.

Answer

The file format is not supported by Athena

Answer

The file is not compressed with gzip

Answer

The file is too large for Athena to query at all

Question 6

A data scientist creates the above IAM policy and attaches it to a role used by an Amazon SageMaker notebook instance. When trying to save a file to the S3 bucket, the operation fails. What is the missing permission?

Accepted Answer

kms:GenerateDataKey. Option D is correct because SageMaker needs s3:PutObject, but also needs s3:GetObject for some operations. However, the error is likely due to missing encryption permissions. Option A is wrong because s3:ListBucket is for listing. Option B is wrong because kms:Decrypt is for reading. Option C is wrong because s3:GetObject is for reading.

Answer

kms:Decrypt

Answer

s3:ListBucket

Answer

s3:GetObject

Question 7

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 8

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 9

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 10

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 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

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 13

A team has a dataset with 500 features and wants to reduce dimensionality. During EDA, they compute the variance of each feature. Which finding would most likely lead to feature removal?

Accepted Answer

Some features have near-zero variance. Option B is correct because near-zero variance features provide little information and can be removed. Option A is wrong because high variance is often useful. Option C is wrong because high correlation between two features might warrant removal of one, but variance is not the direct indicator. Option D is wrong because negative covariance is still informative.

Answer

Some features have high correlation with each other

Answer

Some features have negative covariance with the target

Answer

Some features have very high variance

Question 14

A data scientist uses Amazon SageMaker Data Wrangler to explore a dataset. The target column is 'price' (continuous). Which EDA analysis would best help decide between linear regression and tree-based models?

Accepted Answer

Check linear relationships between features and target. Option A is correct because checking linearity (e.g., scatter plots of features vs. target) is fundamental for linear model assumptions. Option B is wrong because multicollinearity affects linear regression but not tree models. Option C is wrong because class imbalance is for classification. Option D is wrong because outlier detection is important but not the primary factor for model selection.

Answer

Compute variance inflation factor (VIF) for features

Answer

Detect outliers using Z-score

Answer

Identify class imbalance in the target

Question 15

A machine learning engineer is analyzing a text classification dataset with 50,000 documents. Which EDA step is most important to understand the vocabulary size and frequency distribution?

Accepted Answer

Plot frequency of each word in a bar chart. Option D is correct because word frequency distribution (e.g., zipfian) helps decide vocabulary cutoff. Option A is wrong because TF-IDF is a transformation, not EDA. Option B is wrong because document length distribution is about length, not vocabulary. Option C is wrong because bigram analysis is more advanced; basic frequency is first.

Answer

Compute TF-IDF matrix

Answer

Generate bigram collocations

Answer

Plot histogram of document lengths