MLS-C01 Exploratory Data Analysis Practice Test 8 — 15 Questions

Question 1

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 2

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 is to investigate their source (Option D). Outliers could indicate data entry errors, measurement issues, or genuine rare events. Blindly removing them (Option C) or transforming them (Option B) without understanding the cause may distort analysis. Using the feature as-is (Option A) can bias models if outliers are erroneous. Investigation should precede any action.

Answer

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 3

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. Permutation feature importance is the correct model-agnostic method because it measures the increase in prediction error after permuting a feature's values, breaking the relationship with the target, and works with any model. Pearson correlation (A) is bivariate and only captures linear relationships. L1 regularization (B) is model-specific to linear models and embeds feature selection within the model. SHAP values (C) are model-specific as they rely on game theory and require model outputs for calculation.

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Pearson correlation matrix

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L1 regularization

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SHAP 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. Options C, D, and E are correct. Z-score (C) identifies outliers by standard deviations from mean, IQR method (D) uses quartiles to detect outliers beyond 1.5×IQR, and Modified Z-score using MAD (E) is a robust alternative. Cook's distance (A) is a regression diagnostic for influential points, not univariate outlier detection. DBSCAN (B) is a multivariate clustering algorithm.

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 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. Amazon Athena has a default limit of 100 MB per CSV file when querying without partitioning. A 1 GB file exceeds this limit, causing an error. Option A is wrong because CSV is a supported file format in Athena. Option C is wrong while gzip compression is supported, it is not required; the issue is file size, not compression. Option D is wrong because Athena can query large files, but only if they are properly partitioned or if the file size is within the per-file limit.

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The file format is not supported by Athena

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The file is not compressed with gzip

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The file is too large for Athena to query at all

Question 8

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 9

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.

Answer

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 10

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. The most critical EDA step for a highly imbalanced target variable is to examine its distribution. Therefore, plotting the histogram of the target variable (D) reveals the imbalance and guides decisions on resampling or evaluation metrics. Option A (PCA) is primarily for dimensionality reduction and not essential for understanding target balance. Option B (correlation matrix) examines feature relationships, not target distribution. Option C (mean imputation) addresses missing values, which is important but not the most critical for handling class imbalance.

Answer

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 11

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 12

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 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.

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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 13

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 imputation for the 'city' feature, the first exploratory data analysis (EDA) step is to investigate the pattern of missingness. Option A is correct because you must determine whether the missing data are Missing Completely at Random (MCAR), Missing at Random (MAR), or Missing Not at Random (MNAR). This involves checking if missingness in 'city' is related to other features or is random. Understanding the missing mechanism informs the appropriate imputation strategy. Option B (impute with mode) is an imputation method, not a diagnostic step; applying it without prior analysis risks introducing bias. Option C (drop rows) may be valid only if missingness is MCAR and the amount of data loss is acceptable, but it should not be the first step. Option D (label encoding) transforms categorical data and does not address missing values.

Answer

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 14

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 have little to no information content and are often redundant for modeling. Removing them reduces dimensionality without significant loss. Option A is incorrect: high correlation between features suggests multicollinearity, but variance is not the direct measure; correlation is addressed by other techniques like PCA. Option B is incorrect: negative covariance with the target indicates an inverse relationship, which can be informative. Option C is incorrect: high variance often indicates useful information, though it may warrant scaling; it is not a reason for removal.

Answer

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 15

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. Checking linear relationships (e.g., scatter plots of features vs. target) helps determine whether linear regression is appropriate or if tree-based models (which capture non-linear patterns) would perform better. Option A (VIF) is used to detect multicollinearity, which affects linear regression but does not directly guide model selection between linear and tree models. Option C (Z-score) identifies outliers, which is important but not the primary factor for deciding between these model types. Option D (class imbalance) is relevant for classification problems, not regression.

Answer

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