MLS-C01 Practice Exam 2 — 20 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.

Answer

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.

Answer

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

Answer

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.

Answer

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.

Answer

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.

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 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.

Answer

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

Answer

CSV file with user_id, item_id, and label columns

Answer

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

Answer

Learning rate is too high

Answer

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

Answer

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

Answer

Recall

Answer

Accuracy

Question 11

A team is building a product recommendation system using matrix factorization in Amazon SageMaker. They notice that the model's training loss decreases steadily but validation loss starts increasing after 5 epochs. What is the most likely cause?

Accepted Answer

Overfitting. In matrix factorization for recommendation systems, a decreasing training loss with an increasing validation loss after several epochs is a classic sign of overfitting. The model is memorizing the training data (including noise) rather than learning generalizable patterns, which degrades its performance on unseen validation data.

Answer

Underfitting

Answer

Not enough training data

Answer

Learning rate too high

Question 12

A company is using Amazon SageMaker to train a deep learning model on a large dataset. The training job is taking too long. The team wants to reduce training time without changing the model architecture. Which action should they take?

Accepted Answer

Use SageMaker's distributed training with multiple instances. SageMaker's distributed training with multiple instances splits the dataset and model computations across several machines, enabling parallel processing that significantly reduces wall-clock training time. This approach leverages data parallelism or model parallelism without altering the model architecture, directly addressing the need for faster training.

Answer

Increase the learning rate by a factor of 10

Answer

Reduce the number of epochs

Answer

Reduce the batch size

Question 13

A data scientist is deploying a regression model in Amazon SageMaker that predicts housing prices. The model shows high bias (underfitting). Which action is most likely to reduce bias?

Accepted Answer

Add more features or increase model complexity. High bias (underfitting) means the model is too simple to capture the underlying patterns in the data. Adding more features or increasing model complexity (e.g., using polynomial features, deeper trees, or a more flexible algorithm) directly addresses underfitting by giving the model greater capacity to learn from the data. In Amazon SageMaker, this could involve using a more complex built-in algorithm like XGBoost with deeper trees or adding feature engineering transformations in a processing job.

Answer

Reduce the amount of training data

Answer

Increase regularization strength

Answer

Use a simpler model

Question 14

A machine learning engineer is training a neural network on Amazon SageMaker using a custom Docker container. The training job fails with an error: 'CUDA out of memory.' The training instance is an ml.p3.2xlarge with 16 GB GPU memory. The model and data fit into memory when using batch size 32, but the engineer wants to maximize GPU utilization. Which approach should the engineer use to fix the out-of-memory error while maintaining efficient training?

Accepted Answer

Implement gradient accumulation with a larger effective batch size. Gradient accumulation allows the engineer to simulate a larger effective batch size by accumulating gradients over multiple forward/backward passes before performing an optimizer step. This keeps the per-step memory footprint low (avoiding CUDA out-of-memory) while maintaining training dynamics similar to a larger batch, thus maximizing GPU utilization without crashing.

Answer

Enable mixed precision training

Answer

Reduce batch size to 1

Answer

Use a CPU-only instance

Question 15

A data scientist is training a binary classifier on an imbalanced dataset where the positive class represents 1% of the data. The model currently achieves 99% accuracy but a recall of only 10% on the positive class. Which metric combination should the data scientist prioritize to evaluate model improvements?

Accepted Answer

F1 score and AUC-ROC. With a highly imbalanced dataset (1% positive class), 99% accuracy is misleading because the model can achieve it by simply predicting the majority class. The low recall (10%) indicates the model fails to identify most positive instances. The F1 score balances precision and recall, providing a single metric for minority class performance, while AUC-ROC evaluates the model's ability to distinguish between classes across all thresholds, making it robust to class imbalance.

Answer

Precision and recall at 90% precision

Answer

Accuracy and RMSE

Answer

Precision and RMSE

Question 16

A company wants to use Amazon SageMaker to train a deep learning model using a custom TensorFlow script. The data is stored in an S3 bucket. Which SageMaker API operation should be used to launch the training job?

Accepted Answer

CreateTrainingJob. The correct API operation to launch a training job in Amazon SageMaker is CreateTrainingJob. This operation specifies the training algorithm (or custom script), resource configuration (instance type and count), input data configuration (pointing to S3), and output location for the model artifacts. It directly initiates the training process on SageMaker-managed infrastructure.

Answer

CreateHyperParameterTuningJob

Answer

CreateEndpoint

Answer

CreateTransformJob

Question 17

A data scientist runs a SageMaker training job that fails with the above error. The S3 bucket and object exist, and the IAM role has s3:GetObject permission. What is the MOST likely cause?

Accepted Answer

The S3 object was uploaded with incorrect checksum or is corrupted. The error indicates that SageMaker cannot read the S3 object, even though the bucket and object exist and the IAM role has s3:GetObject permission. The most likely cause is that the object was uploaded with an incorrect checksum or is corrupted, which prevents SageMaker from verifying the integrity of the data during the training job initialization. SageMaker uses ETag (MD5 checksum) validation when reading objects, and a mismatch triggers a failure.

Answer

The training instance does not have internet access

Answer

The S3 bucket has versioning enabled and the object version is not specified

Answer

The IAM role lacks kms:Decrypt permission for an encrypted S3 object

Question 18

A data scientist is trying to run a SageMaker training job that writes output to an S3 bucket 'my-bucket'. The IAM policy is shown. The training job fails with an AccessDenied error when trying to write to S3. What is the reason?

Accepted Answer

The S3 bucket is encrypted with AWS KMS and the policy does not include kms:GenerateDataKey. The correct answer is A because the training job fails with an AccessDenied error when writing to an S3 bucket that is encrypted with AWS KMS. The IAM policy shown must include the `kms:GenerateDataKey` permission to allow the SageMaker training job to generate a data key for encrypting the output objects. Without this KMS permission, the S3 PutObject operation is denied even if the policy allows `s3:PutObject`, as KMS encryption requires explicit authorization to use the customer master key (CMK).

Answer

The policy does not allow s3:ListBucket

Answer

The policy does not allow s3:PutObject

Answer

The policy does not allow s3:PutObjectAcl

Question 19

A data scientist is building a multi-class classification model with 10 classes. The dataset has 100,000 samples. After training a random forest with 100 trees, the model achieves 85% accuracy on the test set. However, the data scientist notices that for one rare class (1% of data), recall is only 5%. Which technique is MOST likely to improve recall for the rare class without significantly reducing overall accuracy?

Accepted Answer

Apply SMOTE to oversample the rare class in the training data. SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the rare class by interpolating between existing minority instances, which directly addresses the class imbalance. This increases the model's exposure to the rare class during training, improving recall without discarding data or significantly altering the overall class distribution, thus preserving overall accuracy.

Answer

Increase the number of trees to 500

Answer

Use stratified sampling only for the test set

Answer

Reduce the decision threshold for the rare class to 0.1

Question 20

A company uses an XGBoost model to predict equipment failures. The model has high precision but low recall. The business impact of a false negative is very high (missing a failure). Which action would MOST effectively increase recall while keeping precision reasonably high?

Accepted Answer

Decrease the probability threshold for the positive class. Decreasing the probability threshold for the positive class means the model will classify a case as a failure at a lower predicted probability, which captures more true positives (increases recall). However, this also allows more false positives, so precision may drop, but the trade-off is acceptable given the high cost of false negatives. This is a standard post-training calibration technique for imbalanced classification problems.

Answer

Increase the regularization parameter lambda

Answer

Set the objective to 'reg:squarederror'

Answer

Increase the number of boosting rounds