MLS-C01 Exploratory Data Analysis Practice Test 17 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

AWS Glue. AWS Glue is a serverless ETL service that can be scheduled to run recurring jobs for data transformation, such as extracting day of week and month from a date column. Option A is wrong because Amazon Athena is an interactive query service, not an ETL scheduler. Option C is wrong because Amazon SageMaker is a machine learning platform, not an ETL service. Option D is wrong because AWS Lambda can run code on a schedule but is not a full-fledged ETL service for recurring transformations of large datasets.

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Amazon Athena

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Amazon SageMaker

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AWS Lambda

Question 2

A company stores sensor data in Amazon S3. A data scientist wants to explore the data using SQL without moving it. Which AWS service should they use?

Accepted Answer

Amazon Athena. Amazon Athena is the correct choice because it is a serverless interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL without any data movement or infrastructure management. Athena uses Presto under the hood and charges only for the data scanned per query, making it ideal for ad-hoc exploratory analysis on sensor data stored in S3.

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Amazon EMR

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Amazon Redshift

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Amazon QuickSight

Question 3

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. The most appropriate first step when encountering outliers beyond 3 standard deviations is to investigate their source before deciding on any treatment. Outliers may represent data entry errors, measurement issues, or genuinely rare but valid events that carry important signal. Understanding their origin informs whether to remove, transform, or retain them, aligning with best practices in exploratory data analysis (EDA).

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Use the feature as-is in the model

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Apply a log transformation to the feature

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Remove all rows with outlier values

Question 4

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 C and E are correct. Option C is correct because when data are Missing Completely at Random (MCAR), the missingness is independent of both observed and unobserved data, so listwise deletion (removing rows with missing values) does not introduce bias; the remaining sample is still a random subsample. Option E is correct because the median is not influenced by extreme values, making it a more robust imputation method compared to the mean, which can be skewed by outliers. Option A is incorrect because dropping columns with >50% missing values is not always recommended; it depends on the importance of the variable and the analysis goals. Option B is incorrect because mean imputation reduces the variance of the imputed variable, as it forces imputed values to the center. Option D is incorrect because Multiple Imputation by Chained Equations (MICE) is not always the safest; it assumes data are Missing at Random (MAR) and can be complex or inappropriate for other missingness mechanisms.

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

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Mean imputation preserves the variance of the original distribution.

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

Question 5

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

Accepted Answer

Z-score. The Z-score (C) is a standard univariate outlier detection technique that flags observations whose value deviates from the mean by more than a threshold number of standard deviations (commonly |z| > 3), directly operating on a single variable. The Interquartile range (IQR) method (D) is also a classic univariate approach: it computes Q1 and Q3, defines the bounds Q1 − 1.5·IQR and Q3 + 1.5·IQR, and marks points outside those fences as outliers. The Modified Z-score using median absolute deviation (E) is a robust univariate variant that replaces the mean and standard deviation with the median and MAD (typically using 0.6745·(x − median)/MAD), making it resistant to extreme values in a single variable. By contrast, Cook's distance (A) measures the influence of each observation on a regression model's fitted coefficients, so it is a multivariate/regression diagnostic rather than a univariate outlier test, and DBSCAN (B) is a density-based clustering algorithm that identifies outliers as noise points in a multi-dimensional feature space, not a univariate technique.

Answer

Cook's distance

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DBSCAN clustering

Question 6

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

Accepted Answer

Histogram. Options B and E are correct. A histogram displays the frequency distribution of a single numeric variable by binning the data, and a box plot shows the five-number summary (minimum, first quartile, median, third quartile, maximum) to visualize spread and outliers. Option A (heatmap) is typically used for two numeric variables to show density or correlation. Option C (bar chart) is for categorical data, not numeric distribution. Option D (scatter plot) visualizes the relationship between two numeric variables, not the distribution of one.

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Heatmap

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Bar chart

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Scatter plot

Question 7

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. (kms:GenerateDataKey) because the S3 bucket is likely encrypted with a KMS key. When SageMaker writes an object to an encrypted bucket, it needs permission to call kms:GenerateDataKey to generate a data key for encryption. Option A (kms:Decrypt) is for decryption, not encryption. Option B (s3:ListBucket) allows listing objects, not writing. Option D (s3:GetObject) allows reading objects, not writing. The error when saving indicates missing encryption permissions.

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kms:Decrypt

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s3:ListBucket

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s3:GetObject

Question 8

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). The correct query is D. It reads the @timestamp and @message fields, filters log entries containing '500' in the message, and then counts the number of such entries per minute using stats count() by bin(1m). This directly matches the requirement to count 500 errors per minute. Option A attempts to filter on a 'status' field, but the logs shown do not have a separate status field; the status code is embedded in the message. Option B filters for 'ERROR', but the logs use numeric status codes, not the word 'ERROR'. Option C sorts the results instead of aggregating, so it does not provide the count per minute.

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

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

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

Question 9

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. Plotting the histogram of the target variable is the most critical EDA step because it directly reveals the class imbalance (1% positive), which can severely impact model training and evaluation. Understanding the distribution helps in choosing appropriate techniques like resampling, class weights, or specialized metrics. Without this step, the data scientist might overlook the imbalance and build a model that predicts the majority class with high accuracy but poor recall for the minority class.

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Apply PCA and visualize the first two principal components

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Compute pairwise correlation matrix among all features

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Impute missing values using mean imputation

Question 10

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. Plotting learning curves, which show training and validation loss over epochs, is the correct EDA technique to detect overfitting. The divergence where training loss decreases but validation loss increases is a clear sign of overfitting. Option A (box plots) helps identify outliers but does not directly indicate overfitting. Option C (residual plots) checks for homoscedasticity in regression, not overfitting. Option D (confusion matrix) is used for classification, not regression.

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Create box plots of each feature to identify outliers

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Generate residual plots to check heteroscedasticity

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Plot confusion matrix on the validation set

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. A line plot of daily fraud rate and non-fraud rate directly visualizes how the proportion of fraudulent versus non-fraudulent transactions changes over time, which is exactly what the engineer wants to understand. Because fraud is only 0.05% of the data, plotting rates (rather than raw counts) makes the temporal trend visible instead of being swamped by the 999,500 legitimate transactions.

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

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Scatter plot of transactions over time colored by fraud status

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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. Before deciding on an imputation strategy, the data scientist must determine whether the missingness is random (MCAR/MAR) or systematic (MNAR), because this affects which imputation method is valid and whether imputation introduces bias. Checking if missingness correlates with other features or the target reveals the mechanism. This is a fundamental EDA step that informs all subsequent handling.

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Impute missing values with the mode of the column

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Drop all rows with missing values

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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. Features with near-zero variance carry almost no information — they are nearly constant across all observations, so they contribute little to distinguishing samples or predicting the target. Removing them reduces dimensionality with minimal information loss. This is the standard low-variance filter in feature selection.

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Some features have high correlation with each other

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Some features have negative covariance with the target

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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. Linear regression assumes a linear relationship between each feature and the continuous target, while tree-based models can capture nonlinearities and interactions without that assumption. Plotting or measuring the linearity between features and the continuous 'price' target directly informs whether a linear model is appropriate or whether a tree-based approach is warranted.

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Compute variance inflation factor (VIF) for features

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Detect outliers using Z-score

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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. Plotting the frequency of each word in a bar chart directly visualizes the vocabulary size and the frequency distribution — you can see how many unique words exist, which words dominate, and whether the distribution follows Zipf's law. This is the most direct EDA step for understanding vocabulary characteristics in a text corpus.

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Compute TF-IDF matrix

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Generate bigram collocations

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Plot histogram of document lengths