MLS-C01 Exploratory Data Analysis Practice Test 2 — 15 Questions

Question 1

A data scientist is trying to read a CSV file from S3 bucket 'my-bucket' with key 'training/data.csv' using an IAM role with the attached policy shown in the exhibit. The read operation fails with an Access Denied error. What is the most likely cause?

Accepted Answer

The policy does not include the s3:ListBucket permission, which is required to access the object.. The s3:GetObject permission alone is insufficient to read an object from S3 when the request is made via the AWS Console or certain SDK operations that first list the bucket's contents. The s3:ListBucket permission is required for the ListObjects API call, which is often implicitly invoked to resolve the object key path. Without it, the read operation fails with an Access Denied error even if the GetObject permission is granted.

Answer

The object is encrypted with SSE-KMS and the role does not have kms:Decrypt permission.

Answer

The resource ARN in the first statement should be 'arn:aws:s3:::my-bucket/training' without the wildcard.

Answer

The policy explicitly denies s3:GetObject because of the second statement with the trailing slash.

Question 2

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.)

Accepted Answer

Apply log transformation to the transaction amount to reduce skewness.. 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. This is a standard technique for handling right-skewed distributions without losing data.

Answer

Use SMOTE to generate synthetic samples of the minority class.

Answer

Apply standard scaling to all numerical features.

Answer

Drop the transaction amount feature because of its skewness.

Question 3

A machine learning engineer is working on a customer churn prediction project. The dataset contains 100,000 records with 15 features, including customer demographics, account information, and usage patterns. The target variable 'churned' is binary with 15% positive examples. During EDA, the engineer notices that the feature 'tenure' (number of months the customer has been with the company) has a multimodal distribution with peaks at 1, 12, 24, and 36 months. Also, the feature 'monthly_charges' has a strong positive correlation with 'total_charges' (correlation coefficient = 0.95). The engineer wants to build a logistic regression model. Which preprocessing steps should the engineer take to address these issues? (Select TWO.)

Accepted Answer

Bin the 'tenure' feature into categorical groups (e.g., 0-6, 7-12, 13-24, 25-36, 36+) to capture the non-linear relationship.. Option A is correct because binning the 'tenure' feature into categorical groups (e.g., 0-6, 7-12, 13-24, 25-36, 36+) captures the multimodal distribution and non-linear relationship with churn, which logistic regression (a linear model) cannot model directly. This transforms the feature into a format that allows the model to learn different churn probabilities for each tenure segment without imposing a linear assumption.

Answer

Apply log transformation to the 'tenure' feature to make it unimodal.

Answer

Create polynomial features up to degree 3 for 'tenure' to capture non-linearity.

Answer

Standardize all numerical features to have mean 0 and variance 1.

Question 4

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.)

Accepted Answer

Apply Principal Component Analysis (PCA) to reduce dimensionality.. 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.

Answer

Use chi-squared test to rank features by p-value.

Answer

Perform a t-test for each feature to compare means between classes.

Answer

Calculate Pearson correlation coefficients between features and target.

Question 5

Refer to the exhibit. A data scientist ran an S3 Select query on a large CSV file stored in Amazon S3. The output shows only 2 records returned, but the data scientist expected thousands. The file size is 10 GB. What is the MOST likely reason for the small result set?

Accepted Answer

The city column may have leading/trailing spaces or case differences.. S3 Select performs exact string matching by default, so if the WHERE clause filters on the city column, any leading/trailing spaces or case differences will cause mismatches, returning far fewer rows than expected. The query likely used a literal like 'New York' while the data contains ' New York ' or 'new york', resulting in only 2 matches instead of thousands.

Answer

The file needs to be indexed by S3 Select before querying.

Answer

The CSV file contains nested arrays that S3 Select cannot parse.

Answer

S3 Select does not support the WHERE clause on CSV files.

Question 6

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?

Accepted Answer

Apply a log transformation to the 'sqft_living' feature.. 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.

Answer

Remove the 'sqft_living' feature because it violates the normality assumption.

Answer

One-hot encode the 'zipcode' feature to capture location effects.

Answer

Apply standard scaling (z-score) to the 'sqft_living' feature.

Question 7

Drag and drop the steps to create a data processing job using Amazon SageMaker Processing in the correct order.

Question 8

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

Question 9

Match each SageMaker feature to its description.

Question 10

Match each ML model evaluation concept to its definition.

Question 11

A data scientist is analyzing a dataset with 500 features and 10,000 samples. After running a correlation matrix, they find that many feature pairs have correlation >0.95. What is the most appropriate next step to improve model performance?

Accepted Answer

Apply principal component analysis (PCA) to reduce dimensionality.. Option A is correct because high correlation indicates multicollinearity, which can be addressed by dimensionality reduction techniques like PCA. Option B is wrong because adding more data does not fix multicollinearity. Option C is wrong because removing all correlated features may discard useful information. Option D is wrong because increasing regularization can help but is not the most appropriate first step for a large number of correlated features.

Answer

Collect more training data to reduce the impact of correlated features.

Answer

Increase the regularization parameter in the model.

Answer

Remove all features with correlation above 0.95.

Question 12

A machine learning engineer is performing exploratory data analysis on a dataset containing customer transactions. They notice that the target variable is highly imbalanced: 99% of samples belong to class 0 and 1% to class 1. Which technique should they use to address this imbalance before training a classification model?

Accepted Answer

Apply SMOTE to generate synthetic samples for the minority class.. Option C is correct because SMOTE generates synthetic samples for the minority class, which is effective for imbalanced datasets. Option A is wrong because accuracy is not a good metric for imbalanced data. Option B is wrong because under-sampling discards majority class data. Option D is wrong because using raw data without handling imbalance typically leads to poor minority class performance.

Answer

Train the model on the raw data without any modification.

Answer

Use accuracy as the evaluation metric and train on the raw data.

Answer

Under-sample the majority class to match the minority class size.

Question 13

A data scientist is analyzing a dataset with missing values. The missing data mechanism is missing at random (MAR). Which imputation method is most appropriate to preserve relationships between variables?

Accepted Answer

Use multiple imputation by chained equations (MICE).. Option D is correct because multiple imputation by chained equations (MICE) handles MAR well by modeling each variable with missing values conditional on others. Option A is wrong because mean imputation underestimates variance. Option B is wrong because dropping rows with missing data reduces sample size and can introduce bias. Option C is wrong because KNN imputation assumes data are MCAR and may not be optimal for MAR.

Answer

Remove all rows with any missing values.

Answer

Use k-nearest neighbors imputation.

Answer

Replace missing values with the mean of the column.

Question 14

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

Accepted Answer

Apply log transformation to reduce the impact of extreme values.. Option B is correct because 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.

Answer

Replace the mean with the median for numerical features.

Answer

Remove all outliers without further investigation.

Answer

Assume outliers are errors and delete them.

Question 15

Which THREE techniques are commonly used for feature engineering in exploratory data analysis? (Select THREE.)

Accepted Answer

Extracting date/time components like day of week or hour.. Option A is correct because extracting date/time components such as day of week, hour, or month from a timestamp is a standard feature engineering technique. It transforms a single datetime column into multiple categorical or cyclical features that can reveal temporal patterns like weekly seasonality or peak hours, which are often critical for time-series models.

Answer

Using principal component analysis (PCA) to create new features.

Answer

Applying one-hot encoding to numerical features.

MLS-C01 Exploratory Data Analysis Practice Test 2 — 15 Questions

Exhibit

MLS-C01 Exploratory Data Analysis Practice Test 2 — 15 Questions

Exhibit