MLS-C01 Practice Exam 60 — 20 Questions

Question 1

A data scientist is analyzing a dataset with a binary target variable. The dataset is highly imbalanced (99% negative class). Which metric is most appropriate for evaluating the model's performance during exploratory data analysis?

Accepted Answer

Area Under the ROC Curve (AUC-ROC). The Area Under the ROC Curve (AUC-ROC) is robust to class imbalance and measures the trade-off between true positive rate and false positive rate. Accuracy is misleading. F1 score is sensitive to imbalance but less so for threshold selection. Precision and recall individually are less comprehensive.

Answer

Accuracy

Answer

Precision

Answer

F1 Score

Question 2

A data scientist is analyzing a dataset with a target variable that is binary (0/1). Which visualization is most appropriate to explore the relationship between a continuous feature and the target?

Accepted Answer

Box plot of the feature grouped by target.. Option D is correct because a box plot shows distribution of continuous feature across binary classes. Option A is wrong because scatter plot is for two continuous variables. Option B is wrong because histogram shows distribution of one variable. Option C is wrong because bar chart is for categorical features.

Answer

Scatter plot of the feature vs. the target.

Answer

Bar chart of the feature.

Answer

Histogram of the feature.

Question 3

A data scientist wants to visualize the correlation between a continuous feature and a binary target variable. Which plot is most appropriate?

Accepted Answer

Box plot of the feature grouped by target class. Option B is correct because a box plot shows distribution of the continuous feature across categories of the binary target, highlighting differences. Option A is wrong because a scatter plot is for two continuous variables. Option C is wrong because a histogram shows distribution of a single variable. Option D is wrong because a bar chart is for categorical vs categorical or counts.

Answer

Scatter plot with feature on x-axis and target on y-axis

Answer

Histogram of the feature

Answer

Bar chart of target class counts

Question 4

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?

Accepted Answer

Yes, the policy correctly grants the required permissions.. Option A is correct. The policy allows GetObject and PutObject on the training/ folder, and ListBucket with a condition restricting prefix to training/*, which allows listing only that prefix. Option B is wrong because the policy works. Option C is wrong because the condition is valid. Option D is wrong because the policy is sufficient.

Answer

No, the policy must also include s3:DeleteObject for data cleaning.

Answer

No, the policy misses s3:GetObject for the bucket itself.

Answer

No, the condition on ListBucket is invalid.

Question 5

A data analyst wants to check for duplicate rows in a dataset stored in S3. Which AWS service can be used to run a SQL query to count duplicates without moving the data?

Accepted Answer

Amazon Athena. Option B is correct because Amazon Athena allows running SQL queries directly on data in S3, including counting duplicates. Option A is wrong because AWS Glue is an ETL service, not a query engine. Option C is wrong because Amazon Redshift Spectrum can query data in S3 but requires a Redshift cluster. Option D is wrong because Amazon SageMaker Studio is an IDE, not a query service.

Answer

Amazon Redshift Spectrum

Answer

Amazon SageMaker Studio

Answer

AWS Glue

Question 6

A company is performing EDA on a dataset with 10,000 rows and 200 columns. They run a correlation matrix and find many high correlations (|r| > 0.9). What is the best approach to address multicollinearity before modeling?

Accepted Answer

Calculate Variance Inflation Factor (VIF) and remove features with VIF > 10. Option B is correct because VIF measures how much a variable is correlated with others; removing high VIF variables reduces multicollinearity. Option A is wrong because scaling does not remove correlation. Option C is wrong because PCA creates orthogonal components but reduces interpretability. Option D is wrong because Lasso can handle multicollinearity but may not be the best EDA step.

Answer

Standardize all features

Answer

Use Lasso regression with cross-validation

Answer

Apply Principal Component Analysis (PCA) to all features

Question 7

A data scientist creates the above IAM policy and attaches it to a role used by an Amazon SageMaker notebook instance. When trying to save a file to the S3 bucket, the operation fails. What is the missing permission?

Accepted Answer

kms:GenerateDataKey. Option D is correct because SageMaker needs s3:PutObject, but also needs s3:GetObject for some operations. However, the error is likely due to missing encryption permissions. Option A is wrong because s3:ListBucket is for listing. Option B is wrong because kms:Decrypt is for reading. Option C is wrong because s3:GetObject is for reading.

Answer

kms:Decrypt

Answer

s3:ListBucket

Answer

s3:GetObject

Question 8

A data scientist is performing EDA on a dataset with a timestamp column. They want to detect seasonality. Which visualization is most appropriate?

Accepted Answer

Line plot of value over time. Option A is correct because a time series line plot clearly shows seasonal patterns. Option B is wrong because bar chart of monthly averages may not show seasonality within months. Option C is wrong because scatter plot with timestamp vs. value may be cluttered. Option D is wrong because box plot by month shows distribution, not trend over time.

Answer

Box plot of value grouped by month

Answer

Bar chart of average value per month

Answer

Scatter plot of timestamp vs. value

Question 9

An ML team is analyzing a time series dataset of daily website traffic. They notice a pattern where traffic spikes every Sunday. Which EDA technique should they use to confirm this seasonality?

Accepted Answer

Plot the time series data with a line plot. Option A is correct because a line plot over time clearly shows weekly patterns. Option B is wrong because histograms show distribution of values, not time patterns. Option C is wrong because autocorrelation measures correlation with lagged values, but visual inspection is more direct for confirming seasonality. Option D is wrong because scatter plots show relationships between two variables.

Answer

Compute autocorrelation at different lags

Answer

Create a scatter plot of traffic vs. day of week

Answer

Plot a histogram of the traffic values

Question 10

A data scientist is trying to upload a CSV file to an S3 bucket using the AWS CLI without specifying server-side encryption. The upload fails with an AccessDenied error. Based on the bucket policy exhibit, what is the most likely cause?

Accepted Answer

The upload request did not specify the required server-side encryption.. Option B is correct because the policy requires that all PutObject requests include server-side encryption with AES256. Option A is wrong because the policy allows GetObject and PutObject, but with a condition. Option C is wrong because the condition is on the encryption, not on the bucket. Option D is wrong because the error is AccessDenied, not NoSuchBucket.

Answer

The bucket does not exist.

Answer

The data scientist does not have any permissions to the bucket.

Answer

The data scientist used the wrong AWS region.

Question 11

A company has customer feedback data stored in CSV files in S3. The data includes a 'feedback_text' column. Which AWS service is best suited for performing sentiment analysis as part of exploratory data analysis?

Accepted Answer

Amazon Comprehend. Option A is correct because Amazon Comprehend is a natural language processing (NLP) service that can perform sentiment analysis directly. Option B is wrong because Amazon Lex is for conversational interfaces, not text analysis. Option C is wrong because Amazon Rekognition is for image and video analysis. Option D is wrong because Amazon Textract is for extracting text from documents, not sentiment.

Answer

Amazon Rekognition

Answer

Amazon Textract

Answer

Amazon Lex

Question 12

A data scientist is performing exploratory data analysis on a dataset with missing values. The dataset contains a column 'income' with 20% missing values. The income distribution is right-skewed. Which imputation method is most appropriate to preserve the skewness?

Accepted Answer

Impute with the median income. Option D is correct because median is robust to skewness and preserves the central tendency without affecting the skewness as much as mean. Option A is wrong because mean is sensitive to outliers and would reduce skewness. Option B is wrong because mode is for categorical data. Option C is wrong because dropping rows reduces sample size.

Answer

Impute with the mean income

Answer

Drop rows with missing income

Answer

Impute with the mode income

Question 13

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

Accepted Answer

Z-score. Options A, C, and D are correct. Option B is wrong because DBSCAN is a multivariate clustering method. Option E is wrong because Cook's distance is for regression diagnostics.

Answer

Cook's distance

Answer

DBSCAN clustering

Question 14

A team has a dataset with 500 features and wants to reduce dimensionality. During EDA, they compute the variance of each feature. Which finding would most likely lead to feature removal?

Accepted Answer

Some features have near-zero variance. Option B is correct because near-zero variance features provide little information and can be removed. Option A is wrong because high variance is often useful. Option C is wrong because high correlation between two features might warrant removal of one, but variance is not the direct indicator. Option D is wrong because negative covariance is still informative.

Answer

Some features have high correlation with each other

Answer

Some features have negative covariance with the target

Answer

Some features have very high variance

Question 15

During EDA, a data scientist finds that a feature 'age' has 30% missing values. The dataset has 100,000 rows. Which imputation strategy is most robust if the data is not missing at random (MNAR) and the missingness is related to the age value itself?

Accepted Answer

Create a separate category indicating missingness and impute with a placeholder. Option C is correct because missingness related to the value itself means that the missing data are systematically different; creating a 'missing' category allows the model to learn the pattern. Option A is wrong because mean imputation reduces variance and ignores the systematic difference. Option B is wrong because median imputation has similar issues. Option D is wrong because random imputation introduces noise without capturing the missingness pattern.

Answer

Impute with the mean age

Answer

Impute with a random sample from the observed ages

Answer

Impute with the median age

Question 16

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.)

Accepted Answer

Violin plots. Options A and C are correct. Box plots show distribution of continuous variable across categories. Violin plots combine box plot and density. Option B is wrong because scatter plot is for two continuous variables. Option D is wrong because bar chart of counts shows frequency, not relationship with target. Option E is wrong because correlation matrix is for numerical features.

Answer

Correlation matrix

Answer

Scatter plot with categorical variable on x-axis

Answer

Bar chart of category counts

Question 17

A data scientist is visualizing the distribution of a numerical feature that is heavily right-skewed. Which visualization technique is most appropriate?

Accepted Answer

Box plot with log scale. A box plot with log scale is effective for skewed data as it shows outliers and distribution shape after transformation. Histogram with log scale also works. KDE is similar to histogram. Q-Q plot checks normality. Scatter plot is for two variables.

Answer

Histogram with linear scale

Answer

Scatter plot

Answer

Q-Q plot

Question 18

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?

Accepted Answer

Use target encoding based on the mean of the target variable per category.. Option B is correct because target encoding is effective for high cardinality. Option A is wrong because one-hot encoding would create too many columns. Option C is wrong because label encoding may introduce ordinal relationships. Option D is wrong because dropping the feature may lose important information.

Answer

Apply one-hot encoding.

Answer

Use label encoding to convert categories to integers.

Answer

Drop the high-cardinality feature.

Question 19

Which TWO are common steps in exploratory data analysis?

Accepted Answer

Checking for missing values.

Answer

Training a machine learning model.

Answer

Deploying the model to production.

Answer

Tuning hyperparameters.

Question 20

Which TWO techniques are appropriate for detecting outliers in a univariate numeric dataset?

Accepted Answer

Z-score method. Options A and C are correct: Z-score identifies outliers based on standard deviations from the mean; IQR uses the interquartile range. DBSCAN (B) is for multivariate clustering. Mahalanobis distance (D) is multivariate. Cook's distance (E) is for regression influence.

Answer

Cook's distance

Answer

Mahalanobis distance

Answer

DBSCAN clustering