MLS-C01 Practice Test 22 — 15 Questions

Question 1

Refer to the exhibit. An IAM policy is attached to a SageMaker execution role. A data scientist tries to create a training job that reads training data from s3://my-bucket/confidential/data.csv. What will happen?

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

Accepted Answer

The training job will fail with an access denied error. The policy allows s3:GetObject on my-bucket/* but explicitly denies s3:GetObject on my-bucket/confidential/*. Since explicit Deny overrides any Allow, the training job will fail with an access denied error.

Answer

The training job will succeed because there is an Allow on my-bucket/*

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The training job will succeed because the Deny statement is invalid

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The training job will fail because the role lacks sagemaker:CreateTrainingJob

Question 2

You are building a CI/CD pipeline for SageMaker using AWS CodePipeline. Which THREE components are essential for a fully automated model training and deployment pipeline?

Accepted Answer

AWS CodeCommit to store the training script and model code. AWS CodeCommit is essential because it provides a secure, scalable Git repository to store the training script and model code, which serves as the source stage in the CI/CD pipeline. This ensures version control and triggers the pipeline automatically on code changes, enabling a fully automated workflow.

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AWS CodeDeploy to deploy the model to an endpoint

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AWS CloudFormation to define the infrastructure

Question 3

A data scientist is building a regression model to predict house prices. The dataset contains 10 features, including 'number_of_bedrooms' and 'square_footage'. The scientist observes that the model has high variance. Which TWO actions are most appropriate to reduce overfitting? (Choose TWO.)

Accepted Answer

Reduce model complexity by using a simpler model. A is correct because reducing model complexity, such as using a simpler model (e.g., linear regression instead of a high-degree polynomial), directly decreases variance by limiting the model's capacity to fit noise in the training data. This aligns with the bias-variance tradeoff, where simpler models have higher bias but lower variance, making them less prone to overfitting.

Answer

Increase the number of training epochs

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Decrease the amount of training data

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Add more polynomial features

Question 4

A data scientist runs the AWS CLI command shown in the exhibit. The output shows that job-2 failed. Which action should the data scientist take to diagnose the failure?

Accepted Answer

Run `aws sagemaker describe-training-job --training-job-name job-2` to see the failure reason. The `describe-training-job` API call returns a `FailureReason` field that provides the specific error message for a failed SageMaker training job. This is the most direct and efficient way to diagnose why job-2 failed, as it retrieves the exact failure reason from the SageMaker service without requiring additional log parsing or bucket inspection.

Answer

Check the CloudWatch Logs log group for job-2

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Check the S3 bucket for any error logs uploaded by the training job

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Run `aws sagemaker list-training-jobs --name-contains job-2` to get more details

Question 5

A company is building a fraud detection model. The dataset is highly imbalanced (99% legitimate, 1% fraud). The data scientist trains a model using Amazon SageMaker's built-in XGBoost algorithm. The model achieves 99% accuracy but only catches 10% of fraud cases. Which technique should the data scientist apply to improve recall for the minority class?

Accepted Answer

Set the scale_pos_weight hyperparameter in XGBoost.. Setting the scale_pos_weight hyperparameter in XGBoost adjusts the weight of the positive (minority) class during training, effectively penalizing misclassifications of fraud cases more heavily. This directly addresses the class imbalance by forcing the model to focus on the minority class, which improves recall without altering the dataset distribution. The current 99% accuracy with only 10% fraud recall indicates the model is biased toward the majority class, and scale_pos_weight is the most direct and efficient fix within XGBoost.

Answer

Use random under-sampling of the majority class.

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Use mean squared error as the objective function.

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Use SMOTE to oversample the minority class.

Question 6

A media company uses Amazon SageMaker to train a deep learning model for video classification. The training job uses a single ml.p3.2xlarge instance and processes 50 GB of labeled video data stored in Amazon S3. The training completes successfully in 12 hours. However, the data scientists report that the model’s accuracy is lower than expected. They suspect the training data contains labeling errors. To improve model accuracy without incurring significant additional cost, they want to identify and remove mislabeled training examples before retraining. They have a small budget of $50 and need to complete the analysis within 2 hours. Which approach should the data scientists take?

Accepted Answer

Train a small, fast model on a random sample of the data (e.g., 1 GB) using a cheaper instance like ml.m5.xlarge, then use the model's prediction confidence to flag low-confidence examples as potential mislabels for manual review.. Training a small, fast model on a 1 GB random sample using a cheaper instance (ml.m5.xlarge) allows the team to quickly identify low-confidence predictions, which are strong indicators of mislabeled examples. This approach fits within the $50 budget and 2-hour time constraint, as it avoids processing the full 50 GB dataset and leverages a lightweight model for rapid iteration. By flagging only suspicious samples for manual review, the team can efficiently clean the training data without incurring the cost of re-labeling the entire dataset.

Answer

Use SageMaker Ground Truth to create a new labeling job for the entire dataset, then compare the new labels with the original labels to identify discrepancies.

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Use SageMaker Clarify to generate a bias report for the training data and remove instances that contribute to bias.

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Manually review all 50 GB of video data to correct labels.

Question 7

A team is using Amazon SageMaker to train a model on a dataset that is 500 GB in size, stored as CSV files in S3. The training job takes 2 hours using a single ml.p3.2xlarge instance. The team wants to reduce training time to under 30 minutes. The model architecture supports distributed training. Which solution will achieve this goal with the LEAST amount of code changes?

Accepted Answer

Use multiple ml.p3.2xlarge instances with SageMaker's distributed data parallelism library, enabling automatic sharding of the training data.. SageMaker's distributed data parallelism library automatically shards the training data across multiple ml.p3.2xlarge instances, enabling parallel gradient computation and reducing wall-clock training time from 2 hours to under 30 minutes without requiring manual code changes to the training script. The model architecture already supports distributed training, so the library handles the communication and synchronization (e.g., AllReduce) transparently.

Answer

Use managed spot training to reduce cost and then use cost savings to train with a larger instance.

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Use a single ml.p3.16xlarge instance with more GPUs and memory.

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Change the input mode to Pipe mode to stream data from S3 directly, reducing I/O wait time.

Question 8

A data scientist is trying to create a SageMaker training job using an execution role with the attached IAM policy. The training job fails with an access denied error when trying to read training data from the S3 bucket 'my-bucket'. What is the most likely cause?

Accepted Answer

The S3 bucket policy explicitly denies access to the role.. The most likely cause is that the S3 bucket policy explicitly denies access to the SageMaker execution role. Even if the IAM policy grants the necessary permissions, an explicit deny in the bucket policy overrides any allow, resulting in an access denied error when the training job attempts to read training data from the S3 bucket.

Answer

The IAM policy does not include s3:ListBucket permission.

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The S3 bucket is in a different AWS account.

Answer

The sagemaker:CreateTrainingJob action is not allowed.

Question 9

A company deploys a real-time inference endpoint using Amazon SageMaker with an ML model that has strict latency requirements. The endpoint currently uses a single ml.c5.xlarge instance. During a load test, the p99 latency exceeds the 100ms threshold. The team adds more instances but latency does not improve because the model is heavily CPU-bound. What is the MOST cost-effective change to meet the latency requirement?

Accepted Answer

Change the instance type to a GPU instance such as ml.g4dn.xlarge.. The model is CPU-bound, meaning the bottleneck is compute capacity, not throughput. GPU instances like ml.g4dn.xlarge offload parallel computation from the CPU, significantly reducing per-inference latency. This directly addresses the root cause without over-provisioning, making it the most cost-effective solution.

Answer

Use a multi-model endpoint to serve multiple models on the same instance.

Answer

Enable automatic scaling based on inference latency.

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Increase the number of instances and use a target tracking scaling policy.

Question 10

A company is using Amazon SageMaker to deploy a model for real-time inference. The model takes 200 ms to respond, but the requirement is 100 ms. Which THREE actions could reduce latency? (Choose THREE.)

Accepted Answer

Use a larger instance with more compute capacity. Using a larger instance with more compute capacity reduces inference time by providing more CPU/GPU power. Option B: Pruning removes unnecessary weights, reducing model size and computation. Option D: SageMaker Neo compiles the model to optimize for the target instance, improving performance. Option C is incorrect because CPU instances are generally slower than GPU instances for deep learning models. Option E is incorrect because increasing batch size typically increases latency for real-time inference as it waits for more samples.

Answer

Switch to a CPU-based instance

Answer

Increase the batch size for inference

Question 11

A data scientist is deploying a real-time inference endpoint using SageMaker. The model is a large NLP model requiring GPU for low latency. The endpoint must be highly available across two Availability Zones. Which deployment configuration meets these requirements?

Accepted Answer

Deploy a multi-model endpoint on an ml.p3.2xlarge instance with auto-scaling and at least two instances in different AZs. It uses a GPU instance (ml.p3.2xlarge) to meet the low-latency requirement for a large NLP model, and it deploys at least two instances across different Availability Zones (AZs) to achieve high availability. SageMaker multi-model endpoints allow hosting multiple models on the same endpoint, but here the key is the instance type and the multi-instance, multi-AZ deployment for fault tolerance.

Answer

Deploy a single model endpoint on an ml.c5.xlarge instance with auto-scaling

Answer

Use SageMaker batch transform on GPU instances

Answer

Deploy a single model endpoint on an ml.p3.2xlarge instance with one instance

Question 12

A company is training a deep learning model on Amazon SageMaker. The training job is failing with an out-of-memory error. Which SageMaker feature should the company use to resolve this issue without changing the instance type?

Accepted Answer

Use SageMaker distributed training with model parallelism. SageMaker's distributed training with model parallelism splits the model's layers across multiple GPUs, reducing the memory footprint per GPU. This allows the company to train a large model that exceeds a single GPU's memory without changing the instance type. Model parallelism is specifically designed to handle out-of-memory errors by distributing the model parameters, gradients, and optimizer states across devices.

Answer

Use SageMaker Savings Plans

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Enable SageMaker Managed Spot Training

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Use SageMaker Debugger to monitor memory usage

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Enable SageMaker Profiler to profile memory

Question 13

Which TWO of the following are benefits of using SageMaker Managed Spot Training? (Select TWO.)

Accepted Answer

Potential for significant cost savings. SageMaker Managed Spot Training leverages spare AWS EC2 compute capacity at a significantly reduced price compared to on-demand instances, often achieving cost savings of 60-90%. This makes it a highly cost-effective option for training machine learning models, especially when the training job is fault-tolerant and can handle interruptions.

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No need to checkpoint the model

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Faster training times

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Guaranteed instance availability

Question 14

A company is using SageMaker to host a model for real-time inference. They notice that the endpoint's latency increases over time. The model is stateless and the inference code does not log any errors. What is the MOST likely cause?

Accepted Answer

Memory leak in the inference container. A memory leak in the inference container causes the process's resident memory to grow over time as allocated memory is not freed. Since the model is stateless and no errors are logged, the leak is likely in the inference code or a dependency (e.g., a TensorFlow session or a Python list that grows unbounded). As memory pressure increases, the operating system may swap or the container may be OOM-killed, leading to increased garbage collection pauses and higher latency for each request.

Answer

Gradual increase in request payload size

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Endpoint auto scaling is adding new instances

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Model is accumulating state from previous requests

Question 15

A machine learning engineer is using AWS Step Functions to orchestrate a SageMaker training job followed by a Lambda function for post-processing. The training job completes successfully, but the Lambda function fails with a timeout error. What is the MOST likely cause?

Accepted Answer

The Lambda function execution time exceeds the maximum timeout limit. The Lambda function failed with a timeout error, which directly indicates that its execution duration exceeded the configured maximum timeout limit (default 3 seconds, max 15 minutes). This is the most likely cause because the error message explicitly states 'timeout', and Lambda enforces a hard timeout that terminates the function if it runs longer than the configured limit.

Answer

The Lambda function's IAM role lacks permissions to access the training output

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The Step Functions state machine has a misconfigured retry policy

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The SageMaker training job output data is too large for Lambda to process