MLS-C01 Exploratory Data Analysis Practice Test 28 — 15 Questions

Question 1

A company runs a real-time fraud detection system using Amazon SageMaker. The model is deployed as a SageMaker endpoint and receives predictions within milliseconds. Recently, the model's accuracy has degraded due to data drift. The data scientists want to monitor the model's performance continuously. What is the most effective way to detect data drift?

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

Accepted Answer

Enable Amazon SageMaker Model Monitor to capture inference data and compare it against a baseline dataset. Amazon SageMaker Model Monitor can capture inference data and compare it against a baseline dataset to detect data drift automatically. Option A is wrong because storing data in S3 and using Athena for SQL queries is a batch-oriented approach that lacks automated drift detection capabilities. Option B is wrong because CloudWatch anomaly detection on invocation count and latency metrics monitors operational health, not data drift. Option D is wrong because CloudWatch Logs Insights can query inference logs but does not provide automated drift detection.

Answer

Store all incoming requests in Amazon S3 and use Amazon Athena to run periodic SQL queries for drift detection

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Set up Amazon CloudWatch anomaly detection on the endpoint's invocation count and latency metrics

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Use Amazon CloudWatch Logs Insights to analyze inference logs and set custom alarms

Question 2

A data scientist needs to analyze a dataset stored in Amazon S3 as CSV files. The dataset contains 100 columns, and the data scientist wants to quickly understand the distribution of each column, including missing values, data types, and basic statistics. Which AWS service is best suited for this task?

Accepted Answer

AWS Glue DataBrew. AWS Glue DataBrew is a visual data preparation service that profiles datasets and surfaces column statistics, data types, missing values, and distributions without writing code. It natively connects to S3 CSV sources and is designed for exactly this exploratory profiling task. This makes it the best fit for quickly understanding a 100-column dataset.

Answer

Amazon SageMaker Data Wrangler

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Amazon QuickSight

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Amazon Athena

Question 3

A data scientist is using Amazon SageMaker to train a model. The training dataset contains missing values in several features. The data scientist wants to impute missing values using the median of each feature. Which approach is most appropriate?

Accepted Answer

Compute the median of each feature on the training set only, then impute both training and test sets using that median. Computing the median on the training set only avoids data leakage, and applying that median to both training and test sets ensures consistent imputation without using test set information. Option A is incorrect because dropping rows with missing values discards potentially useful data and is not imputation. Option B is incorrect because computing the median on the entire dataset before splitting introduces data leakage, as the test set influences the imputation values. Option C is incorrect because imputing with zero is arbitrary and does not use the median; also, doing it before splitting would use the entire dataset, causing leakage.

Answer

Drop all rows that contain missing values

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Compute the median on the entire dataset, then split into training and test sets

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Impute missing values with zero for all features before splitting

Question 4

During exploratory data analysis, a data scientist notices that a feature has a highly skewed distribution. Which transformation is most likely to make the distribution approximately normal?

Accepted Answer

Log transformation. A log transformation compresses the right tail of a right-skewed distribution, pulling in extreme high values and making the distribution more symmetric and approximately normal. This is especially effective for positively skewed data that spans several orders of magnitude, such as income or population counts. Min-max scaling and standardization only linearly rescale the data, preserving the original skewness, while one-hot encoding is for categorical variables and does not apply to continuous skewed features.

Answer

Min-max scaling

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One-hot encoding

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Standardization (z-score)

Question 5

Which TWO actions are appropriate when handling missing data in a dataset for machine learning? (Select TWO.)

Accepted Answer

Use a machine learning model to predict missing values based on other features. Option A is correct because using a machine learning model to predict missing values based on other features (a technique known as model-based or regression imputation, e.g., k-NN or MICE) leverages correlations between features to produce more accurate estimates than simple statistics, making it an appropriate handling strategy. Option C is correct because imputing missing values with the mean or median of the feature is a standard, simple statistical imputation method that preserves the dataset size and is especially suitable for numerical features, with the median being robust to outliers. Option B is not appropriate because dropping all rows with any missing value can discard a large portion of the data and introduce bias, especially if missingness is not random. Option D is not appropriate because removing an entire feature with missing values discards potentially useful predictive information that could otherwise be recovered through imputation. Option E is not appropriate because filling missing values with zero is an arbitrary constant that can distort the feature's distribution and introduce false signal unless zero is a meaningful domain value.

Answer

Drop all rows that contain any missing value

Answer

Remove the feature entirely if it contains missing values

Answer

Fill missing values with zero

Question 6

Which THREE techniques are commonly used to detect outliers in a dataset? (Select THREE.)

Accepted Answer

Interquartile range (IQR). The Interquartile Range (IQR) method (A) is a standard statistical technique that flags outliers as points falling below Q1 − 1.5×IQR or above Q3 + 1.5×IQR, making it a classic univariate outlier detector. The Z-score (D) measures how many standard deviations a value lies from the mean, and points with |Z| above a threshold such as 3 are commonly treated as outliers. Isolation Forest (E) is a dedicated ensemble anomaly-detection algorithm that isolates observations via random partitioning, so outliers require fewer splits and receive shorter path lengths. k-means clustering (B) is a clustering algorithm, not an outlier-detection method, though it can be repurposed indirectly. Principal component analysis (C) is a dimensionality-reduction technique and does not by itself identify outliers.

Answer

k-means clustering

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Principal component analysis (PCA)

Question 7

A data scientist needs to understand the distribution of a continuous variable in a large dataset stored in Amazon S3. Which AWS service is most appropriate for quickly generating summary statistics and visualizations?

Accepted Answer

Amazon QuickSight. Amazon QuickSight is a business intelligence service that connects to data sources including Amazon S3 (via Athena or direct S3 manifest) and provides fast, interactive visualizations and summary statistics. It is designed for analysts to build dashboards and explore distributions without writing complex code. This makes it the most appropriate choice for generating visualizations of a continuous variable.

Answer

AWS Glue

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Amazon Athena

Answer

Amazon SageMaker Studio

Question 8

A data scientist is analyzing a dataset with missing values. The missing data is not random and is correlated with other features. Which imputation method is most appropriate to minimize bias?

Accepted Answer

Multiple imputation using MICE. MICE (Multiple Imputation by Chained Equations) models each variable with missing values as a function of the other variables, generating multiple plausible imputations that preserve relationships and reduce bias when missingness is correlated with other features (MAR/MNAR). It is the most appropriate choice because it leverages the correlation structure rather than ignoring it.

Answer

Last observation carried forward

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Listwise deletion

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Mean imputation

Question 9

A data scientist needs to detect outliers in a dataset with multiple features that follow different distributions. Which method is most robust for multivariate outlier detection?

Accepted Answer

Isolation Forest. Isolation Forest is an ensemble method that isolates anomalies effectively in high-dimensional spaces without assuming any specific distribution. Option A is wrong because Z-score assumes a normal distribution. Option B is wrong because IQR is univariate and does not capture multivariate interactions. Option C is wrong because DBSCAN is primarily a clustering algorithm and is not specifically designed for outlier detection, though it can identify outliers as noise; however, Isolation Forest is more robust for this purpose.

Answer

Z-score threshold

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Interquartile range (IQR)

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DBSCAN clustering

Question 10

A data scientist wants to understand the statistical relationship between two categorical variables in a dataset. Which test is most appropriate?

Accepted Answer

Chi-squared test. The chi-squared test is used to determine if there is a significant association between two categorical variables, which is exactly what the data scientist wants to understand. Option B (Pearson correlation coefficient) is incorrect because it measures linear relationship between two continuous variables. Option C (Student's t-test) is used to compare means of two groups, typically for continuous data. Option D (ANOVA) is used to compare means across three or more groups, also for continuous data.

Answer

Pearson correlation coefficient

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Student's t-test

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ANOVA test

Question 11

A data scientist is analyzing a dataset with a time series component. They suspect there is a weekly seasonality. Which technique should they use to confirm this?

Accepted Answer

Compute autocorrelation function (ACF). The autocorrelation function (ACF) measures the correlation between a time series and its lagged values. A significant spike at lag 7 confirms weekly seasonality. Option A (line chart) is subjective and not a definitive test. Option C (Fourier transform) identifies frequency components but is more complex and less direct for confirming seasonality. Option D (moving average) smooths the series and may obscure seasonality.

Answer

Plot the time series line chart

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Perform Fourier transform

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Compute a 7-day moving average

Question 12

A data scientist is performing exploratory data analysis on a dataset with missing values. Which TWO approaches are appropriate for handling missing data in a way that retains as much data as possible?

Accepted Answer

Use a model that handles missing values natively, such as XGBoost. Option A is correct because algorithms like XGBoost implement a sparsity-aware split-finding method that learns a default direction for missing values at each tree node, so no rows or columns need to be discarded and all available data is retained. Option C is correct because median imputation replaces missing entries with the column's median, preserving every row and column while remaining robust to outliers and skew, which suits exploratory data analysis. Option B does not belong because substituting a sentinel like -999 distorts the feature's distribution and can be misinterpreted as a real numeric value by many models. Option D does not belong because dropping columns with a high missing percentage discards potentially useful features and the data they do contain. Option E does not belong because dropping every row with any missing value (listwise deletion) removes the most data and can introduce serious bias.

Answer

Replace missing values with a constant like -999

Answer

Drop columns with high missing percentage

Answer

Drop rows with any missing value

Question 13

A data scientist is exploring a dataset with skewed numerical features. Which THREE transformations can help make the features more normally distributed?

Accepted Answer

Yeo-Johnson transformation. Yeo-Johnson (C) is correct because it is a power-family transformation that handles both positive and negative values and estimates a lambda parameter to reduce skewness and approximate normality. Box-Cox (D) is correct because it is the classic power transformation that stabilizes variance and normalizes positively skewed data by optimizing lambda, though it requires strictly positive inputs. Log transformation (E) is correct because applying log (or log1p for zeros) compresses right-skewed distributions and pulls in extreme high values, making the feature more symmetric and normal-like. Min-max scaling (A) only rescales values linearly to a fixed range and does not change the shape or skewness of the distribution. Standardization (B) only centers to mean 0 and scales to unit variance, preserving the original skewness rather than correcting it.

Answer

Min-max scaling

Answer

Standardization (Z-score)

Question 14

A data scientist is analyzing a dataset with many features and wants to identify which features are most correlated with the target variable. Which EDA technique should be used?

Accepted Answer

Correlation matrix. A correlation matrix displays pairwise Pearson correlation coefficients between all numeric features and the target variable, enabling quick identification of the most correlated features. Option A (box plots grouped by target) is useful for visualizing feature distributions across target categories but does not directly measure correlation strength. Option B (scatter plot matrix) can show pairwise relationships but becomes impractical with many features and lacks a single quantitative correlation measure. Option C (histogram) only shows the distribution of a single feature, not its relationship with the target. Therefore, the correlation matrix is the appropriate EDA technique for identifying features most correlated with the target.

Answer

Box plots grouped by target

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Scatter plot matrix

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Histogram of each feature

Question 15

A data scientist is performing EDA on a time series dataset of daily sales. The data scientist observes a pattern that repeats every 7 days. Which characteristic of the time series is being observed?

Accepted Answer

Seasonality. Seasonality refers to periodic fluctuations in a time series that repeat at fixed intervals, such as daily, weekly, or yearly. In this case, the pattern repeats every 7 days, which is a classic example of weekly seasonality. This is distinct from other time series characteristics like trend (long-term increase/decrease) or autocorrelation (correlation with lagged values).

Answer

Stationarity

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Autocorrelation

Answer

Trend