MLS-C01 Practice Test 9 — 10 Questions

Question 1

A data scientist is analyzing a dataset with 1 million records and 20 features. The target variable is continuous. The scientist wants to identify non-linear relationships between features and the target. Which technique is MOST suitable for this purpose during exploratory data analysis?

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

Accepted Answer

Calculate mutual information scores between each feature and the target.. Mutual information captures any kind of dependency, including non-linear. Option A is wrong because a correlation matrix heatmap only shows pairwise linear correlations among features, not feature-target relationships. Option B is wrong because PCA is for dimensionality reduction and does not directly assess feature-target relationships. Option D is wrong because Pearson correlation only measures linear relationships, missing non-linear ones.

Answer

Visualize the correlation matrix heatmap of all features.

Answer

Apply Principal Component Analysis (PCA) and examine the loadings.

Answer

Compute Pearson correlation coefficients between each feature and the target.

Question 2

A company uses Amazon SageMaker Data Wrangler to perform exploratory data analysis. They want to detect outliers in a numerical column using the Interquartile Range (IQR) method. Which transformation should they apply in Data Wrangler?

Accepted Answer

Handle outliers. Amazon SageMaker Data Wrangler provides a 'Handle outliers' transform that supports IQR-based outlier detection. Option A (Impute) is used to fill missing values, not detect outliers. Option B (Normalize) scales data to a standard range. Option D (Binning) groups continuous values into intervals. Therefore, the correct transform to apply for IQR outlier detection is 'Handle outliers'.

Answer

Impute

Answer

Normalize

Answer

Binning

Question 3

Refer to the exhibit. A data scientist is running an Amazon EMR Spark job for exploratory data analysis on a large dataset. The job fails with the error shown. What is the most appropriate action to resolve this?

Accepted Answer

Increase the executor memory in Spark configuration.. The error message indicates an OutOfMemoryError in the Spark executors. Increasing executor memory (option C) directly addresses this by providing more heap space for data processing. Option A (fewer nodes) reduces total cluster memory, worsening the problem. Option B (Parquet format) can improve I/O performance but does not resolve insufficient memory allocation. Option D (increase driver memory) only helps the driver process, not the executors.

Answer

Reduce the number of worker nodes.

Answer

Convert the input data to Parquet format.

Answer

Increase the driver memory.

Question 4

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?

Accepted Answer

Visualize the missingness using a heatmap or bar chart.. 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.

Answer

Compute the correlation matrix of the features with missing values.

Answer

Drop all rows with any missing values.

Answer

Impute all missing values with the mean of each column.

Question 5

Which TWO metrics are appropriate for evaluating a binary classification model trained on imbalanced data? (Select TWO.)

Accepted Answer

F1 score. The F1 score is appropriate for imbalanced binary classification because it balances precision and recall, making it robust when the positive class is rare. Unlike accuracy, it does not get inflated by a majority negative class, and it directly penalizes models that predict the majority class for all instances.

Answer

Log loss

Answer

Accuracy

Answer

ROC-AUC

Question 6

A data scientist is analyzing a dataset with 100 features and wants to identify which features are most correlated with the target variable. Which AWS service is most appropriate for this task?

Accepted Answer

Amazon SageMaker Data Wrangler. Amazon SageMaker Data Wrangler provides built-in data analysis and visualization capabilities, including correlation analysis, making it suitable for this task. Amazon QuickSight is a BI tool for dashboards, not for feature correlation analysis. Amazon Athena is a query service for data in S3, not for embedded data wrangling. AWS Glue DataBrew is a visual data preparation tool, but SageMaker Data Wrangler is more directly suited for correlation analysis.

Answer

Amazon QuickSight

Answer

Amazon Athena

Answer

AWS Glue DataBrew

Question 7

A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The company has a 100 Mbps internet connection and a tight deadline of two weeks. Which AWS service should the engineer use to transfer the data most efficiently?

Accepted Answer

AWS Snowball Edge. B is correct because transferring 50 TB over a 100 Mbps connection would take approximately 48 days (50 TB * 8 / 100 Mbps / 86400 seconds/day), far exceeding the two-week deadline. AWS Snowball Edge is a physical data transport device that can securely transfer petabytes of data offline, bypassing network bandwidth constraints entirely. For large datasets and tight deadlines, Snowball Edge is the most efficient AWS service.

Answer

AWS Storage Gateway (Volume Gateway)

Answer

Amazon S3 Transfer Acceleration

Answer

AWS DataSync over the internet

Question 8

A data scientist is performing EDA on a dataset with 500,000 rows and 10 columns. The dataset is stored in an S3 bucket as CSV files. The scientist wants to generate summary statistics (mean, median, min, max) for all numeric columns. Which service allows the quickest ad-hoc analysis without provisioning any infrastructure?

Accepted Answer

Amazon Athena. Amazon Athena can query data in S3 directly using SQL. Option A is wrong because AWS Glue ETL requires job setup. Option C is wrong because Amazon SageMaker Data Wrangler requires a notebook instance. Option D is wrong because QuickSight is for visualization, not direct summary statistics.

Answer

AWS Glue ETL

Answer

Amazon SageMaker Data Wrangler

Answer

Amazon QuickSight

Question 9

A company uses Amazon SageMaker to train a model for fraud detection. The dataset has 1 million transactions, with 0.1% fraud. The data scientist trains a random forest model and achieves 99.9% accuracy but 0% recall on the fraud class. Which technique is most likely to improve recall without significantly reducing precision?

Accepted Answer

Apply SMOTE to generate synthetic fraud samples. SMOTE (Synthetic Minority Oversampling Technique) generates synthetic fraud samples by interpolating between existing minority class instances, which directly addresses the extreme class imbalance (0.1% fraud). This increases the representation of the fraud class in the training data, allowing the random forest model to learn decision boundaries that capture fraud patterns, thereby improving recall without introducing the noise or information loss associated with other methods.

Answer

Tune the classification threshold

Answer

Use cost-sensitive learning with a high cost for fraud misclassification

Answer

Undersample the majority class

Question 10

A data scientist is analyzing a dataset with 1 million records and 20 features. The target variable is continuous. The scientist wants to identify non-linear relationships between features and the target. Which technique is MOST suitable for this purpose during exploratory data analysis?

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

Accepted Answer

Area Under the Receiver Operating Characteristic Curve (AUC ROC). The most appropriate metric during exploratory data analysis for evaluating sampling strategies with imbalanced data is AUC ROC, as it is independent of the class distribution and measures the model's ability to distinguish between positive and negative classes regardless of the threshold. Option A (RMSE) is used for regression tasks, not classification. Option C (F1 score) depends on a specific threshold and can be affected by sampling changes. Option D (Accuracy) is misleading for imbalanced datasets because a high accuracy can be achieved by predicting the majority class.

Answer

Root Mean Squared Error (RMSE)

Answer

F1 score

Answer

Accuracy