MLS-C01 Practice Test 12 — 15 Questions

Question 1

A data scientist needs to run complex ETL transformations on a large dataset stored in Amazon S3. The transformations are written in PySpark and require occasional access to Hive metastore. The solution should minimize operational overhead and allow the data scientist to focus on code development. Which AWS service should be used?

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

Accepted Answer

Amazon EMR. Amazon EMR is the correct choice because it natively supports PySpark and Hive metastore integration, allowing the data scientist to run complex ETL transformations on large datasets stored in S3 with minimal operational overhead. EMR provides managed clusters that automatically scale and handle infrastructure, enabling the data scientist to focus on code development rather than cluster management.

Answer

Amazon Redshift

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AWS Glue

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Amazon SageMaker

Question 2

A data scientist trains a convolutional neural network (CNN) for image classification. The training loss decreases steadily, but the validation loss starts increasing after 10 epochs. Which technique should the data scientist use to address this problem?

Accepted Answer

Use early stopping to halt training when validation loss stops decreasing.. Early stopping halts training when validation loss stops improving, preventing overfitting. Option A (increasing data augmentation) may help reduce overfitting but does not directly address the already occurring validation loss increase. Option C (more epochs) would worsen overfitting. Option D (more convolutional layers) increases model capacity, likely worsening overfitting. Option E (higher learning rate) may cause divergence or instability.

Answer

Add more data augmentation to the training set.

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Increase the number of training epochs.

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Add more convolutional layers to increase model capacity.

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Increase the learning rate.

Question 3

A data scientist is using Amazon SageMaker to train a model on a dataset that contains both numerical and categorical features. The categorical features have high cardinality (e.g., postal codes, product IDs). Which feature engineering approach is most suitable for handling these high-cardinality categorical features in a tree-based model?

Accepted Answer

Use label encoding. Label encoding is suitable for tree-based models because these models split on feature values and can handle ordinal relationships implicitly. Unlike linear models, tree-based models do not assume any distance metric between categories, so label encoding avoids the dimensionality explosion of one-hot encoding while preserving the ability to capture splits based on high-cardinality features.

Answer

One-hot encode the categorical features

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Apply target encoding

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Apply binary encoding

Question 4

A data scientist is using Amazon SageMaker to train a model using the built-in XGBoost algorithm. The training job is taking a long time. The data scientist notices that the input data is in CSV format and the training job is using File mode. The data size is 50 GB. What is the BEST way to reduce training time?

Accepted Answer

Switch the input mode to Pipe.. Pipe mode streams data directly from S3 to the training algorithm, reducing I/O overhead and improving throughput compared to File mode, which downloads the entire dataset first. Option A (larger instance) may not help if the bottleneck is I/O rather than compute. Option B (Parquet format) can improve performance but is not as impactful as Pipe mode for streaming. Option C (reduce features) could reduce training time but at the cost of model accuracy, making it not the best approach.

Answer

Use a larger instance type with more vCPUs.

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Convert the data to Parquet format.

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Reduce the number of features in the dataset.

Question 5

A data scientist is building a deep learning model using Amazon SageMaker. The model is overfitting the training data. Which THREE actions can help reduce overfitting?

Accepted Answer

Add L2 regularization to the loss function.. Overfitting can be reduced by regularization techniques such as L2 regularization (Option A) which penalizes large weights, by dropout (Option E) which randomly ignores neurons during training, and by data augmentation (Option B) which increases the effective size of the training dataset by creating modified copies. Increasing model complexity by adding layers (Option C) would worsen overfitting, and reducing the learning rate (Option D) does not directly address overfitting—it affects convergence speed.

Answer

Increase the number of layers in the network.

Answer

Reduce the learning rate.

Question 6

A data scientist is using Amazon SageMaker to deploy a custom model container. The model is a large transformer that requires 16 GB of memory. The scientist wants to minimize inference latency. Which SageMaker hosting option should they choose?

Accepted Answer

Use a real-time endpoint with an instance that has sufficient memory.. For a large model requiring 16GB memory and minimal inference latency, a real-time endpoint with a suitably sized instance (e.g., ml.p3.2xlarge or ml.g4dn.xlarge) provides dedicated resources and low latency. Option B (asynchronous inference) adds queuing latency and is for non-real-time. Option C (Serverless Inference) has memory limits (up to 6 GB) and may have cold starts, not suitable for a 16GB model. Option D (batch transform) is for offline inference on batches, not real-time.

Answer

Use an asynchronous inference endpoint.

Answer

Use SageMaker Serverless Inference.

Answer

Use a batch transform job.

Question 7

An e-commerce company wants to build a recommendation system. They have user-item interaction data (clicks, purchases) and user demographic data. The goal is to recommend items that a user is likely to purchase. Which approach should be used?

Accepted Answer

Factorization Machines using user-item interactions and user features.. Factorization Machines are designed for high-dimensional sparse data and can effectively combine user-item interactions with side features like user demographics. Option A is wrong because linear regression is not suitable for implicit feedback or modeling interactions. Option B is wrong because collaborative filtering (e.g., matrix factorization) does not naturally incorporate user demographic features. Option D is wrong because content-based filtering only uses item features and ignores user-item interaction patterns.

Answer

Linear regression on user and item features.

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Collaborative filtering using matrix factorization.

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Content-based filtering using item features.

Question 8

Refer to the exhibit. A data scientist is trying to run a SageMaker training job using a script that reads training data from 's3://my-bucket/training/data.csv'. The job fails with an access denied error. What is the MOST likely reason?

Accepted Answer

The S3 bucket policy may deny access, or the IAM role lacks necessary permissions beyond GetObject.. The most likely reason for the access denied error is that the S3 bucket policy may deny access, or the IAM role used by SageMaker lacks the necessary permissions beyond s3:GetObject. The script reads from a specific path, but the role might not have permissions to list the bucket or access other required objects. Option B is incorrect because the error is about access denied, not VPC endpoint configuration. Option C is incorrect because the CreateTrainingJob action is allowed. Option D is incorrect because the S3 path is correctly formatted.

Answer

The training job is running in a VPC without S3 VPC endpoint.

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The sagemaker:CreateTrainingJob action is not allowed on the specific resource.

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The S3 path is incorrectly formatted.

Question 9

A machine learning engineer is tuning a Gradient Boosting model for a regression task. The dataset contains 50 features and 100,000 samples. The engineer wants to speed up training without sacrificing predictive performance significantly. Which THREE hyperparameters should the engineer consider adjusting? (Choose THREE.)

Accepted Answer

Reduce the subsample ratio (e.g., from 1.0 to 0.5). (subsample) uses a fraction of samples per tree, reducing both overfitting and training time. Option D (max_depth) limits tree depth, which directly reduces computation. Option E (max_features) restricts the number of features considered for each split, lowering tree complexity. Option B (learning_rate and n_estimators) trades off; although increasing learning rate can reduce training time, it often requires careful adjustment and may hurt performance. Option C (n_estimators) directly increases training time, which is the opposite of the goal.

Answer

Increase learning_rate and decrease n_estimators proportionally

Answer

Increase the number of estimators

Question 10

A company is training a deep learning model on Amazon SageMaker using a large dataset stored in S3. The training job is failing with an error indicating insufficient memory. The model architecture and hyperparameters are fixed. Which change is MOST likely to resolve the issue without modifying the model code?

Accepted Answer

Enable SageMaker's distributed data parallelism.. Enable SageMaker's distributed data parallelism. Since the model architecture and hyperparameters are fixed, the insufficient memory error likely arises because the dataset is too large to fit into the memory of a single instance. Distributed data parallelism splits the training data across multiple instances, allowing each instance to process a smaller subset, thereby reducing per-instance memory usage without any code modifications. Option B (managed Spot training) reduces cost but does not address memory. Option C (using a larger instance) could provide more memory but may be more expensive and does not directly solve the root cause of data size relative to fixed hyperparameters. Option D (Pipe mode) improves data streaming efficiency but does not reduce the memory required for model parameters or intermediate activations.

Answer

Use managed Spot training to get cheaper compute.

Answer

Use a larger instance type with more memory.

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Use Pipe mode for input data instead of File mode.

Question 11

A team is training a linear regression model to predict house prices. After training, they observe that the model has high bias (underfitting). Which action is most likely to reduce bias?

Accepted Answer

Add more relevant features and increase model complexity.. High bias (underfitting) means the model is too simple to capture the underlying patterns in the data. Adding more relevant features and increasing model complexity (e.g., using polynomial features or more interaction terms) gives the linear regression model greater capacity to fit the training data, directly reducing bias. This aligns with the bias-variance tradeoff, where increasing complexity lowers bias at the cost of potentially increasing variance.

Answer

Increase the regularization strength.

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Reduce the amount of training data.

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Decrease the number of model parameters.

Question 12

A data scientist is training a binary classification model on a highly imbalanced dataset (0.1% positive class). To improve recall, the team decides to use SageMaker's built-in XGBoost algorithm. Which parameter adjustment is most likely to increase recall without significantly sacrificing precision?

Accepted Answer

Set scale_pos_weight to the ratio of negative to positive samples. Setting scale_pos_weight to the ratio of negative to positive samples (approximately 999:1) tells XGBoost to assign a higher penalty to misclassifications of the minority positive class. This directly increases the gradient contribution from positive samples during training, which shifts the decision boundary to improve recall while maintaining a balance that avoids excessive false positives, thus preserving precision.

Answer

Increase max_depth from 5 to 10

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Reduce num_round from 100 to 50

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Increase subsample from 0.8 to 1.0

Question 13

A data scientist is training a text classification model using a bag-of-words approach. The dataset contains 1 million documents and 100,000 unique words. The resulting feature matrix is very sparse. Which technique should the data scientist use to reduce the dimensionality of the feature space?

Accepted Answer

Use word embeddings to represent documents. Word embeddings (e.g., Word2Vec, GloVe) map words to dense, low-dimensional vectors that capture semantic relationships, effectively reducing the 100,000-dimensional sparse bag-of-words feature space to a much smaller dense representation (e.g., 100–300 dimensions). This directly addresses the sparsity and high dimensionality of the term-document matrix while preserving meaningful word context.

Answer

Apply TF-IDF transformation

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Remove stop words from the vocabulary

Answer

Apply Principal Component Analysis (PCA) to the term-document matrix

Question 14

A data scientist is exploring a dataset with 50 features. Which TWO EDA techniques are most effective for detecting multicollinearity?

Accepted Answer

Scatter plots of each feature pair. Options C and E are the most effective EDA techniques for detecting multicollinearity. Scatter plots of each pair of features (C) allow visual inspection of linear relationships between features. A correlation matrix displayed as a heatmap (E) provides a quantitative measure of pairwise correlations, making it easy to spot high correlations indicative of multicollinearity. Option A (box plots) shows univariate distributions and does not reveal relationships between features. Option B (VIF analysis) is a formal statistical test for multicollinearity, but it is not typically considered an EDA technique; EDA focuses on visual exploration. Option D (histograms) similarly only show univariate distributions.

Answer

Box plots of each feature

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Variance Inflation Factor (VIF) analysis

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Histograms of each feature

Question 15

A data scientist is using Amazon SageMaker to train a natural language processing model using a custom Docker container. The training script reads data from an S3 bucket and writes checkpoints to an S3 bucket. The training job is failing with the error 'Unable to write to checkpoint path: s3://my-bucket/checkpoints/'. The IAM role associated with the training job has the following policy: {'Effect': 'Allow', 'Action': 's3:PutObject', 'Resource': 'arn:aws:s3:::my-bucket/checkpoints/*'}. The bucket 'my-bucket' exists and the prefix 'checkpoints/' is empty. What is the most likely cause of the failure?

Accepted Answer

The IAM role is missing the s3:ListBucket permission. The error 'Unable to write to checkpoint path' occurs because the SageMaker training job's IAM role lacks the `s3:ListBucket` permission. Even though the role has `s3:PutObject` on the checkpoint prefix, SageMaker's S3 client first performs a `ListObjects` (or `HeadObject`) call to verify the bucket exists and to check the prefix state before writing. Without `s3:ListBucket` on the bucket itself, the API call fails, causing the write operation to abort.

Answer

The IAM role does not have s3:PutObject permission

Answer

The S3 bucket does not exist

Answer

The checkpoint prefix already contains objects