MLS-C01 Practice Test 27 — 15 Questions

Question 1

A data scientist submits a SageMaker training job with the provided configuration. The job fails immediately with the error 'Algorithm not found: 382416733822.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.2-1'. What is the most likely cause?

Accepted Answer

The ECR repository URI is incorrect or the image does not exist.. Option A is correct because the image URI may be wrong. Option B is wrong because region is specified. Option C is wrong because role is specified. Option D is wrong because format is correct.

Answer

The training region is different from the image region.

Answer

The input data format is incorrect.

Answer

The IAM role does not have permission to pull the image.

Question 2

A data scientist is training a neural network on a GPU instance in Amazon SageMaker. The training job fails with an 'OutOfMemoryError'. Which action should the data scientist take to resolve this issue?

Accepted Answer

Reduce the batch size in the training script.. Option C is correct because reducing the batch size reduces memory usage per iteration. Option A is wrong because using a smaller instance type would have less memory. Option B is wrong because hyperparameter tuning does not directly reduce memory. Option D is wrong because distributed training typically increases memory usage per node.

Answer

Enable automatic hyperparameter tuning.

Answer

Switch to distributed training across multiple instances.

Answer

Use a smaller instance type with less GPU memory.

Question 3

A company uses Amazon SageMaker to train a model. The training job uses a custom Docker container. The job fails with the error 'CannotStartContainerError: API error (500).' Which of the following is the most likely cause?

Accepted Answer

The Docker image is built for a different CPU architecture.. Option D is correct because incompatible CPU instruction sets can cause container start failures. Option A is wrong because it would cause a different error. Option B is wrong because the error is during start. Option C is wrong because the error mentions container, not file system.

Answer

The training script has a syntax error.

Answer

The S3 input data is missing.

Answer

The output path is not writable.

Question 4

A company is deploying a machine learning model using Amazon SageMaker. The model needs to be updated frequently. Which THREE practices should the company implement for model versioning and deployment?

Accepted Answer

Use AWS CodePipeline to automate the training and deployment pipeline.. Option A is correct for versioning. Option C is correct for canary deployments. Option E is correct for automated pipelines. Option B is wrong because it's for data. Option D is wrong because instances should be monitored.

Answer

Manually update the endpoint configuration each time.

Answer

Store all training datasets in a single S3 bucket without versioning.

Question 5

A data scientist is using Amazon SageMaker to train a custom image classification model using a PyTorch script. The training job runs successfully but the model accuracy is lower than expected. The scientist wants to debug the training process by inspecting gradients and layer outputs. Which SageMaker feature should be used to capture this internal state during training?

Accepted Answer

Use SageMaker Debugger to capture tensors and gradients.. SageMaker Debugger captures internal model state such as gradients and tensors during training, enabling analysis and debugging. SageMaker Profiler (B) focuses on system performance, not model internals. SageMaker Experiments (C) tracks trials and metrics but does not capture internal state. SageMaker Model Monitor (D) detects data drift after deployment.

Answer

Use SageMaker Experiments to track hyperparameters and metrics.

Answer

Use SageMaker Profiler to profile system bottlenecks.

Answer

Use SageMaker Model Monitor to detect data drift.

Question 6

A data scientist is using Amazon SageMaker to train a large neural network on a GPU instance. The training is taking longer than expected. The scientist wants to reduce training time without changing the model architecture. Which TWO approaches should the scientist consider?

Accepted Answer

Use SageMaker's distributed training with multiple GPU instances.. Using multiple GPU instances with SageMaker distributed training (A) can accelerate training. Using SageMaker Managed Spot Training (B) reduces cost but not time. Using SageMaker Debugger (C) helps debugging but not speed. SageMaker Automatic Model Tuning (D) is for hyperparameter optimization. Using a larger GPU instance (E) with more memory and compute can directly reduce training time.

Answer

Use SageMaker Automatic Model Tuning to find optimal hyperparameters.

Answer

Use SageMaker Managed Spot Training to reduce cost.

Answer

Enable SageMaker Debugger to capture training metrics.

Question 7

A company uses Amazon SageMaker to train a model. The training job fails with an 'OutOfMemory' error. The training data is stored in S3 and the instance type is ml.m5.xlarge. What is the most efficient way to resolve this issue?

Accepted Answer

Use a larger instance type, such as ml.m5.2xlarge. Option C is correct: Increasing the instance type provides more memory. Option A (increasing instance count) does not increase memory per instance. Option B (reducing batch size) may help but is less efficient. Option D (enabling spot instances) does not address memory.

Answer

Enable managed spot training

Answer

Reduce the batch size in the training script

Answer

Increase the number of instances using distributed training

Question 8

A company is using SageMaker to train a model. The training data includes personally identifiable information (PII). The company must ensure that the data is encrypted at rest and in transit. Which combination of actions meets these requirements?

Accepted Answer

Use S3 server-side encryption and enable SageMaker inter-container traffic encryption. Option C is correct: S3 SSE-S3 or SSE-KMS encrypts data at rest, and SageMaker uses HTTPS for in-transit encryption. Option A (only SSL) lacks at-rest encryption. Option B (only S3 SSE) lacks in-transit. Option D (VPC only) does not encrypt.

Answer

Use S3 server-side encryption with S3 managed keys (SSE-S3)

Answer

Enable SSL for data in transit and use VPC endpoints

Answer

Place all resources in a private VPC subnets with no internet access

Question 9

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.. Option D is correct because the bucket policy may deny access. Option A is wrong because the role has s3:GetObject. Option B is wrong because it's allowed. Option C is wrong because it's allowed.

Answer

The IAM policy does not include s3:ListBucket permission.

Answer

The S3 bucket is in a different AWS account.

Answer

The sagemaker:CreateTrainingJob action is not allowed.

Question 10

An ML engineer needs to run a hyperparameter tuning job on Amazon SageMaker. The training algorithm supports distributed training across multiple GPUs. The engineer wants to minimize the total time to find the best hyperparameters. Which strategy should be used?

Accepted Answer

Use Bayesian optimization as the tuning strategy.. Bayesian search uses past results to select hyperparameters, converging faster than random or grid search. Random search (B) does not use past results. Grid search (C) is exhaustive and slow. Hyperband (D) is a bandit method but requires early stopping; Bayesian is better for minimizing total time with a fixed budget.

Answer

Use random search to explore a wide range.

Answer

Use grid search to cover all combinations.

Answer

Use Hyperband which is designed for distributed training.

Question 11

A team uses AWS Glue ETL jobs to preprocess data for SageMaker training. The job runs successfully but the output data is empty. What is the most likely cause?

Accepted Answer

The filter transformation condition is too restrictive, removing all rows. Option A is correct: If the filter condition excludes all records, output is empty. Option B (partition pruning) would not cause empty output if data exists. Option C (data type mismatch) causes errors, not empty output. Option D (insufficient memory) causes job failure, not empty output.

Answer

There is a data type mismatch between source and target

Answer

The source data is partitioned and only a subset of partitions is read

Answer

The Glue job runs out of memory and fails silently

Question 12

A company is using SageMaker to host a model that makes predictions on streaming data from Amazon Kinesis. The model must provide predictions with sub-second latency. Which approach should the company use?

Accepted Answer

Use a SageMaker real-time endpoint and invoke it from an AWS Lambda function that is triggered by Kinesis. Option C is correct: SageMaker real-time endpoint provides sub-second latency with Kinesis via Lambda. Option A (batch transform) is not real-time. Option B (async inference) has higher latency. Option D (Kinesis Analytics) does not use SageMaker.

Answer

Use SageMaker asynchronous inference with a Kinesis trigger

Answer

Use Amazon Kinesis Data Analytics with a built-in ML model

Answer

Use SageMaker batch transform to process batches of records from Kinesis

Question 13

A company uses SageMaker to train a model. The training job is taking too long and the data scientist wants to speed it up. Which THREE strategies should the data scientist consider? (Select THREE.)

Accepted Answer

Use a GPU instance type like ml.p3.2xlarge. Options A, C, and D are correct. A: Distributed training reduces time. C: Using a GPU instance accelerates compute. D: Using Pipe mode reduces I/O time. B (reducing epochs) may reduce accuracy. E (increasing batch size) can help but may cause memory issues.

Answer

Reduce the number of training epochs

Answer

Increase the batch size in the training script

Question 14

Which TWO options are valid ways to reduce inference latency for a model deployed on a SageMaker real-time endpoint? (Select TWO.)

Accepted Answer

Enable SageMaker Neo to compile the model for the target instance. Using a GPU instance can accelerate model computations. Enabling SageMaker Neo compilation optimizes the model for target hardware. Option C is wrong because increasing timeout does not reduce latency. Option D is wrong because batch transform is not real-time. Option E is wrong because increased instance count does not directly reduce latency per request.

Answer

Use SageMaker batch transform instead of real-time endpoint

Answer

Deploy the model to multiple instances behind a load balancer

Answer

Increase the endpoint's invocation timeout

Question 15

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.. Switching to an instance with GPU acceleration (e.g., ml.g4dn.xlarge) offloads computation to GPU, reducing CPU-bound latency. More instances (B) increase throughput but not per-request latency if the model is CPU-bound. Multi-model endpoints (C) help with many models but not single-model latency. Automatic scaling (D) helps with varying load but not per-request latency improvement.

Answer

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

Answer

Enable automatic scaling based on inference latency.

Answer

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

MLS-C01 Practice Test 27 — 15 Questions

Exhibit

MLS-C01 Practice Test 27 — 15 Questions

Exhibit