MLS-C01 Timed Practice Test 3 — 10 Questions

Question 1

During EDA, a data scientist creates a scatter matrix of numerical features and notices that some features have a funnel-shaped pattern (variance increases with the mean). What is the appropriate transformation to stabilize variance?

Accepted Answer

Apply log transformation.. A funnel-shaped pattern in a scatter matrix indicates heteroscedasticity, where variance increases with the mean. The log transformation is appropriate because it compresses the scale of the data, making the variance more constant across the range of values, which stabilizes variance for right-skewed or multiplicative data.

Answer

Standardize the features using Z-scores.

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Apply a sine transformation.

Answer

Apply Box-Cox transformation with lambda=0.

Question 2

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

Accepted Answer

Use Z-score and flag values with absolute Z-score > 3.. 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.

Answer

Apply a Random Forest classifier to predict outliers.

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Remove any value that is more than one standard deviation from the mean.

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Use DBSCAN clustering with default parameters.

Question 3

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

Accepted Answer

Check the proportion of missing values for each feature.. Option A is correct because 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.

Answer

Encode all categorical features using label encoding for simplicity.

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Include all categorical features with high cardinality as-is in the model.

Question 4

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?

Accepted Answer

Use AWS Glue DataBrew to profile the dataset, view data quality reports, and visualize distributions.. 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.

Answer

Use Amazon Athena to run SQL queries and generate summary statistics.

Answer

Use Amazon SageMaker Data Wrangler to import the data and create a flow for feature engineering.

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Use Amazon SageMaker Ground Truth to label the data and then analyze the labels.

Question 5

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.

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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 6

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.

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Apply standard scaling to all numerical features.

Answer

Drop the transaction amount feature because of its skewness.

Question 7

A company is building a recommendation system for an e-commerce platform. They have user-item interaction data (clicks, purchases) and want to use matrix factorization. They plan to use Amazon SageMaker to train the model. Which dataset format is MOST appropriate for the built-in Factorization Machines algorithm?

Accepted Answer

RecordIO-protobuf with user_id, item_id, and label fields. The built-in Factorization Machines algorithm in Amazon SageMaker requires the RecordIO-protobuf format for optimal performance, as it allows efficient binary serialization and direct integration with SageMaker's distributed training infrastructure. This format supports sparse data representation, which is critical for high-dimensional user-item interaction data, and enables faster I/O and reduced memory overhead compared to text-based formats.

Answer

Libsvm format with user_id and item_id as features

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CSV file with user_id, item_id, and label columns

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JSON lines file with user_id, item_id, and label fields

Question 8

A machine learning team is training a deep learning model on Amazon SageMaker and notices that the training loss is decreasing but the validation loss is increasing. What is the most likely cause?

Accepted Answer

Overfitting the training data. When training loss continues to decrease while validation loss increases, the model is memorizing the training data rather than learning generalizable patterns. This is the classic symptom of overfitting, where the model's capacity exceeds what is needed for the underlying data distribution, causing it to fit noise in the training set. In Amazon SageMaker, this can be observed by monitoring the validation loss metric during training jobs.

Answer

Vanishing gradients

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Learning rate is too high

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Underfitting the training data

Question 9

A company is using Amazon SageMaker to tune hyperparameters for a gradient boosting model. The objective is to minimize root mean squared error (RMSE). The data scientist wants to explore the hyperparameter space efficiently. Which THREE hyperparameter tuning strategies should the data scientist consider? (Choose 3.)

Accepted Answer

Bayesian optimization. Bayesian optimization is correct because it builds a probabilistic model of the objective function (RMSE) and uses an acquisition function to select the next hyperparameter combination to evaluate. This approach is sample-efficient, making it ideal for expensive-to-evaluate models like gradient boosting, as it balances exploration and exploitation to find optimal hyperparameters with fewer trials.

Answer

Grid search

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Manual search

Question 10

A data scientist is training a binary classification model using Amazon SageMaker. The dataset is highly imbalanced (99% negative class, 1% positive class). The model currently achieves 99% accuracy but fails to detect most positive cases. Which metric should the data scientist primarily use to evaluate model performance?

Accepted Answer

F1 score. In highly imbalanced datasets (99% negative, 1% positive), accuracy is misleading because a model can achieve 99% accuracy by simply predicting the majority class for all instances, failing to detect any positive cases. The F1 score (option B) is the harmonic mean of precision and recall, providing a balanced measure that penalizes models that trade off recall for precision or vice versa. This makes it the primary metric for evaluating binary classification performance on imbalanced data, as it directly reflects the model's ability to correctly identify positive cases while minimizing false positives.

Answer

ROC AUC

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Recall

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Accuracy