MLS-C01 Machine Learning Implementation and Operations Practice Test 15 — 15 Questions

Question 1

A machine learning team is using SageMaker to build a model. They need to track hyperparameter tuning experiments, compare results, and visualize metrics. Which SageMaker feature should they use?

Accepted Answer

SageMaker Experiments. SageMaker Experiments is the correct answer because it provides experiment tracking, comparison, and visualization capabilities for hyperparameter tuning and other training runs. SageMaker Hyperparameter Tuning (option D) only automates the tuning process but does not track or compare experiments. SageMaker Debugger (option E) is used for debugging training issues, not for experiment tracking. SageMaker Model Monitor (option C) monitors deployed models for data drift and quality, not for tracking tuning experiments. SageMaker Ground Truth (option B) is for data labeling. Therefore, only SageMaker Experiments meets all the requirements.

Answer

SageMaker Ground Truth

Answer

SageMaker Model Monitor

Answer

SageMaker Hyperparameter Tuning

Answer

SageMaker Debugger

Question 2

A company has deployed a model on SageMaker for real-time inference. The endpoint is experiencing high latency during traffic spikes. Which action should the company take to reduce latency?

Accepted Answer

Enable SageMaker endpoint auto-scaling. Enabling SageMaker endpoint auto-scaling allows the endpoint to dynamically adjust the number of instances based on incoming traffic, which directly reduces latency during spikes by ensuring sufficient compute capacity is available. Auto-scaling uses CloudWatch metrics (e.g., InvocationsPerInstance or latency) to trigger scale-out events, preventing queue buildup and response time degradation.

Answer

Use a larger instance type for the endpoint

Answer

Attach SageMaker Elastic Inference to the endpoint

Answer

Use SageMaker Neo to compile the model

Answer

Switch to SageMaker batch transform

Question 3

A data scientist is using SageMaker to train a model with a custom algorithm. The training script uses TensorFlow and runs on GPU instances. The training job fails with 'CUDA_ERROR_OUT_OF_MEMORY'. What is the most likely cause?

Accepted Answer

The batch size is too large for the GPU memory. The error 'CUDA_ERROR_OUT_OF_MEMORY' indicates that the GPU memory has been exhausted during training. In TensorFlow, the batch size directly determines how many samples are processed simultaneously on the GPU; a batch size that is too large will exceed the available GPU memory, causing this specific CUDA error. Reducing the batch size is the standard fix for this issue.

Answer

The S3 bucket is in a different region

Answer

The GPU driver is outdated

Answer

The training script has a memory leak on CPU

Answer

The instance type does not have enough CPU cores

Question 4

A company wants to use SageMaker to host multiple models behind a single endpoint to reduce costs. Which SageMaker feature should they use?

Accepted Answer

SageMaker Multi-Model Endpoints. SageMaker Multi-Model Endpoints allow you to deploy multiple models behind a single endpoint, each loaded dynamically from Amazon S3 based on the inference request. This reduces hosting costs by sharing a single instance across many models, as only the models that are actively invoked consume memory. The correct answer is E because this feature is specifically designed for cost-efficient multi-model hosting.

Answer

SageMaker Elastic Inference

Answer

SageMaker inference pipeline

Answer

SageMaker batch transform

Answer

SageMaker multi-container endpoints

Question 5

A machine learning team is using SageMaker to build a model. They need to track hyperparameter tuning experiments, compare results, and visualize metrics. Which SageMaker feature should they use?

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

Accepted Answer

Use SSE-S3 encryption on the S3 bucket and enforce HTTPS. SSE-S3 provides server-side encryption at rest for objects in S3, and enforcing HTTPS ensures data is encrypted in transit using TLS. This combination meets the requirements for both encryption at rest and in transit without additional client-side complexity.

Answer

Use client-side encryption and transfer data via HTTP

Answer

Use SSE-KMS encryption on the S3 bucket and disable HTTP

Answer

Use SSE-C encryption on the S3 bucket and HTTPS

Answer

Use no encryption on S3 but use HTTPS

Question 6

A company is using SageMaker to train a model with a large dataset that is stored in S3. The training job is taking a long time due to high I/O latency. The team has already converted the data to RecordIO format. What should they do next to reduce I/O latency?

Accepted Answer

Use Pipe mode to stream the RecordIO data. Pipe mode streams data directly from S3 to the training algorithm in a sequential manner, eliminating the need to download files to the local disk. Since the data is already in RecordIO format, Pipe mode can efficiently read the serialized records, significantly reducing I/O latency compared to File mode.

Answer

Use SageMaker fast file mode

Answer

Use multiple training instances

Answer

Use Amazon FSx for Lustre as the training data source

Answer

Shuffle the data before training

Question 7

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.

Answer

No need to checkpoint the model

Answer

Faster training times

Answer

Guaranteed instance availability

Question 8

Which THREE of the following are valid ways to deploy a model using SageMaker? (Select THREE.)

Accepted Answer

Deploy to a SageMaker batch transform job. SageMaker batch transform jobs allow you to run inference on an entire dataset asynchronously, processing large batches of data without requiring a persistent endpoint. This is ideal for offline predictions where low latency is not needed, and the job automatically manages compute resources, scaling, and output storage.

Answer

Deploy to AWS Lambda

Answer

Deploy to Amazon EC2 directly

Question 9

Which TWO of the following are valid configurations for SageMaker Training Job resource limits? (Select TWO.)

Accepted Answer

Maximum number of instances. SageMaker Training Jobs allow you to set a resource limit on the maximum number of instances that can be used for distributed training, which helps control costs and prevent accidental over-provisioning. Option C is correct because you can specify a maximum run time in seconds for a training job; if the job exceeds this limit, SageMaker automatically stops it, ensuring you don't incur unexpected charges.

Answer

Maximum wait time in seconds

Answer

Minimum number of instances

Answer

Maximum number of spot instances

Question 10

An IAM policy attached to a SageMaker notebook role is shown in the exhibit. A data scientist is trying to run a training job from the notebook, but the job fails with an access denied error. The training job needs to read data from 'my-bucket' and write output to 'my-bucket'. What is the most likely cause of the failure?

Accepted Answer

The training job execution role does not have the same permissions. The IAM policy shown is attached to the SageMaker notebook role, which is used by the data scientist to interact with the notebook. However, when a training job is launched, it runs under a separate execution role (the SageMaker execution role for training jobs), not the notebook role. The training job fails because that execution role lacks the necessary S3 permissions (e.g., s3:GetObject, s3:PutObject) to read from and write to 'my-bucket'. The notebook role's permissions are irrelevant to the training job's runtime actions.

Answer

The policy does not allow s3:ListBucket

Answer

The policy does not allow sagemaker:CreateTrainingJob

Answer

The S3 bucket is not specified in the Resource

Answer

The policy does not allow s3:GetObject

Question 11

A SageMaker training job log shows the exhibit. The training job fails immediately after starting. The training data is supposed to be provided via Pipe mode from S3. What is the most likely cause?

Accepted Answer

The input data channel is not properly configured. The training job fails immediately after starting, which is characteristic of a Pipe mode configuration issue. In Pipe mode, SageMaker streams data from S3 directly to the algorithm via a Unix FIFO pipe, and if the input data channel is not properly configured (e.g., missing or incorrect S3 path, wrong channel name, or mismatched content type), the training job will fail at launch without any data being read. The log exhibit likely shows an error such as 'Unable to read from pipe' or 'NoSuchKey', confirming the channel misconfiguration.

Answer

The instance type does not have enough memory

Answer

The S3 bucket has insufficient permissions

Answer

The training script is using File mode instead of Pipe mode

Answer

The hyperparameters are incorrectly specified

Question 12

A SageMaker endpoint configuration is shown in the exhibit. The company wants to deploy the model to a real-time endpoint. What is missing from this configuration to successfully create the endpoint?

Accepted Answer

The endpoint name is not specified in the configuration. The endpoint configuration must include an EndpointName parameter to uniquely identify the endpoint within the AWS account and region. Without it, the CreateEndpoint API call fails because the service cannot route traffic or manage the deployment. The exhibit shows a valid EndpointConfigName, but the endpoint itself is not named, which is a required field for real-time inference endpoints.

Answer

The model name is missing

Answer

The initial instance count is missing

Answer

The accelerator type is missing

Answer

The data capture configuration is missing

Question 13

A data scientist is using Amazon SageMaker to train a model. Training is taking longer than expected. The scientist notices that the training job is using a single instance type with limited GPU memory. Which action will MOST likely reduce training time?

Accepted Answer

Configure the training job to use distributed data parallelism across multiple instances.. The training job is bottlenecked by limited GPU memory on a single instance. Distributed data parallelism splits the dataset across multiple instances, each processing a subset of the data in parallel, which directly reduces wall-clock training time by leveraging aggregate GPU memory and compute. This is the most effective action to address the stated problem of slow training due to limited GPU memory.

Answer

Use SageMaker Managed Spot Training to lower cost.

Answer

Use batch normalization layers.

Answer

Enable SageMaker Debugger for real-time monitoring.

Question 14

An ML team deploys a real-time inference endpoint on Amazon SageMaker. Users report high latency. The model is a PyTorch model using a custom container. Which combination of changes should the team implement to reduce latency? (Choose the best answer.)

Accepted Answer

Use SageMaker Inference Recommender to benchmark different instance families and select the best.. SageMaker Inference Recommender runs load tests across multiple instance families and configurations, providing a benchmark that identifies the optimal instance type and model server settings to minimize latency for a given model and payload. This data-driven approach directly addresses the high-latency issue without requiring code changes or switching to a different inference paradigm.

Answer

Switch to asynchronous inference endpoint.

Answer

Use SageMaker Elastic Inference to attach an accelerator.

Answer

Compile the model using SageMaker Neo.

Question 15

A company uses Amazon SageMaker to host a model for fraud detection. The model uses a custom XGBoost container. The endpoint receives about 100 requests per second, each with 50 features. The team notices that the model's predictions are occasionally incorrect for a subset of requests. Which approach should the team take to debug the issue?

Accepted Answer

Enable SageMaker Model Monitor to capture and analyze inference data.. SageMaker Model Monitor captures inference data (input features and predictions) and compares them against a baseline to detect data drift or quality issues. This allows the team to identify if incorrect predictions stem from distribution shifts or anomalous input patterns, which is the most direct debugging approach for sporadic prediction errors.

Answer

Use SageMaker Debugger to capture tensors during inference.

Answer

Scale the endpoint to more instances to reduce load.

Answer

Enable detailed CloudWatch Logs for the endpoint.