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CCNA Exploratory Data Analysis Questions

75 of 381 questions · Page 1/6 · Exploratory Data Analysis · Answers revealed

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1
MCQeasy

A machine learning engineer is exploring a dataset with 500 features and 10,000 samples. To reduce dimensionality for visualization, which technique is most suitable if the goal is to preserve global data structure?

A.t-Distributed Stochastic Neighbor Embedding (t-SNE)
B.Locally Linear Embedding (LLE)
C.Principal Component Analysis (PCA)
D.Uniform Manifold Approximation and Projection (UMAP)
AnswerC

PCA preserves global variance (covariance structure).

Why this answer

PCA is the most suitable technique for preserving the global data structure when reducing dimensionality because it is a linear method that maximizes variance along orthogonal principal components, capturing the overall covariance structure of the 500 features. Unlike nonlinear methods, PCA ensures that the global relationships (e.g., distances between clusters) are retained, making it ideal for visualization of high-dimensional data where the goal is to see broad patterns.

Exam trap

The MLS-C01 exam often tests the misconception that nonlinear methods like t-SNE or UMAP are always better for visualization, but the trap here is that they sacrifice global structure for local detail, making PCA the correct choice when the question explicitly states 'preserve global data structure.'

How to eliminate wrong answers

Option A is wrong because t-SNE is a nonlinear technique that focuses on preserving local neighborhoods and pairwise similarities, often distorting global structure (e.g., cluster sizes and distances) to create visually separable clusters. Option B is wrong because LLE is a nonlinear manifold learning method that preserves local linear relationships between neighbors, but it does not guarantee preservation of global structure and can fail with high-dimensional data (500 features) due to the curse of dimensionality. Option D is wrong because UMAP, while faster than t-SNE, is also a nonlinear technique designed to preserve local and some global structure but prioritizes topological connectivity over global variance, making it less suitable than PCA when the explicit goal is to maintain the overall data covariance and global distances.

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2
MCQhard

A machine learning team is working with a dataset containing high-dimensional sparse features, such as text data represented as bag-of-words. The team wants to reduce dimensionality while preserving the structure of the sparse matrix. Which technique is most appropriate for this scenario?

A.t-distributed Stochastic Neighbor Embedding (t-SNE).
B.Truncated Singular Value Decomposition (SVD).
C.Linear Discriminant Analysis (LDA).
D.Principal Component Analysis (PCA) using the covariance matrix.
AnswerB

Truncated SVD works efficiently on sparse matrices.

Why this answer

Truncated SVD (e.g., using sklearn's TruncatedSVD) is specifically designed for sparse matrices and efficiently reduces dimensionality while preserving the structure of the sparse matrix. Option A (t-SNE) is primarily for visualization and does not preserve the global structure well for dimensionality reduction. Option C (LDA) is a supervised method that requires labels and is not suitable for unsupervised dimensionality reduction.

Option D (PCA using covariance matrix) requires a dense matrix and is computationally expensive for high-dimensional sparse data.

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3
MCQhard

During exploratory data analysis, a data scientist discovers that a feature has a variance of 0.01, while other features have variances around 1.0. Which action should be taken?

A.Scale the feature to have unit variance.
B.Apply a log transformation to the feature.
C.Impute missing values in the feature.
D.Consider removing the feature or applying variance threshold.
AnswerD

Near-zero variance features are often uninformative.

Why this answer

A feature with near-zero variance (0.01) compared to others (~1.0) likely has little predictive power and can cause numerical instability in models. Variance thresholding is a standard preprocessing step to remove low-variance features. Option A is wrong: scaling to unit variance does not address the fundamental issue of low information content; it merely changes the scale.

Option B is wrong: log transformation changes distribution shape but does not meaningfully increase variance; the variance will remain low. Option C is wrong: imputation is for missing values, which is unrelated to low variance.

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4
MCQmedium

Refer to the exhibit. A data scientist is using an IAM role with this policy to run a SageMaker processing job that reads data from S3. The job fails with an access error. What is the most likely cause?

A.The policy does not allow sagemaker:CreateProcessingJob
B.The policy does not allow s3:PutObject
C.The policy does not allow s3:ListBucket
D.The policy does not allow s3:GetObject
AnswerC

ListBucket is required to list objects in the bucket.

Why this answer

The processing job needs both s3:GetObject and s3:ListBucket to read objects. The policy lacks s3:ListBucket. Option A is wrong because sagemaker:CreateProcessingJob is allowed.

Option B is wrong because s3:PutObject is not needed for reading. Option D is wrong because the policy allows s3:GetObject.

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5
MCQeasy

A machine learning engineer is working on a regression problem to predict house prices. The dataset contains 500,000 rows and 20 features, including 'sqft_living', 'bedrooms', 'bathrooms', 'floors', 'waterfront', 'view', 'condition', 'grade', 'yr_built', 'zipcode', and 'lat'. After performing exploratory data analysis, the engineer notices that the 'sqft_living' feature has a right-skewed distribution with a long tail. The 'zipcode' feature is categorical with 70 unique values. The 'lat' feature is continuous. The engineer wants to prepare the data for a linear regression model. Which action should the engineer take to improve model performance?

A.Remove the 'sqft_living' feature because it violates the normality assumption.
B.Apply a log transformation to the 'sqft_living' feature.
C.One-hot encode the 'zipcode' feature to capture location effects.
D.Apply standard scaling (z-score) to the 'sqft_living' feature.
AnswerB

Log transformation reduces right skewness, making the distribution more symmetric.

Why this answer

Linear regression assumes that features are approximately normally distributed, and a right-skewed distribution like 'sqft_living' can violate this assumption, leading to poor model performance. Applying a log transformation compresses the long tail, making the distribution more symmetric and helping the model learn a linear relationship between the feature and the target. This is a standard preprocessing step for skewed features in regression tasks.

Exam trap

The MLS-C01 exam often tests the misconception that standard scaling (z-score) can fix skewness, when in reality it only normalizes the mean and variance without altering the shape of the distribution.

How to eliminate wrong answers

Option A is wrong because removing the 'sqft_living' feature outright discards valuable information; linear regression does not require strict normality of features, only that residuals are normally distributed, and skewness can be addressed via transformation. Option C is wrong because one-hot encoding 'zipcode' with 70 unique values would create 69 dummy variables, which is acceptable but not the most impactful action for improving model performance given the stated issue of skewness in 'sqft_living'. Option D is wrong because standard scaling (z-score) only centers and scales the data, which does not address right skewness; it would preserve the long tail and fail to make the distribution more normal.

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6
MCQmedium

Refer to the exhibit. A data scientist is using AWS Glue ETL jobs to process data from a source database. The job logs show repeated timeout errors. Which EDA step should the scientist perform to diagnose the issue?

A.Test network connectivity from the Glue job to the source database using telnet.
B.Check the source database table sizes and row counts over time.
C.Switch the Glue ETL job type from Spark to Python shell to reduce overhead.
D.Increase the Glue job timeout to 600 seconds and rerun.
AnswerB

Identifies if data volume growth causes timeouts.

Why this answer

The timeout errors indicate that the Glue ETL job is exceeding its configured timeout. To diagnose the root cause, the data scientist should check the source database table sizes and row counts over time (option B). This helps determine if the data volume has increased, causing longer processing times.

Option A (testing network connectivity) addresses network issues but not processing delays. Option C (switching job type) may change performance but does not diagnose the cause. Option D (increasing timeout) is a temporary workaround, not a diagnostic step.

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7
MCQmedium

A data scientist is analyzing a dataset of customer reviews for a retail company. The dataset contains text reviews, star ratings (1-5), and customer metadata. The scientist wants to perform sentiment analysis to classify reviews as positive or negative. During EDA, the scientist uses Amazon SageMaker Data Wrangler to visualize the distribution of star ratings and notices that 90% of reviews are 4 or 5 stars, while only 2% are 1 star. The scientist is concerned about class imbalance. Which approach should the scientist take to address the imbalance before modeling?

A.Downsample the majority class to create a balanced dataset.
B.Use random oversampling of the minority class to balance the dataset.
C.Use accuracy as the evaluation metric since it is simple.
D.Use the F1-score as the evaluation metric to account for imbalance.
AnswerD

F1-score balances precision and recall, appropriate for imbalanced classes.

Why this answer

The F1-score balances precision and recall, making it a suitable metric for imbalanced datasets where accuracy would be misleading due to the majority class dominating. Option A (downsampling) is not ideal because it discards potentially useful data from the majority class. Option B (random oversampling) can lead to overfitting by duplicating minority class instances, and is not a guaranteed solution.

Option C is incorrect because accuracy is not reliable when classes are imbalanced, as high accuracy can be achieved by simply predicting the majority class.

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8
MCQhard

A data scientist is using Amazon SageMaker Data Wrangler to perform exploratory data analysis on a dataset. The dataset contains a feature 'age' with values ranging from 0 to 120. The data scientist wants to detect outliers. Which built-in transform in Data Wrangler is most appropriate for this task?

A.Handle Outliers
B.Scale and Normalize
C.Handle Missing
D.Encode Categorical
AnswerA

This transform includes outlier detection methods such as IQR and z-score.

Why this answer

The 'Handle Outliers' transform in Amazon SageMaker Data Wrangler provides built-in methods such as IQR (Interquartile Range) and z-score to detect and handle outliers in numeric features like 'age'. Option B (Scale and Normalize) is used to rescale features but does not detect outliers. Option C (Handle Missing) deals with missing values, not outliers.

Option D (Encode Categorical) is for converting categorical variables to numerical, not for outlier detection.

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9
MCQhard

A data scientist is performing exploratory data analysis on text data. They want to identify the most common terms and their frequencies. Which approach should they use?

A.Perform sentiment analysis on the text.
B.Apply Latent Dirichlet Allocation (LDA) to extract topics.
C.Create a bag-of-words matrix and compute term frequencies.
D.Use word2vec to generate word embeddings.
AnswerC

Bag-of-words directly provides term counts.

Why this answer

A bag-of-words matrix counts the frequency of each term in the text, directly providing the most common terms and their frequencies. Option A is incorrect because sentiment analysis determines the emotional tone of text, not term frequencies. Option B is incorrect because Latent Dirichlet Allocation (LDA) is a topic modeling technique that assigns topics to documents, not term frequencies.

Option D is incorrect because word2vec generates dense vector embeddings that capture semantic relationships, not raw term frequencies.

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10
MCQmedium

A data scientist is working with a dataset containing customer transactions. The dataset has a column named 'transaction_date' with timestamp values. The scientist wants to create new features such as day of week, hour, and whether the transaction occurred on a weekend. Which AWS service provides built-in feature engineering capabilities for datetime columns?

A.Amazon SageMaker Data Wrangler
B.Amazon Athena
C.AWS Glue ETL
D.Amazon EMR
AnswerA

Amazon SageMaker Data Wrangler includes built-in transformations for datetime features.

Why this answer

Amazon SageMaker Data Wrangler includes built-in transformations for datetime features like extracting day, month, hour, etc. Option B (Amazon Athena) can extract parts but not as a feature engineering step. Option C (AWS Glue ETL) requires custom code.

Option D (Amazon EMR) requires more manual effort.

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11
MCQmedium

Refer to the exhibit. A data scientist is unable to run an Amazon Athena query on data in `my-bucket`. The IAM policy shown is attached to the user. What is the most likely reason for the failure?

A.The ListBucket action is not granted.
B.Athena needs s3:PutObject permission to write results.
C.The data is encrypted with SSE-C.
D.The bucket does not exist.
AnswerB

Athena writes output to S3.

Why this answer

Athena requires `s3:PutObject` permission to write query results to an output location. Without this permission, the query fails. Option A is not the issue because the bucket exists and is accessible; Option C is not necessarily relevant; Option D is incorrect because the bucket does exist (otherwise the user couldn't even attempt the query).

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12
MCQmedium

A data scientist is exploring a dataset with many missing values. They want to understand the pattern of missingness before deciding on imputation. Which approach is most appropriate?

A.Compute the correlation matrix of the features with missing values.
B.Drop all rows with any missing values.
C.Impute all missing values with the mean of each column.
D.Visualize the missingness using a heatmap or bar chart.
AnswerD

Visualization helps identify patterns like monotonic or random missingness.

Why this answer

Visualizing missingness with a heatmap or bar chart (using libraries like missingno) reveals patterns such as MCAR, MAR, or MNAR. Option A (correlation matrix) does not directly show missingness patterns. Option B (dropping rows) may remove valuable data and assumes MCAR.

Option C (mean imputation) also assumes MCAR and can bias results if missingness is not random.

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13
MCQeasy

A data scientist is exploring a dataset and finds that the correlation between two features is 0.95. What should the data scientist do to address multicollinearity before training a linear regression model?

A.Remove one of the two features
B.Apply L2 regularization
C.Standardize the features
D.Apply Principal Component Analysis
AnswerA

Removing one feature eliminates the high correlation.

Why this answer

Removing one of the highly correlated features reduces multicollinearity. Regularization (B) like Ridge can help but does not remove multicollinearity. Scaling (C) does not affect correlation, so it does not address multicollinearity.

PCA (D) can reduce multicollinearity by creating uncorrelated components, but it changes interpretability and is not the simplest solution.

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14
MCQmedium

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?

A.fields @timestamp, status | filter status = 500 | stats count() by bin(1m)
B.fields @timestamp, @message | filter @message like /ERROR/ | stats count() by bin(1m)
C.fields @timestamp, @message | filter @message like /500/ | sort @timestamp desc
D.fields @timestamp, @message | filter @message like /500/ | stats count() by bin(1m)
AnswerD

Correctly filters for 500 status code and counts per minute.

Why this answer

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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15
MCQmedium

A data scientist is exploring a dataset with 100 features. After generating pair plots, the scientist notices that many features have skewed distributions. Which transformation should the scientist apply to make the distributions more Gaussian-like for modeling?

A.Log transformation
B.Yeo-Johnson transformation
C.Standard scaling (z-score normalization)
D.Box-Cox transformation
AnswerB

Works for any real values.

Why this answer

(Yeo-Johnson transformation) is correct because it can handle both positive and negative values, making it suitable for datasets with skewed distributions that may include negative numbers. Option A (Log transformation) is wrong because it only works for strictly positive values. Option C (Standard scaling) does not change the shape of the distribution; it only centers and scales the data, so it does not fix skewness.

Option D (Box-Cox transformation) also requires positive values, limiting its applicability.

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16
MCQeasy

During EDA, a data scientist notices that a numeric feature 'age' has outliers beyond 3 standard deviations. What is the most appropriate first step?

A.Use the feature as-is in the model
B.Apply a log transformation to the feature
C.Remove all rows with outlier values
D.Investigate the source of the outliers
AnswerD

Understanding outliers guides proper handling.

Why this answer

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.

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17
MCQeasy

A data analyst is investigating a dataset where the target variable is binary (0/1). The analyst wants to check for multicollinearity among the numerical features. Which statistical measure should the analyst use?

A.Variance Inflation Factor (VIF).
B.Mutual information between features and target.
C.Chi-square test of independence.
D.Pearson correlation coefficient between each pair of features.
AnswerA

VIF measures how much a feature is explained by other features.

Why this answer

Variance Inflation Factor (VIF) is the correct measure to check for multicollinearity among numerical features. VIF quantifies how much a feature is correlated with other features by calculating the ratio of variance of a model with multiple features to variance of a model with one feature. Option B (Mutual information) measures dependence between features and target, not among features.

Option C (Chi-square test) is for categorical variables. Option D (Pearson correlation) only measures pairwise linear relationships, not multicollinearity involving multiple features.

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18
MCQmedium

A data scientist is performing EDA on a dataset with many features. They suspect some features are redundant due to high pairwise correlations. Which technique can help identify groups of correlated features?

A.Use t-SNE to visualize feature relationships
B.Apply PCA and examine the loadings
C.Compute mutual information between each feature and the target
D.Use chi-square test for each pair
E.Create a correlation matrix and visualize with a heatmap
AnswerE

A correlation matrix heatmap clearly shows correlated feature groups.

Why this answer

Create a correlation matrix and visualize with a heatmap. This technique directly shows pairwise correlations between features, making it easy to identify groups of highly correlated (redundant) features. Option A is incorrect: t-SNE is a dimensionality reduction technique for visualization of high-dimensional data, but it does not quantify pairwise correlations between features.

Option B is incorrect: PCA reduces dimensionality by creating principal components that are linear combinations of original features; while loadings indicate feature contributions, they do not directly show pairwise correlations between original features. Option C is incorrect: Mutual information measures dependency between features and target, not between features themselves. Option D is incorrect: The chi-square test is used for testing association between categorical variables, not for continuous features or pairwise correlation analysis.

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19
Drag & Dropmedium

Drag and drop the steps to use Amazon SageMaker Feature Store for feature engineering in the correct order.

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps
Order
1Step 1
2Step 2
3Step 3
4Step 4

Why this order

Feature Store involves defining group, ingesting, querying, training, and maintaining.

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20
MCQhard

A data engineer is performing exploratory data analysis on a dataset stored in Amazon S3 using AWS Glue DataBrew. The dataset contains a column 'age' with missing values. DataBrew's profile shows that the column has 5% missing values, a mean of 45, and a standard deviation of 15. Which imputation strategy should the engineer recommend to minimize bias if the missing data is Missing at Random (MAR)?

A.Replace missing values with the mean (45)
B.Remove rows with missing 'age' values
C.Replace missing values with the median
D.Use multiple imputation to generate several plausible values and combine results
AnswerD

Multiple imputation preserves the natural variability and provides valid statistical inferences under MAR.

Why this answer

Multiple imputation provides unbiased estimates under MAR by accounting for uncertainty and preserving relationships between variables. Option A is wrong because mean imputation reduces variance, distorts distributions, and can bias estimates. Option B is wrong because listwise deletion reduces sample size and can introduce bias if missingness is related to other variables.

Option C is wrong because median imputation is a single imputation method that does not account for the uncertainty due to missing data and may still introduce bias.

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21
Multi-Selecteasy

Which TWO are common steps in exploratory data analysis?

Select 2 answers
A.Training a machine learning model.
B.Checking for missing values.
C.Visualizing distributions of features.
D.Deploying the model to production.
E.Tuning hyperparameters.
AnswersB, C

Missing value analysis is a key step.

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22
MCQmedium

A data scientist is analyzing a time series dataset of daily website traffic. The scientist notices a strong weekly seasonality. To better understand the underlying patterns, which decomposition method should the scientist use to separate the trend, seasonal, and residual components?

A.Additive decomposition using moving averages.
B.Use STL (Seasonal and Trend decomposition using Loess).
C.Fit an ARIMA model and examine residuals.
D.Apply an ETS (Error, Trend, Seasonal) model.
AnswerB

STL is robust and flexible for any seasonality.

Why this answer

STL (Seasonal and Trend decomposition using Loess) is a robust method for decomposing time series into trend, seasonal, and residual components. It can handle any seasonality period, including weekly seasonality in daily data, and is robust to outliers. Option A is wrong because additive decomposition using moving averages assumes fixed seasonal amplitude and is sensitive to outliers.

Option C is wrong because ARIMA is a forecasting model, not a decomposition method. Option D is wrong because ETS is an exponential smoothing framework for forecasting, not primarily for decomposition.

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23
Multi-Selecteasy

Which TWO approaches are appropriate for handling missing categorical data during exploratory data analysis? (Choose two.)

Select 2 answers
A.Use one-hot encoding to represent missingness as a binary feature.
B.Impute with the mode (most frequent) of the column.
C.Treat missing values as a separate 'Unknown' category.
D.Drop all rows with missing values in that column.
E.Impute missing values with the mean of the column.
AnswersB, C

Mode is a simple imputation for categorical data.

Why this answer

Options B and C are correct. Imputing with the mode (B) is a simple and effective method for categorical data, as it preserves the most frequent category without introducing new values. Treating missing values as a separate 'Unknown' category (C) allows the model to capture potential patterns associated with missingness, which can be informative.

Option A is incorrect because one-hot encoding is a technique for representing categorical variables, not for handling missing data; it requires the values to be known first. Option D is incorrect because dropping rows with missing values can result in significant data loss and may introduce bias, especially if missingness is not random. Option E is incorrect because mean imputation is suitable for numerical data, not categorical data.

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24
MCQhard

A data scientist is exploring a dataset with 1,000 features and only 200 samples. The goal is to build a binary classifier. Which technique should be used first during exploratory data analysis to reduce dimensionality and avoid overfitting?

A.Compute pairwise correlations and remove highly correlated features.
B.Apply L1 regularization (Lasso) to select features.
C.Use t-SNE to visualize clusters and reduce dimensions.
D.Use principal component analysis (PCA) to reduce dimensions.
AnswerD

PCA reduces dimensionality while preserving variance.

Why this answer

PCA is an unsupervised dimensionality reduction technique that is well-suited for high-dimensional datasets with few samples, as it reduces features while retaining variance and helps avoid overfitting. Option A is wrong because pairwise correlation only captures linear relationships and may miss interactions, and removing correlated features may not be sufficient for high dimensionality. Option B is wrong because L1 regularization (Lasso) is a model-based feature selection method applied during training, not during initial exploratory data analysis (EDA).

Option C is wrong because t-SNE is a visualization technique for reducing dimensions to 2 or 3 for plotting, but it is not suitable for generating features for modeling and can be unstable with few samples.

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25
MCQmedium

A data scientist is exploring a dataset stored as a single 2 GB object in S3. The scientist wants to read only a subset of the file (e.g., the first 1000 lines) to perform initial data inspection. Which approach should the scientist take to minimize data transfer and cost?

A.Use the AWS CLI to download the entire file and then use head to get the first lines.
B.Use S3 Select with a SQL query to retrieve the first 1000 rows.
C.Use the S3 Range header to read the first 1 MB of the file and parse lines.
D.Use Amazon Athena to query the file with LIMIT 1000.
AnswerB

S3 Select efficiently retrieves only the required subset.

Why this answer

S3 Select enables retrieving a subset of data using SQL queries, such as SELECT * FROM s3object LIMIT 1000, minimizing data transfer and cost. Option A is inefficient as it downloads the entire 2 GB file. Option C retrieves bytes, not lines, and may still transfer excessive data or require multiple requests to locate line boundaries.

Option D scans the entire file with Athena, incurring cost and latency for a full table scan even with LIMIT 1000.

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26
MCQmedium

A data scientist is performing exploratory data analysis on a dataset containing customer transactions. The dataset has 1 million rows with 50 features, including numerical and categorical variables. The goal is to identify patterns and potential data quality issues before building a model. Which approach should the data scientist take to efficiently explore the data?

A.Use AWS Glue DataBrew to profile the dataset, view data quality reports, and visualize distributions.
B.Use Amazon Athena to run SQL queries and generate summary statistics.
C.Use Amazon SageMaker Data Wrangler to import the data and create a flow for feature engineering.
D.Use Amazon SageMaker Ground Truth to label the data and then analyze the labels.
AnswerA

DataBrew provides an interactive interface for data profiling, cleaning, and visualization, making it suitable for EDA.

Why this answer

AWS Glue DataBrew is purpose-built for visual data preparation and profiling without writing code. It can directly profile the 1-million-row dataset, automatically generate data quality reports (e.g., missing values, outliers, data types), and provide distribution visualizations for both numerical and categorical features, making it the most efficient choice for exploratory data analysis.

Exam trap

The MLS-C01 exam often tests the distinction between tools for exploratory data analysis versus tools for data transformation or labeling, leading candidates to confuse SageMaker Data Wrangler (feature engineering) or Athena (SQL querying) with a dedicated profiling tool like DataBrew.

How to eliminate wrong answers

Option B is wrong because Amazon Athena is a serverless query engine for analyzing data in S3 using SQL, but it does not provide built-in profiling, data quality reports, or visualizations; it requires manual SQL queries to generate summary statistics, which is less efficient for exploratory analysis. Option C is wrong because Amazon SageMaker Data Wrangler is designed for importing, transforming, and creating feature engineering flows, not for initial data profiling and quality assessment; its primary purpose is preparing data for model training, not exploratory analysis. Option D is wrong because Amazon SageMaker Ground Truth is a data labeling service for creating labeled datasets, not for exploratory data analysis or profiling; using it to analyze labels would be an incorrect and inefficient use of the service.

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27
MCQmedium

A data scientist is exploring a dataset stored in an Amazon S3 bucket. The dataset contains both numerical and categorical features. The scientist wants to compute summary statistics (mean, median, standard deviation) for all numerical features and count the distinct values for categorical features. Which AWS service is most appropriate for this task with minimal coding?

A.Amazon Athena
B.AWS Glue ETL jobs
C.AWS Glue DataBrew
D.Amazon SageMaker Data Wrangler
E.Amazon EMR
AnswerC

AWS Glue DataBrew provides a visual, no-code interface for data profiling, making it ideal for minimal coding.

Why this answer

AWS Glue DataBrew is the most appropriate service for this task because it provides a visual, no-code interface for data preparation and profiling. It can automatically compute summary statistics (mean, median, standard deviation) for numerical features and count distinct values for categorical features without writing any code. Amazon Athena requires writing SQL queries, which is not 'minimal coding' and is less suitable for profiling.

AWS Glue ETL jobs require writing Python or Scala code, so it is more code-intensive. Amazon SageMaker Data Wrangler also requires some setup and integration with SageMaker, and while it can perform similar tasks, it is not as straightforward for simple profiling as DataBrew. Amazon EMR requires managing clusters and writing code, making it the least minimal coding option.

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28
MCQeasy

A machine learning engineer notices that the target variable in a regression dataset has a long-tailed distribution. Which visualization technique is most appropriate to assess the distribution before applying a log transformation?

A.Bar chart
B.Histogram with density curve
C.Box plot
D.Scatter plot
AnswerB

Histogram and density curve show the distribution shape, including long tails.

Why this answer

(Histogram with density curve) is the most appropriate visualization for assessing a long-tailed distribution because it clearly shows the shape, spread, and tail behavior of the target variable. A histogram with an overlaid density curve helps identify skewness and the need for a log transformation. Option A (Bar chart) is for categorical data, not continuous distributions.

Option C (Box plot) provides quartiles and outliers but does not fully reveal the distribution shape, especially the length of tails. Option D (Scatter plot) visualizes relationships between two variables, not a univariate distribution.

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29
MCQhard

A data scientist is analyzing a dataset with a timestamp column and several numeric measurements. The goal is to detect seasonality and trends. Which AWS service can be used directly from SageMaker Studio to perform this analysis without writing code?

A.Amazon Forecast
B.Amazon SageMaker Data Wrangler
C.Amazon QuickSight ML Insights
D.AWS Glue DataBrew
AnswerB

Includes built-in time series analysis.

Why this answer

SageMaker Data Wrangler is the correct choice because it integrates directly with SageMaker Studio and includes built-in time series analysis capabilities such as seasonality detection and trend analysis, all without writing code. Option A (Amazon Forecast) is a forecasting service that requires a separate workflow and is not directly usable for exploratory analysis from Studio. Option C (Amazon QuickSight ML Insights) is for visualization and anomaly detection but not for time series decomposition within SageMaker Studio.

Option D (AWS Glue DataBrew) is a data preparation tool that does not provide native time series analysis features.

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30
MCQhard

A data scientist is analyzing a dataset stored in Amazon S3 (100 GB, CSV format) using Amazon SageMaker Studio. The dataset contains 500 columns and 10 million rows. The data scientist wants to understand the distribution of each column, detect missing values, and identify outliers. However, the SageMaker Studio notebook instance runs out of memory when loading the entire dataset into a pandas DataFrame. The data scientist needs to complete the EDA efficiently without modifying the source data. What should the data scientist do?

A.Write a script that loads only a random 10% sample of rows to reduce memory usage.
B.Use AWS Glue ETL to transform the data into Parquet format and then load into pandas.
C.Launch a larger notebook instance with more memory (e.g., ml.r5.24xlarge) and reload the data.
D.Use Amazon SageMaker Data Wrangler to create a data flow that samples and profiles the data.
AnswerD

Data Wrangler can handle large datasets efficiently.

Why this answer

SageMaker Data Wrangler is purpose-built for EDA on large datasets; it automatically samples data and profiles columns without requiring the entire dataset to be loaded into memory. Option A (sampling 10% of rows) could work but risks missing critical patterns or outliers, and is less integrated than Data Wrangler. Option B (converting to Parquet with AWS Glue) adds complexity and still requires memory to load into pandas.

Option C (larger instance) may still be insufficient and is more expensive. Data Wrangler provides a seamless, integrated experience within SageMaker Studio for efficient EDA.

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31
MCQeasy

During EDA, a data scientist finds that a categorical feature 'city' has 500 unique values but only 10 cities account for 90% of the data. What is a recommended way to handle the rare categories?

A.Group rare categories into a single 'Other' category.
B.Apply label encoding to all categories.
C.One-hot encode all 500 categories.
D.Drop all rows with rare categories.
AnswerA

Reduces cardinality and retains data.

Why this answer

Grouping rare categories into 'Other' reduces cardinality, avoids overfitting from high-dimensional sparse features, and retains the majority of data from the top 10 cities. Option B (label encoding) is not recommended as it imposes an arbitrary ordinal relationship that may mislead the model. Option C (one-hot encoding all 500 categories) would create 499 dummy features, leading to the curse of dimensionality and sparse data.

Option D (dropping rows with rare categories) discards potentially valuable data and may introduce bias.

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32
MCQhard

A data scientist is performing exploratory data analysis on a dataset with mixed data types (numerical, categorical, text). The goal is to identify clusters of similar records. Which technique is most appropriate?

A.DBSCAN
B.Hierarchical clustering
C.K-means clustering
D.K-prototypes clustering
AnswerD

K-prototypes is designed for mixed numerical and categorical data.

Why this answer

K-prototypes extends k-means to handle mixed data by combining Euclidean distance for numerical and Hamming distance for categorical. K-means only works with numerical data. DBSCAN works on numerical data.

Hierarchical clustering typically uses numerical distance. Gower distance can be used but is less common in clustering algorithms.

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33
Multi-Selecteasy

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.)

Select 2 answers
A.Amazon Athena
B.Amazon Redshift query editor
C.Amazon QuickSight
D.AWS Glue console
E.Amazon S3 console
AnswersA, D

Athena can query the Glue Data Catalog using SHOW CREATE TABLE or INFORMATION_SCHEMA.

Why this answer

Amazon Athena can query the AWS Glue Data Catalog using SQL, including viewing table schemas via the INFORMATION_SCHEMA or by running DESCRIBE statements on tables. The AWS Glue console directly displays the schema of tables in the Data Catalog under the 'Tables' section. Amazon Redshift query editor is for querying Redshift data warehouses, not for directly viewing Glue Catalog schemas unless a federated query is set up.

Amazon QuickSight is a business intelligence tool for visualizing data, not for schema exploration. Amazon S3 console only shows objects in S3 buckets, not the schema of Glue tables.

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34
MCQmedium

A data scientist is working on a project to predict customer churn for a telecom company. The dataset includes 50,000 records with 20 features, including customer demographics, account information, and service usage. The data scientist uses Amazon SageMaker Studio and loads the data into a pandas DataFrame. During EDA, the data scientist notices that the target variable 'churn' has only 10% positive cases. Additionally, several features have missing values: 'income' has 5% missing, 'age' has 2% missing, and 'total_charges' has 1% missing. The data scientist also observes that 'income' is highly skewed with a long right tail, and 'age' is moderately skewed. The data scientist wants to handle missing values and prepare the data for modeling. Which course of action is most appropriate?

A.Impute 'income' with median, 'age' with median, 'total_charges' with median, and use SMOTE to handle class imbalance after splitting the data.
B.Remove all rows with any missing values, and use random oversampling to handle class imbalance.
C.Impute 'income' with mode, 'age' with mode, 'total_charges' with mode, and use SMOTE after splitting.
D.Impute all missing values with the mean of each column, and use stratified sampling to handle class imbalance.
AnswerA

Median is robust to skewness. SMOTE is appropriate for imbalance.

Why this answer

Median imputation is robust to skewness (particularly for income and age), and SMOTE is applied after splitting to avoid data leakage and handle class imbalance. Option B is wrong because removing rows with missing values would discard roughly 8% of the data (5%+2%+1% with potential overlap), which is a significant loss of information; additionally, random oversampling may lead to overfitting. Option C is wrong because mode imputation is appropriate for categorical data, not for continuous features like income, age, and total_charges.

Option D is wrong because mean imputation is sensitive to outliers and skewness, especially for income with a long right tail; also, stratified sampling only ensures proportional representation in train/test splits, it does not generate synthetic samples to address imbalance.

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35
MCQhard

A data scientist is building a model to predict housing prices using a dataset with 100,000 records and 50 features. The features include 'sqft_living', 'sqft_lot', 'bedrooms', 'bathrooms', 'floors', 'waterfront', 'view', 'condition', 'grade', etc. The data scientist uses Amazon SageMaker Data Wrangler for EDA. Upon reviewing the data, the data scientist finds that 'sqft_living' has a correlation of 0.7 with 'sqft_above' (square footage above ground) and 0.6 with 'sqft_basement'. Also, 'grade' (overall grade of the house) is highly correlated with 'condition' (0.8). The target variable 'price' is right-skewed. The data scientist plans to use a linear regression model. Which set of actions should the data scientist take to improve model performance?

A.Apply standard scaling to all numeric features and use the data as is, since linear regression is robust to multicollinearity.
B.Remove all features that have correlation >0.5 with any other feature to eliminate multicollinearity, and apply standard scaling to all numeric features.
C.Apply principal component analysis (PCA) to all features to reduce dimensionality, and then fit linear regression on the principal components.
D.Apply log transformation to the target variable 'price' to reduce skewness, and remove either 'sqft_above' or 'sqft_living' and either 'grade' or 'condition' to handle multicollinearity.
AnswerD

Log transform addresses skewness; removing one of each pair reduces multicollinearity.

Why this answer

Log-transforming the right-skewed target variable 'price' helps meet the normality assumption of linear regression residuals. Additionally, removing either 'sqft_above' or 'sqft_living' (correlated 0.7) and either 'grade' or 'condition' (correlated 0.8) reduces multicollinearity, which can destabilize coefficient estimates. Option A is incorrect because standard scaling does not address skewness or multicollinearity.

Option B is incorrect because removing all features with correlation >0.5 is too aggressive and may discard useful information. Option C is incorrect because PCA reduces dimensionality but the components may be less interpretable, and log transformation is still needed for the target.

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36
MCQhard

A data scientist is setting up an IAM policy for a SageMaker notebook instance that needs to read and write data in the 'training/' folder of an S3 bucket, and also list objects in the bucket. Does the policy satisfy the requirements?

A.Yes, the policy correctly grants the required permissions.
B.No, the policy must also include s3:DeleteObject for data cleaning.
C.No, the policy misses s3:GetObject for the bucket itself.
D.No, the condition on ListBucket is invalid.
AnswerA

Assuming the policy grants the minimal permissions described, it satisfies the requirements. The explanation should note the missing policy.

Why this answer

The stem does not include the IAM policy, so the question cannot be definitively answered as written. However, assuming a typical policy that grants s3:GetObject and s3:PutObject on the training/ prefix and s3:ListBucket with a condition restricting the prefix to training/*, the policy meets the requirements. Therefore, option A is correct under that interpretation.

Option B is wrong because s3:DeleteObject is not required for reading and writing. Option C is wrong because s3:GetObject is allowed on the training/ objects, not on the bucket itself. Option D is wrong because the condition on ListBucket is valid and correctly limits listing to the training/ prefix.

Exam trap

This question is invalid because it references a policy that is not displayed. In a real exam, the policy would be shown. Traps include overlooking the condition on ListBucket or assuming extra permissions are needed.

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37
MCQmedium

During exploratory data analysis, a data scientist notices that the distribution of a continuous feature is heavily right-skewed. Which transformation should be applied to make the distribution more symmetric for linear regression?

A.Standardization (z-score)
B.One-hot encoding
C.Min-max scaling
D.Log transformation
AnswerD

Log transformation reduces right skewness.

Why this answer

Log transformation is commonly used to reduce right skewness and make the distribution more symmetric. Standardization (z-score) does not change the shape of the distribution; it only centers and scales. One-hot encoding is for categorical features, not continuous.

Min-max scaling also does not affect skewness; it rescales the range but preserves shape.

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38
MCQmedium

A data scientist is analyzing a dataset with a skewed target variable for a regression problem. During EDA, the scientist wants to transform the target variable to approximate a normal distribution. Which transformation should the scientist apply first?

A.Quantile transformation
B.Min-Max scaling
C.Log transformation
D.Box-Cox transformation
AnswerD

Box-Cox automatically finds the best power transformation to achieve normality.

Why this answer

Box-Cox transformation (D) is a parametric transformation that identifies the optimal power transformation to make data more normally distributed. For skewed target variables in regression, it is often preferred as a first approach because it can handle various skewness patterns and includes log transformation as a special case (lambda=0). Quantile transformation (A) is non-parametric and can overfit; Min-Max scaling (B) only rescales range, not shape; Log transformation (C) is a specific case that works for positive data but may not be optimal for all skewness.

Therefore, Box-Cox is the best first choice.

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39
MCQmedium

A data engineer is using Amazon SageMaker Data Wrangler to perform exploratory data analysis on a large dataset stored in S3. The analysis reveals high cardinality in a categorical feature with over 1 million unique values. What is the best approach to handle this before training a model?

A.Apply one-hot encoding.
B.Use label encoding to convert categories to integers.
C.Drop the high-cardinality feature.
D.Use target encoding based on the mean of the target variable per category.
AnswerD

Target encoding reduces cardinality and captures target relationship.

Why this answer

Target encoding (also known as mean encoding) replaces each category with the mean of the target variable for that category, effectively handling high cardinality without exploding dimensionality or imposing ordinal relationships. Option A is wrong because one-hot encoding on a feature with 1 million unique values would create over 1 million columns, making the dataset sparse and computationally expensive. Option B is wrong because label encoding assigns arbitrary integers, which may introduce unintended ordinal relationships and mislead the model.

Option C is wrong because dropping the feature could discard valuable predictive information.

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40
MCQmedium

A data scientist runs the AWS CLI command shown in the exhibit to list objects larger than 100 KB in an S3 bucket. The data scientist wants to understand the size distribution of these files. What is the most significant limitation of this approach for EDA?

A.The command only returns objects larger than 100 KB, not equal to.
B.The command may return incomplete results if there are more than 1000 objects.
C.The command uses the wrong query syntax and will fail.
D.The command does not return the file names, only sizes.
AnswerB

S3 list-objects returns up to 1000 objects per call; pagination is required for more.

Why this answer

The AWS CLI `list-objects` command returns a maximum of 1000 objects by default. If the bucket contains more than 1000 objects larger than 100 KB, the command will only return the first 1000, leading to incomplete results for EDA. Option A is incorrect because the command uses `> 100000` which excludes objects exactly 100 KB, but that is not the most significant limitation.

Option C is incorrect because the query syntax is valid. Option D is incorrect because the command does return the object keys (file names) as well as sizes.

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41
MCQmedium

A data scientist is using Amazon SageMaker Data Wrangler to explore a dataset. They notice that a feature has a very high correlation (0.95) with the target variable. What should they do to avoid overfitting?

A.Use L2 regularization in the model
B.Apply PCA to reduce dimensionality
C.Standardize the feature using StandardScaler
D.Remove the feature from the dataset
AnswerD

Correct: High correlation with target can indicate data leakage; removing is safest.

Why this answer

The feature with a 0.95 correlation to the target is likely leaking target information (data leakage), which would cause the model to overfit on training data but fail on new data. Removing the feature (Option D) directly addresses the leakage. Option A (L2 regularization) helps with overfitting from noisy features but does not remove the leaked information.

Option B (PCA) reduces dimensionality but the leak would still be present in the principal components. Option C (StandardScaler) only normalizes the feature, not removes it. Therefore, the best action is to remove the feature.

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42
MCQhard

A data scientist is using Amazon SageMaker Studio notebooks for EDA. They want to share a reproducible report that includes code, visualizations, and narrative text with their team. Which approach should they use?

A.Save the notebook as an .ipynb file and share it via Amazon S3.
B.Use Amazon SageMaker Clarify to generate an EDA report.
C.Export the results to Amazon QuickSight and create a dashboard.
D.Use Amazon SageMaker Autopilot to generate a report.
AnswerA

Correct. A .ipynb file preserves code, output, and markdown, making it fully reproducible when shared via S3.

Why this answer

A Jupyter notebook (.ipynb) saved in SageMaker Studio contains code, visualizations, and narrative text, and sharing it via Amazon S3 allows team members to reproduce and interact with the analysis. Option B (SageMaker Clarify) is for bias detection and model explainability, not for sharing EDA reports. Option C (Amazon QuickSight) is used for interactive dashboards and does not include the underlying code.

Option D (SageMaker Autopilot) automates model building and does not generate a shareable EDA report.

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43
MCQeasy

A data scientist loads a large dataset from Amazon S3 into a pandas DataFrame using a SageMaker notebook. The dataset contains a mix of numeric and categorical features. The data scientist wants to quickly check for missing values. Which pandas function is most appropriate?

A.df.info()
B.df.describe()
C.df.shape
D.df.isnull().sum()
AnswerD

This returns the sum of null values per column.

Why this answer

Df.isnull().sum() returns the count of missing values per column. Option A is wrong because df.info() provides column data types and non-null counts, but not missing value counts directly. Option B is wrong because df.describe() only summarizes numeric columns.

Option C is wrong because df.shape returns the dimensions, not missing values.

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44
Multi-Selecteasy

A data scientist is exploring a dataset with categorical variables. Which TWO EDA techniques are appropriate for understanding the relationship between a categorical feature and a continuous target? (Choose TWO.)

Select 2 answers
A.Correlation matrix
B.Violin plots
C.Scatter plot with categorical variable on x-axis
D.Bar chart of category counts
E.Side-by-side box plots
AnswersB, E

Violin plots show density and distribution across categories.

Why this answer

Box plots (E) and violin plots (B) are both effective for visualizing the distribution of a continuous variable across different categories. Violin plots combine box plots with kernel density estimation, providing a richer view. Correlation matrix (A) is for numerical variables.

Scatter plots (C) require two continuous variables. Bar chart of counts (D) shows frequency distribution of categories, not relationship with a continuous target.

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45
MCQhard

A data scientist is building a fraud detection model using a dataset of 500,000 credit card transactions. The dataset contains 20 features, including transaction amount, merchant category, time since last transaction, and customer age. The target variable 'is_fraud' has 0.1% positive examples. Initial EDA reveals that the transaction amount distribution is highly skewed with a long tail. Also, there are missing values in the 'customer_age' field (5% missing). The data scientist needs to prepare the data for training a binary classifier. Which combination of preprocessing steps should the data scientist apply to address these issues and improve model performance? (Select TWO.)

A.Use SMOTE to generate synthetic samples of the minority class.
B.Apply standard scaling to all numerical features.
C.Apply log transformation to the transaction amount to reduce skewness.
D.Impute missing values in customer_age with the mean of the non-missing values.
E.Drop the transaction amount feature because of its skewness.
AnswerC, D

Log transformation is effective for reducing right skewness and can make the distribution more Gaussian-like, which benefits many models.

Why this answer

This is a multi-select question requiring two correct preprocessing steps. Option C is correct because applying a log transformation to the highly skewed transaction amount reduces skewness and compresses the dynamic range, which helps many machine learning algorithms (especially those sensitive to feature scales like logistic regression or SVM) converge faster and perform better. Option D is correct because imputing missing values in customer_age with the mean is a simple and effective method when the missing rate is only 5% and the data is roughly normally distributed, preserving sample size.

Option A is wrong because SMOTE is typically applied after splitting the data to avoid data leakage, and it is not a preprocessing step for EDA; also, the class imbalance is severe but SMOTE may be considered later. Option B is wrong because standard scaling does not handle skewness; it should be applied after skewness correction. Option E is wrong because dropping the feature due to skewness would lose valuable information; transformation is preferable.

Exam trap

The trap here is that candidates often confuse handling skewness with scaling—they may choose standard scaling (Option B) thinking it addresses skewness, but standard scaling only centers and scales the data, not corrects the shape of the distribution.

How to eliminate wrong answers

Option A is wrong because SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class, but with only 0.1% fraud cases (500 out of 500,000), SMOTE would create an extremely large synthetic dataset that risks overfitting and does not address the skewed transaction amount or missing values. Option B is wrong because standard scaling (z-score normalization) is not appropriate for highly skewed features like transaction amount; scaling after log transformation would be valid, but applying standard scaling directly to a skewed distribution does not reduce skewness and can still leave the feature non-Gaussian, harming model performance. Option E is wrong because dropping the transaction amount feature due to skewness discards valuable predictive information; skewness can be corrected via transformation (e.g., log) rather than deletion, which would reduce model accuracy.

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46
Multi-Selectmedium

Which TWO of the following are appropriate techniques for detecting outliers in a univariate continuous feature?

Select 2 answers
A.Apply a Random Forest classifier to predict outliers.
B.Use Z-score and flag values with absolute Z-score > 3.
C.Remove any value that is more than one standard deviation from the mean.
D.Use DBSCAN clustering with default parameters.
E.Use the interquartile range (IQR) and flag values below Q1 - 1.5*IQR or above Q3 + 1.5*IQR.
AnswersB, E

Z-score >3 is a common outlier threshold.

Why this answer

The Z-score method (Option B) is a standard statistical technique for detecting outliers in a univariate continuous feature. It measures how many standard deviations a data point is from the mean, and flagging values with an absolute Z-score greater than 3 is a common threshold because, under a normal distribution, approximately 99.7% of data falls within three standard deviations, making points beyond this likely outliers.

Exam trap

The MLS-C01 exam often tests the misconception that removing values more than one standard deviation from the mean is a valid outlier detection technique, when in fact it removes a large portion of normal data and is not a standard practice.

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47
Multi-Selecthard

Which TWO of the following are appropriate methods for handling missing data in a dataset?

Select 2 answers
A.Dropping features with more than 50% missing values
B.Mean imputation for all features
C.Multiple imputation
D.Using algorithms that handle missing values internally (e.g., XGBoost)
E.Listwise deletion (removing rows with missing values)
AnswersC, D

Multiple imputation accounts for uncertainty by creating multiple datasets.

Why this answer

Multiple imputation and using algorithms that handle missing values (e.g., XGBoost) are valid. Listwise deletion reduces sample size. Mean imputation may bias distributions.

Dropping features with many missing values may lose information.

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48
Multi-Selecthard

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.)

Select 3 answers
A.Fit a Lasso regression model and select features with non-zero coefficients
B.Remove features with variance below a threshold (e.g., <0.01)
C.Remove features with high pairwise correlation (e.g., >0.95)
D.Calculate mutual information between each feature and the target, and keep top k features
E.Apply Principal Component Analysis (PCA) and select top components
AnswersB, C, D

Low-variance features provide little information and can be removed.

Why this answer

In a high-dimensional dataset (p >> n), feature selection is crucial. Option B (Variance Threshold) is suitable because features with low variance (e.g., <0.01) are likely to be constant or near-constant and thus uninformative. Option C (removing features with high pairwise correlation >0.95) helps reduce redundancy and multicollinearity.

Option D (mutual information) is a filter method that measures dependency between each feature and the target, allowing selection of the most relevant features. Option A (Lasso regression) is a modeling method that can be used for feature selection but is not typically used during EDA; it is a supervised learning technique. Option E (PCA) is a dimensionality reduction technique that creates new components, not feature selection.

Therefore, the correct answers are B, C, D.

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49
MCQhard

A machine learning team is building a fraud detection model. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraudulent). Which EDA technique is most important to apply before modeling?

A.Normalize all numerical features to have zero mean and unit variance.
B.Remove outliers from the dataset using the IQR method.
C.Create a stratified train-test split to preserve the class distribution.
D.Perform correlation analysis to remove highly correlated features.
AnswerC

Ensures the rare class appears in both training and test sets.

Why this answer

Stratified sampling is crucial for highly imbalanced datasets to ensure that the rare class is proportionally represented in both training and testing splits, allowing for proper evaluation. Normalization (A) is important but does not address imbalance. Removing outliers (B) could remove fraud cases.

Correlation analysis (D) is useful but not the most critical step for imbalance.

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50
MCQeasy

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?

A.AWS Glue DataBrew
B.Amazon SageMaker Data Wrangler
C.Amazon QuickSight
D.Amazon Athena
AnswerA

AWS Glue DataBrew provides visual data profiling and preparation without coding, making it ideal for quickly understanding dataset characteristics.

Why this answer

AWS Glue DataBrew (Option A) is correct because it provides visual data profiling and preparation without writing code, allowing users to quickly understand distributions, missing values, data types, and basic statistics. Option B (Amazon SageMaker Data Wrangler) is designed for data preparation and feature engineering within the SageMaker ecosystem, but it is more focused on transforming data for machine learning rather than initial exploratory analysis. Option C (Amazon QuickSight) is a business intelligence tool for creating visualizations and dashboards, not for profiling raw datasets.

Option D (Amazon Athena) is an interactive query service that can analyze data in S3 using SQL, but it does not offer built-in data profiling capabilities for quick exploration.

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51
Multi-Selecthard

Which TWO statements about handling categorical variables in exploratory data analysis are correct? (Select TWO.)

Select 2 answers
A.When a categorical feature has high cardinality, consider grouping rare categories.
B.Target encoding always avoids data leakage.
C.One-hot encoding creates binary columns for each category.
D.Label encoding is suitable for nominal categorical variables.
E.Categorical variables should always be dropped if they have many unique values.
AnswersA, C

Grouping reduces dimensionality and overfitting.

Why this answer

High-cardinality categorical features can lead to overfitting and sparse representations. Grouping rare categories into a single 'Other' bucket reduces dimensionality and noise, improving model generalization without losing significant predictive signal.

Exam trap

The MLS-C01 exam often tests the misconception that label encoding is safe for nominal data, when in fact it imposes an ordinal relationship that can distort model performance.

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52
MCQmedium

A data engineer is building a data pipeline that aggregates customer transaction data. The engineer notices that some transactions have duplicate entries due to a system error. Which approach should the engineer use to identify and remove duplicates based on a unique transaction ID?

A.Sort the data by transaction ID and then check consecutive rows for equality
B.Use fuzzy matching to find similar transaction IDs
C.Group by all columns and aggregate with sum
D.Use the drop_duplicates method on the transaction ID column
AnswerD

drop_duplicates removes exact duplicate rows based on specified columns.

Why this answer

Using drop_duplicates on the transaction ID column is a straightforward and efficient method to remove duplicate rows based on the unique identifier. Option A is incorrect; sorting and checking consecutive rows is a valid but more complex approach, and not as direct as drop_duplicates. Option B is incorrect because fuzzy matching is designed for approximate matches, not exact duplicates.

Option C is incorrect because grouping by all columns and summing would aggregate data, potentially losing information, and does not specifically remove duplicate transaction IDs.

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53
Multi-Selectmedium

A data scientist is performing exploratory data analysis on a dataset with 100 features. They want to identify which features are most correlated with the target variable. Which THREE methods are appropriate for this task?

Select 3 answers
A.Pearson correlation coefficient
B.Variance threshold
C.One-hot encoding
D.Feature importance from a random forest
E.Mutual information
AnswersA, D, E

Measures linear correlation between each feature and the target.

Why this answer

Pearson correlation coefficient measures linear relationship between features and target. Feature importance from a random forest provides a ranking of feature relevance. Mutual information captures both linear and non-linear dependencies.

Together, these three methods effectively identify correlated features. Variance threshold is used for removing low-variance features, not for correlation. One-hot encoding is a preprocessing technique for categorical variables, not a correlation method.

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54
MCQeasy

A data scientist is exploring a dataset with many features and wants to detect multicollinearity. Which technique should the scientist use?

A.Calculate the Variance Inflation Factor (VIF) for each feature.
B.Compute the Pearson correlation matrix between features.
C.Perform ANOVA on each feature against the target.
D.Create pairwise scatter plots of all features.
AnswerA

Variance Inflation Factor (VIF) measures how much the variance of a regression coefficient is inflated due to multicollinearity. A high VIF indicates strong multicollinearity, making it a quantitative method for detection.

Why this answer

Variance Inflation Factor (VIF) is a standard metric for detecting multicollinearity. Option D (pairwise scatter plots) can hint at relationships but does not quantify multicollinearity. Option B (Pearson correlation matrix) shows pairwise linear correlation but does not capture multicollinearity among multiple variables.

Option C (ANOVA) is used for comparing means, not for detecting multicollinearity.

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55
Multi-Selectmedium

A data scientist is performing EDA on a dataset with mixed data types (numerical, categorical, text). The dataset is stored in S3. Which TWO AWS services can be used to directly perform statistical summaries and visualizations without writing custom code?

Select 2 answers
A.Amazon SageMaker Studio
B.AWS Glue DataBrew
C.Amazon Athena
D.Amazon SageMaker Data Wrangler
E.Amazon QuickSight
AnswersD, E

Data Wrangler offers visual data analysis and built-in visualizations.

Why this answer

Options D and E are correct. Amazon SageMaker Data Wrangler provides a visual interface for data preparation and analysis with built-in transforms and visualizations directly on S3 data. Amazon QuickSight is a BI service that connects to S3 and creates dashboards with statistical summaries and visualizations.

Option A (SageMaker Studio) is an IDE for ML development, not a direct analysis service without custom code. Option B (AWS Glue DataBrew) is a data preparation tool but requires some configuration and is not primarily for statistical summaries and visualizations. Option C (Athena) is a SQL query engine for querying data, but does not provide built-in visualizations.

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56
Multi-Selecthard

A data engineer is analyzing a large dataset stored in Amazon S3 using AWS Glue and Amazon Athena. They notice that queries against a table with many small files are slow. Which TWO actions can improve query performance?

Select 2 answers
A.Use Athena's automatic compression
B.Increase the number of Glue DPUs
C.Convert files to Apache Parquet format
D.Decrease the number of partitions
E.Use a larger number of partitions
AnswersC, E

Parquet is a columnar format that reduces data scanned and improves compression, leading to faster queries.

Why this answer

Converting files to Apache Parquet format (C) improves query performance by leveraging columnar storage, which reduces the amount of data scanned and provides better compression. Using a larger number of partitions (E) allows Athena to perform partition pruning, limiting the data scanned per query. These two actions together reduce the volume of data processed and improve query speed, unlike increasing Glue DPUs (irrelevant for Athena), using automatic compression (not a distinct action), or decreasing partitions (increases scanned data).

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57
MCQmedium

A company has a dataset with a timestamp column and multiple numerical metrics. They want to identify seasonality and trends. Which AWS service is best suited for this analysis?

A.Amazon SageMaker Canvas
B.Amazon CloudWatch
C.Amazon QuickSight
D.Amazon Athena
AnswerC

QuickSight offers time series analysis and forecasting capabilities.

Why this answer

Amazon QuickSight provides built-in time series visualization and forecasting. SageMaker Canvas is for ML models without code. Athena is for querying.

CloudWatch is for monitoring AWS resources. Kinesis Data Analytics is for real-time analytics.

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58
MCQmedium

A data scientist is working with a dataset containing customer transaction records stored in Amazon S3 as CSV files. The dataset has 500 columns and 2 million rows. The scientist wants to perform EDA to understand data types, missing values, and summary statistics for each column. They need to do this quickly and without writing custom code. The scientist has access to AWS Glue DataBrew and Amazon SageMaker Data Wrangler. Which approach should the scientist take?

A.Use Amazon SageMaker Data Wrangler to import the data and generate a report
B.Use Amazon Athena to run SELECT statements on each column
C.Use AWS Glue DataBrew to create a profile job that outputs data quality reports
D.Use AWS Glue ETL jobs with PySpark to compute statistics
AnswerC

DataBrew's profile job automatically computes statistics and detects missing values.

Why this answer

AWS Glue DataBrew provides a visual interface for data profiling and can handle large datasets without writing code. It automatically detects data types, missing values, and summary statistics. Option A is wrong because SageMaker Data Wrangler requires more manual setup and coding, and is not as straightforward for quick profiling.

Option B is wrong because Amazon Athena requires writing SQL queries and is not a dedicated profiling tool. Option D is wrong because AWS Glue ETL with PySpark requires writing custom code.

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59
MCQmedium

A DevOps engineer runs the CloudWatch Logs Insights query shown above on the log group for an ML training job. The result shows a spike in ERROR messages at a specific hour. What should the engineer do next to identify the root cause?

A.Modify the query to display the actual @message for the hour with the spike.
B.Remove the filter on ERROR to see all messages.
C.Change the bin to 5m to see more detailed spikes.
D.Increase the limit to 50 to see more hours.
AnswerA

Directly see error details.

Why this answer

Modifying the query to include the @message field for the specific hour with the spike allows the engineer to see the actual error messages, which is the direct way to identify the root cause. Option B is incorrect because removing the filter would show all messages, which is less targeted. Option C is incorrect because changing the bin to 5m might be too granular and not helpful without examining the messages.

Option D is incorrect because increasing the limit does not help identify the cause without seeing the messages.

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60
Multi-Selecthard

A data engineer is performing exploratory data analysis on a dataset with 1 million rows and 50 features. The engineer wants to identify missing values and outliers. Which THREE approaches should the engineer use? (Choose three.)

Select 3 answers
A.Create a correlation heatmap of all features
B.Use a DataFrame.info() method to see non-null counts
C.Plot box plots for all features simultaneously
D.Use a missingno matrix to visualize missing data patterns
E.Use a DataFrame.describe() to view summary statistics
AnswersB, D, E

info() shows non-null counts and data types.

Why this answer

Options B, D, and E are correct because they directly help identify missing values and outliers. DataFrame.info() shows non-null counts per column, revealing missing values. The missingno matrix visualizes missing data patterns across rows and columns.

DataFrame.describe() provides summary statistics (count, mean, std, min, max, quartiles) that can indicate outliers (e.g., values far outside the interquartile range). Option A (correlation heatmap) is used to assess relationships between features, not to detect missing values or outliers. Option C (box plots for all features simultaneously) is impractical with 50 features because it would be cluttered and hard to interpret; box plots are more useful when applied selectively to a few features or after filtering.

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61
MCQmedium

A data scientist uses SageMaker Studio to run EDA on a dataset with 500 features. The goal is to reduce dimensionality before modeling. Which EDA technique should the data scientist use to understand the variance explained by each feature?

A.Histogram of the target variable
B.Scree plot of principal components
C.Heatmap of feature correlations
D.Box plot of each feature
AnswerB

Scree plot displays variance explained by each component.

Why this answer

A Scree plot from PCA shows the eigenvalues or variance explained by each principal component, helping decide how many components to retain. Option A is wrong because a histogram shows distribution, not variance. Option C is wrong because a heatmap of correlations shows pairwise relationships, not variance.

Option D is wrong because a box plot shows summary statistics.

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62
Multi-Selecthard

Which THREE of the following are best practices when performing exploratory data analysis on a dataset with both numerical and categorical features?

Select 3 answers
A.Check the proportion of missing values for each feature.
B.Compute pairwise correlation coefficients between numerical features.
C.Encode all categorical features using label encoding for simplicity.
D.Include all categorical features with high cardinality as-is in the model.
E.Visualize the distribution of numerical features using histograms and box plots.
AnswersA, B, E

Missing value analysis is a key EDA step.

Why this answer

Checking the proportion of missing values for each feature is a fundamental step in exploratory data analysis (EDA). It helps identify data quality issues, such as systematic missingness, which can bias downstream modeling and inform decisions about imputation strategies or feature exclusion.

Exam trap

The trap here is that candidates may assume label encoding is harmless for categorical features, but it imposes an artificial order that can distort model behavior, especially in tree-based models that rely on split points.

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63
Multi-Selectmedium

A data scientist is analyzing a dataset with 100 features and 10,000 observations. The target variable is binary (0/1). Initial exploratory data analysis reveals that many features have missing values, high correlation with each other, and non-normal distributions. The data scientist wants to identify the most important features for predicting the target while reducing dimensionality. Which TWO actions should the data scientist take? (Choose two.)

Select 2 answers
A.Use chi-squared test to rank features by p-value.
B.Apply Principal Component Analysis (PCA) to reduce dimensionality.
C.Perform a t-test for each feature to compare means between classes.
D.Calculate Pearson correlation coefficients between features and target.
E.Compute mutual information between each feature and the target.
AnswersB, E

PCA reduces dimensionality by creating uncorrelated components, handling multicollinearity.

Why this answer

B is correct because Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms correlated features into a set of linearly uncorrelated principal components, effectively handling high correlation and reducing the feature space. It does not require normality assumptions and can work with missing values after imputation, making it suitable for this dataset.

Exam trap

The MLS-C01 exam often tests the misconception that correlation-based methods (like Pearson or chi-squared) are sufficient for feature selection in high-dimensional, non-normal data, when in fact they fail due to assumptions about linearity and distribution.

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64
MCQeasy

During EDA, a data scientist notices that a feature has a high proportion of missing values (e.g., 70%). The feature is continuous and expected to be important based on domain knowledge. What is the best approach to handle this?

A.Remove the feature entirely to avoid bias.
B.Create a binary indicator for missingness and impute the continuous values with the median.
C.Impute missing values with -1 since it is out of range.
D.Drop all rows with missing values in that feature.
AnswerB

This captures both the pattern of missingness and the distribution.

Why this answer

It preserves the predictive signal from the feature while accounting for the pattern of missingness. Creating a binary indicator allows the model to learn whether missingness itself is informative, and median imputation is robust to outliers for a continuous feature. This approach avoids the bias of dropping the feature entirely and is more principled than arbitrary out-of-range imputation.

Exam trap

The trap here is that candidates often choose to drop the feature or rows without considering that missingness can be a meaningful signal, and that a binary indicator combined with robust imputation is a standard technique for high-missingness continuous features.

How to eliminate wrong answers

Option A is wrong because removing a feature with 70% missing values discards potentially important domain-driven signal, and the missingness itself may be informative. Option C is wrong because imputing with -1 (an arbitrary out-of-range value) can distort the feature's distribution and introduce a false signal that the model may misinterpret as a valid numeric relationship. Option D is wrong because dropping all rows with missing values in that feature would discard 70% of the dataset, leading to severe sample size reduction and potential selection bias.

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65
MCQeasy

A data analyst is exploring a dataset and wants to identify outliers in a numerical feature. Which visualization technique is most effective for detecting outliers?

A.Line chart
B.Histogram
C.Scatter plot
D.Box plot
AnswerD

Box plots display outliers as individual points outside the whiskers.

Why this answer

Box plot. A box plot is specifically designed to show quartiles and highlight outliers as points beyond the whiskers. A line chart (A) is for trends over time, a histogram (B) shows distribution but does not isolate outliers, and a scatter plot (C) is for bivariate relationships.

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66
Multi-Selectmedium

A machine learning engineer is analyzing a dataset with 500 features and suspects multicollinearity. Which TWO techniques can help identify and address multicollinearity during exploratory data analysis? (Choose TWO.)

Select 2 answers
A.Apply t-SNE for visualization
B.Apply Principal Component Analysis (PCA)
C.Calculate Variance Inflation Factor (VIF) for each feature
D.Generate a correlation matrix heatmap
E.Use Lasso regression to select features
AnswersC, D

VIF > 5-10 indicates multicollinearity.

Why this answer

Variance Inflation Factor (VIF) measures how much the variance of a regression coefficient is inflated due to multicollinearity. Correlation matrix heatmap shows pairwise correlations. PCA reduces dimensionality but does not directly identify multicollinearity.

Lasso regression addresses it via regularization but is a modeling step. t-SNE is for visualization of high-dimensional data.

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67
Multi-Selecteasy

Which TWO actions are appropriate when dealing with outliers in a dataset during exploratory data analysis? (Select TWO.)

Select 2 answers
A.Replace the mean with the median for numerical features.
B.Apply log transformation to reduce the impact of extreme values.
C.Remove all outliers without further investigation.
D.Use visualization techniques like box plots to identify outliers.
E.Assume outliers are errors and delete them.
AnswersB, D

Log transformation can compress skewed distributions and reduce outlier influence.

Why this answer

Applying a log transformation compresses the range of the data, reducing the influence of extreme values without removing them. This is a common technique in exploratory data analysis for right-skewed distributions, as it can make the data more normally distributed and improve the performance of models that assume normality.

Exam trap

The MLS-C01 exam often tests the distinction between data transformation techniques (like log transformation) and data removal or replacement strategies, trapping candidates who think that simply changing a summary statistic (mean to median) or deleting outliers without investigation is a proper handling method.

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68
MCQmedium

A data engineer ingests streaming data into Amazon Kinesis Data Streams. The data science team needs to analyze the data using Amazon SageMaker notebooks. What is the most efficient way to provide access to the stream data for ad-hoc exploration?

A.Create an AWS Lambda function to transform and write data to DynamoDB, then query DynamoDB from the notebook.
B.Configure a Kinesis Firehose delivery stream to deliver data to an S3 bucket, then query the data from the notebook using Athena.
C.Install the Kinesis Agent on the SageMaker notebook instance and configure it to write data to a local file.
D.Use the Kinesis connector for Spark to read data directly from the stream into a Spark DataFrame in the notebook.
AnswerD

Direct, real-time access for ad-hoc exploration.

Why this answer

The Kinesis connector for Spark enables direct reading of streaming data into a Spark DataFrame in the SageMaker notebook, allowing real-time ad-hoc analysis with minimal latency. Option A is incorrect because writing to DynamoDB via Lambda adds unnecessary transformation steps and latency, and DynamoDB is not optimized for large-scale streaming data exploration. Option B is incorrect because using Kinesis Firehose to deliver data to S3 and then querying with Athena introduces significant latency (data is written in batches) and is not suitable for real-time exploration.

Option C is incorrect because the Kinesis Agent is designed for sending data from sources to Kinesis, not for consuming or reading data; it cannot be used to read stream data into a notebook.

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69
MCQeasy

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

A.The mean and median are significantly different
B.The minimum age of 0 and maximum age of 120 may be outliers
C.The missing value rate of 2.3% is too high
D.The number of unique values (85) is too high
AnswerB

Age 0 and 120 are likely data errors.

Why this answer

A minimum age of 0 and maximum age of 120 are potential data entry errors or outliers that warrant further investigation, as extreme values can skew analysis. Option A is incorrect because the mean and median being close suggests the distribution is relatively symmetric and not a data quality issue. Option C is incorrect because a missing value rate of 2.3% is generally considered low and may be handled without major concern.

Option D is incorrect because 85 unique values for age is reasonable given the possible age range; it does not indicate a problem.

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70
MCQhard

During exploratory data analysis, a data scientist notices that the correlation matrix of features shows many pairs with absolute correlation > 0.95. The dataset includes both numerical and categorical variables. Which technique is most appropriate to reduce multicollinearity while preserving the most information?

A.Apply Principal Component Analysis (PCA) to the features.
B.Use only one-hot encoded categorical features.
C.Apply L1 regularization during model training.
D.Remove one feature from each highly correlated pair.
AnswerA

PCA reduces dimensionality and decorrelates features.

Why this answer

PCA is the most appropriate technique because it transforms correlated features into orthogonal principal components, effectively handling multicollinearity while preserving variance. Option B (using only one-hot encoded categorical features) discards numerical features and may not address multicollinearity; Option C (L1 regularization) is a modeling technique, not for EDA; Option D (removing one feature per highly correlated pair) is ad-hoc and can lose information compared to PCA which captures variance in fewer dimensions.

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71
MCQhard

A machine learning engineer is analyzing a dataset for a regression problem. The target variable has a long-tail distribution with extreme outliers. The engineer wants to reduce the influence of outliers while preserving the relative order of values. Which data transformation should the engineer apply to the target variable?

A.Min-max normalization
B.Box-Cox transformation
C.Rank transformation
D.Log transformation
AnswerC

Rank transformation replaces values with their rank order, making the distribution uniform and robust to outliers.

Why this answer

The rank transformation is correct because it maps each value to its rank in the dataset, preserving the relative order of values while eliminating the impact of magnitude differences. This effectively reduces the influence of extreme outliers without distorting the ordinal relationships. Option A (min-max normalization) is incorrect because it linearly scales values to a fixed range, and outliers can still dominate the scaling.

Option B (Box-Cox transformation) can reduce skew but requires positive values and does not fully remove outlier influence; it transforms the distribution shape but still allows extreme values to affect the transformation parameters. Option D (log transformation) reduces right skew but remains monotonic, so extreme high values still have a disproportionate effect on the model compared to rank transformation.

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72
MCQmedium

A data scientist is performing EDA on a time-series dataset and observes a strong upward trend and seasonal patterns. The scientist needs to make the data stationary for modeling. Which transformation should be applied?

A.Apply one-hot encoding
B.Apply PCA
C.Apply min-max scaling
D.Apply differencing to the series
E.Apply logarithmic transformation
AnswerD

Differencing removes trends and seasonality, making the series stationary.

Why this answer

Differencing is a technique that removes trends and seasonality by subtracting the previous observation from the current one, making the time series stationary. One-hot encoding (A) is used for categorical variables. PCA (B) reduces dimensionality but does not address stationarity.

Min-max scaling (C) normalizes the range of data but does not remove trend or seasonality. Logarithmic transformation (E) stabilizes variance but does not eliminate trends. Therefore, differencing (D) is the correct choice.

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73
MCQhard

A data scientist is exploring a large dataset (10 TB) stored in Amazon S3. The dataset is in CSV format and has many columns. The scientist wants to quickly compute summary statistics (mean, min, max, count) for each column without moving the data. Which approach is most cost-effective and efficient?

A.Import the data into Amazon SageMaker Data Wrangler
B.Launch an Amazon EMR cluster with Spark
C.Use S3 Select to compute statistics
D.Use Amazon Athena with SQL queries
E.Use AWS Glue DataBrew to profile the data
AnswerD

Athena queries data in place with no data movement and pay-per-query pricing.

Why this answer

Amazon Athena is a serverless query service that allows you to run SQL queries directly on data stored in S3 without moving it. It is cost-effective because you pay only for the data scanned per query. For summary statistics like mean, min, max, count, you can use aggregate functions like AVG, MIN, MAX, COUNT in SQL.

Option A (SageMaker Data Wrangler) requires importing data into SageMaker, incurring transfer costs and time. Option B (Amazon EMR) requires provisioning a cluster, which adds overhead and cost for a simple summary task. Option C (S3 Select) works on a single object and cannot compute statistics across entire dataset easily; it is more suited for filtering.

Option E (AWS Glue DataBrew) is a data preparation tool that may be more expensive and overkill for simple summary statistics.

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74
MCQmedium

A data scientist is analyzing a dataset with missing values in several columns. The dataset contains both numerical and categorical features. Which approach should the data scientist use to handle missing values while minimizing bias and preserving relationships in the data?

A.Use multiple imputation (e.g., MICE) to impute missing values
B.Use forward-fill to propagate the last observed value
C.Delete all rows with missing values
D.Replace missing values with the mean or median of each column
AnswerA

MICE models each variable as a function of others, preserving relationships and reducing bias.

Why this answer

Multiple Imputation by Chained Equations (MICE) models each missing value as a function of other variables, preserving relationships and reducing bias. Option B (forward-fill) is unsuitable for non-time-series data and can introduce bias. Option C (deleting rows) reduces sample size and may introduce bias if data is not missing completely at random.

Option D (mean/median imputation) distorts distributions and reduces variance, potentially biasing relationships.

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75
MCQeasy

Refer to the exhibit. A data scientist examines a sample of data and notices that all columns are numeric. The scientist wants to check for multicollinearity. Which statistic should be computed from this sample?

A.Correlation matrix (Pearson)
B.Chi-square test of independence
C.Variance Inflation Factor (VIF)
D.Covariance matrix
AnswerA

A correlation matrix can reveal high pairwise correlations.

Why this answer

The correlation matrix shows pairwise Pearson correlations, which can indicate high collinearity. Option B is wrong because chi-square is for categorical variables, not numeric. Option C is wrong because VIF requires more variables than observations (or typically used after regression).

Option D is wrong because covariance alone is scale-dependent.

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