MLS-C01 Practice Exam 61 — 20 Questions

Question 1

A data scientist wants to use AWS Step Functions to orchestrate a machine learning workflow including data preprocessing, training, and evaluation. Which SageMaker integration is best suited for this purpose?

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

Accepted Answer

Use the SageMaker SDK with Step Functions service integrations. AWS Step Functions has a native service integration with SageMaker, allowing you to directly call SageMaker API actions (e.g., CreateTrainingJob, CreateModel, CreateEndpointConfig, CreateEndpoint) without needing intermediate Lambda functions. This integration supports both synchronous and asynchronous invocations, making it ideal for orchestrating a multi-step ML workflow with built-in error handling, retries, and state management.

Answer

Implement each step as an AWS Lambda function and call Step Functions

Answer

Use SageMaker Pipelines to define the workflow

Answer

Use AWS Batch to run the steps sequentially

Question 2

Which TWO of the following are true about the bias-variance tradeoff?

Accepted Answer

High variance can cause overfitting. The bias-variance tradeoff describes the balance between underfitting (high bias) and overfitting (high variance). Simple models have high bias and low variance, leading to underfitting. Complex models have low bias and high variance, leading to overfitting. Ensemble methods like bagging reduce variance by averaging multiple models. Therefore:
- A is false: Bagging reduces variance, not increases.
- B is false: Simple models have low variance, not high.
- C is true: High variance causes the model to fit noise, i.e., overfitting.
- D is true: High bias causes the model to miss relevant patterns, i.e., underfitting.
- E is false: High variance models are typically too complex, not too simple.

Answer

Ensemble methods like bagging increase variance

Answer

Simple models tend to have high variance

Answer

High variance models are typically too simple

Question 3

A data scientist is training a random forest classifier on Amazon SageMaker and wants to reduce overfitting. Which TWO actions should the scientist take? (Choose TWO.)

Accepted Answer

Limit the maximum depth of trees. To reduce overfitting in a random forest, limit the maximum depth of trees (Option D) to prevent them from growing too deep and capturing noise. Increasing the number of trees (Option E) reduces the variance of the ensemble, lowering overfitting risk. Option A is incorrect because increasing the number of features considered per split can increase tree correlation and overfitting. Option B is incorrect because increasing maximum depth makes trees more complex and prone to overfitting. Option C is incorrect because decreasing the number of trees increases variance, which can worsen overfitting.

Answer

Increase the number of features considered per split

Answer

Increase the maximum depth of trees

Answer

Decrease the number of trees

Question 4

A data scientist is training a gradient boosting model using SageMaker's built-in XGBoost algorithm. The model is overfitting on the training data. Which hyperparameter adjustment is most likely to reduce overfitting?

Accepted Answer

Increase lambda (L2 regularization). Increasing the L2 regularization term (lambda) penalizes large weights, which helps reduce overfitting. Option A is incorrect because increasing the learning rate (eta) can cause the model to converge too quickly and may lead to overfitting if not paired with proper regularization. Option B is incorrect because increasing max_depth increases model complexity, which typically worsens overfitting. Option C is incorrect because increasing num_round (number of boosting rounds) allows the model to fit the training data more closely, increasing the risk of overfitting.

Answer

Increase learning rate (eta)

Answer

Increase max_depth

Answer

Increase num_round

Question 5

A data scientist wants to deploy a PyTorch model for real-time inference with latency under 100 ms. Which AWS service is most suitable?

Accepted Answer

Amazon SageMaker real-time endpoint. Amazon SageMaker real-time endpoints are designed for low-latency inference, typically under 100 ms, by hosting a model behind an HTTPS endpoint that auto-scales based on traffic. They support PyTorch natively via pre-built containers or custom containers, making them the most suitable choice for this requirement.

Answer

Amazon SageMaker Processing

Answer

AWS Lambda with container image

Answer

Amazon SageMaker Batch Transform

Question 6

A machine learning engineer is deploying a model that predicts loan defaults. The model uses features like income, credit score, and debt-to-income ratio. After deployment, the model's performance degrades over time. Which concept best describes this phenomenon?

Accepted Answer

Model drift. Model drift is the correct answer because it is the general term for degradation in model performance over time, often caused by changes in data distributions or relationships between features and the target. This phenomenon includes both data drift (changes in input distribution) and concept drift (changes in the relationship between inputs and the target). Option A (Data drift) is a specific type of model drift focusing on input features, not the overall degradation. Option B (Concept drift) is another specific type concerning the target relationship. Option C (Overfitting) is a training-time issue where the model fits noise, not a time-dependent degradation after deployment.

Answer

Data drift

Answer

Concept drift

Answer

Overfitting

Question 7

A company is using Amazon SageMaker to train a machine learning model. The training job is configured to use the File mode to download data from S3 to the training instances. The training data is stored in a single S3 bucket with multiple prefixes. Which TWO actions are required to ensure the training job can access the data? (Choose TWO.)

Accepted Answer

Grant the SageMaker execution role s3:GetObject permission for the data bucket.. Options A and C are correct. Option A: The SageMaker execution role must have the s3:GetObject permission for the data bucket to read the training data. Option C: When using File mode, the training job must specify the S3 data channel with the correct prefix to indicate the location of the data. Option B is incorrect because Pipe mode is not required for File mode. Option D is incorrect because concatenating all data into a single file is unnecessary for File mode. Option E is incorrect because File mode does not require RecordIO-protobuf format.

Answer

Configure the training job to use Pipe mode.

Answer

Concatenate all data files into a single file.

Answer

Convert the data to RecordIO-protobuf format.

Question 8

A data scientist is training a binary classification model on a dataset with 100 features and 10,000 samples. The model achieves 99% accuracy on the training set but only 65% on the test set. Which technique should be applied first to address this issue?

Accepted Answer

Apply L2 regularization to the model. The symptoms indicate overfitting. Regularization (L1/L2) is a direct method to reduce overfitting by penalizing large coefficients. Option A is wrong because reducing the size of the training dataset would worsen overfitting. Option B is wrong because increasing the number of trees in a random forest could help reduce overfitting in some cases, but it's not the first technique to apply; regularization is more direct. Option D is wrong because adding more features would increase model complexity and worsen overfitting.

Answer

Reduce the size of the training dataset

Answer

Increase the number of trees in a random forest

Answer

Add more features to the model

Question 9

A machine learning team is using Amazon SageMaker to train a model with a large dataset stored in S3. The training job is taking too long. Which THREE of the following actions can reduce training time? (Choose three.)

Accepted Answer

Use a GPU instance with more powerful GPUs.. Using a GPU instance with more powerful GPUs (Option B) reduces training time because it increases the parallel compute capacity for matrix operations, which are the core of deep learning. Amazon SageMaker allows you to select instances like p3.16xlarge with NVIDIA V100 GPUs, which offer significantly higher FLOPS compared to smaller GPU instances, directly accelerating model training.

Answer

Decrease the batch size.

Answer

Increase the batch size.

Question 10

A company uses Amazon SageMaker to train a model for detecting fraudulent transactions. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraudulent). Which approach is most effective to address this imbalance?

Accepted Answer

Apply SMOTE to generate synthetic samples. SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, effectively balancing the dataset without causing overfitting. Option A is less effective because while class weights can help, they do not increase the number of training examples for the minority class. Option C is wrong because random oversampling duplicates existing samples, which can lead to overfitting. Option D is not always feasible or effective as collecting more data may not be possible and does not guarantee balance.

Answer

Use class weights in the loss function

Answer

Random oversampling of the minority class

Answer

Collect more data for the minority class

Question 11

Which THREE measures can help reduce inference latency for a deep learning model deployed on SageMaker real-time endpoints? (Select THREE.)

Accepted Answer

Enable SageMaker Neo to compile the model.. A is correct because SageMaker Neo compiles the trained model into an optimized binary for the target hardware (e.g., CPU, GPU, or Inferentia), using Apache TVM to fuse operations and prune unused computations. This reduces inference latency by up to 2x without requiring code changes, making it a direct latency-reduction measure for real-time endpoints.

Answer

Increase the batch size for inference.

Answer

Use a multi-model endpoint to share the instance.

Question 12

A team is using Amazon SageMaker to train a deep learning model. The training job is taking too long, and they want to reduce training time without significant accuracy loss. They have already tried increasing the number of instances. Which technique should they consider next?

Accepted Answer

Gradient accumulation. Gradient accumulation, is correct because it allows the use of larger effective batch sizes without increasing memory usage, which can lead to faster convergence and reduced training time. Option A, increasing L2 regularization, does not directly reduce training time; it helps prevent overfitting. Option B, reducing model complexity, can reduce training time but may cause significant accuracy loss due to underfitting. Option D, early stopping, can reduce training time by stopping training early but does not address the core issue of slow training per epoch; it may also halt training before convergence, risking accuracy loss.

Answer

Increase L2 regularization

Answer

Reduce model complexity

Answer

Early stopping

Question 13

A data scientist is training a neural network on image data using TensorFlow with GPU instances on SageMaker. The training is slow because the GPU utilization is low. The data pipeline uses tf.data with a large number of preprocessing operations. Which action would most likely increase GPU utilization?

Accepted Answer

Increase the prefetch buffer size in the tf.data pipeline.. Increasing the prefetch buffer size in the tf.data pipeline allows the CPU to prepare batches in advance while the GPU is computing, reducing idle time and improving GPU utilization. Option A (increase learning rate) does not affect data throughput. Option C (reduce batch size) can decrease utilization as it reduces the amount of work per GPU step. Option D (increase number of CPU instances) addresses CPU capacity but the bottleneck is often data pipeline, not CPU count; increasing instances may not help. Option E (use smaller images) reduces computation per image but may not improve utilization percentage if the pipeline is the bottleneck.

Answer

Increase the learning rate to converge faster.

Answer

Reduce the batch size to speed up each step.

Answer

Increase the number of CPU instances in the training job.

Answer

Use smaller image sizes to reduce computation.

Question 14

A data scientist is using Amazon SageMaker to train a model. The training job is taking longer than expected. The scientist wants to reduce training time without changing the algorithm or the hardware. Which action is most likely to help?

Accepted Answer

Increase the batch size used during training.. Increasing the batch size allows the model to process more samples per gradient update, reducing the number of iterations per epoch and thus speeding up training. This is a common technique to reduce training time without changing the algorithm or hardware. While it may affect convergence, it is the most direct way among the options to shorten training time. Adding regularization (B) introduces extra computation and does not reduce time. Data augmentation (C) increases the dataset size, which increases training time. Reducing epochs (D) also reduces training time, but it decreases the number of times the model sees the data, which can significantly harm model performance, making it less desirable than increasing batch size.

Answer

Add regularization to the loss function.

Answer

Use data augmentation to increase the dataset size.

Answer

Reduce the number of training epochs.

Question 15

A company uses SageMaker to train a large language model. The training job is taking too long. The data scientist wants to use distributed training with data parallelism. Which SageMaker feature should be used?

Accepted Answer

SageMaker distributed training libraries. SageMaker's distributed training libraries support data parallelism, which allows training large models faster by splitting data across multiple GPUs. Option B (SageMaker Neo) is for model optimization and compilation, not for distributed training. Option C (SageMaker Processing) is for data preprocessing and postprocessing, not for training. Option D (SageMaker Debugger) is for monitoring and debugging training jobs, not for distributing training.

Answer

SageMaker Neo

Answer

SageMaker Processing

Answer

SageMaker Debugger

Question 16

Which THREE of the following are valid strategies to reduce overfitting in a deep neural network? (Choose 3)

Accepted Answer

Use early stopping.. Early stopping halts training when validation performance degrades, preventing overfitting. Option D is correct because L2 regularization adds a penalty on large weights, discouraging complexity. Option E is correct because dropout randomly drops neurons during training, reducing co-adaptation. Option A is wrong because adding more layers increases model capacity, which exacerbates overfitting. Option C is wrong because a higher learning rate can cause the loss to diverge and does not directly address overfitting.

Answer

Increase the number of layers.

Answer

Increase the learning rate.

Question 17

Which TWO of the following are best practices for hyperparameter tuning using Amazon SageMaker? (Choose 2)

Accepted Answer

Use early stopping to terminate poorly performing training jobs.. Early stopping terminates poorly performing training jobs, saving time and compute resources. Option E is correct because automatic model tuning with Bayesian optimization efficiently searches the hyperparameter space by focusing on promising regions. Option A is incorrect because grid search is exhaustive and computationally expensive, not a best practice. Option C is incorrect because tuning all algorithm hyperparameters can be unnecessary and inefficient; only relevant hyperparameters should be tuned. Option D is incorrect because while a larger dataset may improve model accuracy, it does not directly improve tuning efficiency; hyperparameter tuning optimizes the learning process, not data size.

Answer

Use grid search to exhaustively explore all combinations.

Answer

Include all algorithm hyperparameters in the tuning job.

Answer

Use a larger training dataset to improve tuning results.

Question 18

Refer to the exhibit. A data scientist is using Amazon SageMaker Ground Truth to label a dataset. The output manifest file references S3 objects with metadata. The scientist notices that a training job using the labeled data yields poor accuracy. What is the most likely issue?

Accepted Answer

The training data is in an incorrect format for the algorithm.. The poor accuracy is most likely due to the training data being in an incorrect format for the algorithm. Amazon SageMaker Ground Truth outputs a manifest file with metadata, but the source S3 objects may be in a format (e.g., raw images, text files) that is not directly compatible with the chosen built-in algorithm or custom model. For example, if the algorithm expects RecordIO-encoded data or a specific CSV structure, but the manifest points to raw JPEG images, the training job will still run (no failure) but produce poor results. Other options: missing labels or IAM issues would typically cause job failures, not just poor accuracy; data distribution shift is possible but less directly indicated by the exhibit.

Answer

The labeled dataset has missing labels for some records.

Answer

The IAM role used for training does not have permissions to read the manifest file.

Answer

The data distribution differs significantly between the training set and the real-world inference data.

Question 19

A data analyst is using Amazon SageMaker Studio to perform exploratory data analysis on a dataset stored in S3. The analyst wants to generate summary statistics and visualizations quickly. Which built-in feature of SageMaker Studio should the analyst use?

Accepted Answer

SageMaker Data Wrangler. SageMaker Data Wrangler is a built-in visual data preparation tool in SageMaker Studio that provides summary statistics, histograms, and correlation matrices without writing code. Option A (SageMaker Ground Truth) is for data labeling, not EDA. Option C (SageMaker Autopilot) automates machine learning model building. Option D (SageMaker Clarify) is for bias detection and model explainability.

Answer

SageMaker Ground Truth

Answer

SageMaker Autopilot

Answer

SageMaker Clarify

Question 20

During training, a binary classification model has an AUC of 0.99 on the training set but only 0.72 on the validation set. Which of the following is the most likely cause?

Accepted Answer

Overfitting.. A large gap between high training AUC (0.99) and lower validation AUC (0.72) indicates overfitting. Option A is wrong: class imbalance would affect both sets similarly or the model might ignore the minority class, but would not typically produce such a large gap. Option B is wrong: underfitting would show poor performance on both sets (e.g., AUC around 0.5-0.6). Option D is wrong: data leakage would inflate both training and validation metrics, not create a gap.

Answer

Class imbalance in the training set.

Answer

Underfitting.

Answer

Data leakage from validation to training.