MLS-C01 Machine Learning Implementation and Operations Practice Test 22 — 15 Questions

Question 1

A company is deploying a model to an Amazon SageMaker endpoint for real-time inference. The model requires a GPU for low-latency predictions. Which instance type should be chosen?

Accepted Answer

ml.g4dn.xlarge. The ml.g4dn.xlarge instance is correct because it includes an NVIDIA T4 GPU, which is required for low-latency real-time inference with deep learning models. GPU instances accelerate matrix operations and parallel processing, reducing inference latency compared to CPU-only instances.

Answer

ml.c5.xlarge

Answer

ml.r5.2xlarge

Answer

ml.m5.large

Question 2

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

Answer

The Step Functions state machine has a misconfigured retry policy

Answer

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

Question 3

A company wants to track and compare metrics from multiple machine learning experiments. Which Amazon SageMaker feature should be used?

Accepted Answer

SageMaker Experiments. SageMaker Experiments is the correct choice for tracking and comparing metrics from multiple machine learning experiments. SageMaker Model Monitor is used to detect data drift, SageMaker Debugger is used to debug training jobs, and SageMaker Ground Truth is used for data labeling. Thus, only option A is correct.

Answer

SageMaker Ground Truth

Answer

SageMaker Model Monitor

Answer

SageMaker Debugger

Question 4

A data scientist uses SageMaker to train a model and wants to automatically stop the training job if the loss is not improving after a certain number of steps. Which feature should be used?

Accepted Answer

SageMaker Debugger. SageMaker Debugger provides built-in rules that monitor training metrics (e.g., loss) in real time and can trigger actions such as stopping the training job when the loss stops improving for a specified number of steps. This is done via the `StopTrainingJobOnRuleEvaluation` action, which automatically halts the job when a rule like `loss_not_decreasing` is violated.

Answer

SageMaker Experiments

Answer

SageMaker Automatic Model Tuning

Answer

SageMaker Ground Truth

Question 5

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

Answer

Endpoint auto scaling is adding new instances

Answer

Model is accumulating state from previous requests

Question 6

Which TWO AWS services can be used to deploy a machine learning model for serverless inference? (Choose 2.)

Accepted Answer

Amazon SageMaker Serverless Inference. Amazon SageMaker Serverless Inference automatically provisions, scales, and manages compute resources to run inference requests without requiring you to manage any underlying infrastructure. It scales down to zero when not in use and charges only for the compute time consumed, making it a fully serverless option for deploying ML models. AWS Lambda can also be used for serverless inference by packaging the model and inference code as a Lambda function. Lambda scales automatically, charges per invocation, and can be triggered by various AWS services, making it suitable for lightweight, event-driven inference workloads. Both services provide pay-per-request, auto-scaling-to-zero inference without requiring management of compute resources or container orchestration.

Answer

Amazon EMR

Answer

Amazon ECS with Fargate

Answer

AWS Batch

Question 7

Which THREE actions should be taken to ensure data security when training a model using Amazon SageMaker with data stored in Amazon S3? (Choose 3.)

Accepted Answer

Use a VPC to isolate the SageMaker training job. Using a VPC to isolate the SageMaker training job ensures that the training instance runs within a private network, preventing direct internet access and allowing traffic to flow only through controlled network interfaces. This reduces the attack surface and helps meet compliance requirements for data security.

Answer

Apply an S3 bucket policy that denies all access except from the SageMaker service

Answer

Attach an EBS volume for storing training data

Question 8

Which TWO approaches can reduce inference latency on a SageMaker real-time endpoint? (Choose 2.)

Accepted Answer

Attach an Elastic Inference accelerator. Elastic Inference (EI) accelerators attach a dedicated, low-cost FPGA-based inference accelerator to a SageMaker endpoint, offloading matrix operations from the CPU. This reduces inference latency by accelerating the compute-intensive forward pass of deep learning models without requiring a full GPU instance, making it ideal for real-time, low-latency predictions.

Answer

Increase the batch size

Answer

Enable SageMaker Model Monitor

Answer

Use a GPU instance type

Question 9

A financial services company uses Amazon SageMaker to train a fraud detection model. The training data is stored in an S3 bucket encrypted with AWS KMS. The SageMaker training job is configured to use a custom Docker container that reads data from S3 and writes model artifacts back to S3. The training job fails with the error: 'Unable to write model artifact to s3://my-bucket/output/model.tar.gz. Access Denied.' The IAM role used by the training job has the following permissions: s3:GetObject and s3:PutObject on the bucket, and kms:Decrypt on the KMS key. The training job is not using a VPC. What is the MOST likely cause of the failure?

Accepted Answer

The IAM role does not have kms:GenerateDataKey permission on the KMS key. The training job needs kms:GenerateDataKey permission to write objects encrypted with the KMS key. The provided IAM role has s3:GetObject, s3:PutObject, and kms:Decrypt, but lacks kms:GenerateDataKey, causing the Access Denied error when writing. Option A is incorrect because a region mismatch would not necessarily cause an Access Denied error if cross-region access is allowed, and the error indicates a permissions issue. Option C is incorrect because S3 Batch Operations is not required for writing artifacts. Option D is incorrect because the role already includes s3:PutObject.

Answer

The S3 bucket is in a different region than the training job

Answer

The S3 bucket requires S3 Batch Operations for writing artifacts

Answer

The IAM role does not have s3:PutObject permission on the output bucket

Question 10

A media company uses SageMaker to deploy a real-time inference endpoint for content recommendation. The model is a PyTorch model that uses GPU. The endpoint is deployed with an ml.p3.2xlarge instance. Over time, the endpoint's latency increases significantly during peak hours. The company has enabled auto scaling based on CPU utilization. However, the latency spikes occur even when CPU utilization is low. The model is stateless and the inference code is efficient. What is the MOST likely cause of the latency spikes?

Accepted Answer

Auto scaling is configured based on CPU utilization, but the bottleneck is GPU utilization. The model runs on GPU, so the bottleneck is GPU utilization, not CPU. Auto scaling based on CPU utilization does not help when the GPU is saturated. The latency spikes during peak hours suggest that the GPU is overloaded, but auto scaling is not triggered because CPU utilization remains low.

Answer

The model uses stateful processing that accumulates requests

Answer

The inference container has a memory leak that causes gradual slowdown

Answer

The instance type is too small for the model

Question 11

A startup is using SageMaker to train a model using the built-in XGBoost algorithm. The training job runs successfully but the resulting model performs poorly on the test data. The data scientist suspects overfitting. The training data is relatively small (10,000 rows). Which action should be taken to reduce overfitting?

Accepted Answer

Decrease the number of trees (num_round) to 50. Overfitting occurs when the model learns noise in the training data. Decreasing the number of trees (num_round) reduces model complexity, which helps prevent overfitting, especially with a small dataset (10,000 rows). Option B (increasing learning rate) can cause the model to converge too quickly to a suboptimal solution, potentially increasing overfitting. Option C (increasing trees) increases complexity and overfitting. Option D (larger instance) does not affect overfitting as it only changes compute resources.

Answer

Increase the learning rate to 0.3

Answer

Increase the number of trees (num_round) to 500

Answer

Use a larger instance type

Question 12

A data scientist is training a deep learning model on a large dataset using Amazon SageMaker. The training job is taking too long and the scientist wants to reduce the training time by distributing the workload across multiple GPUs. Which SageMaker feature should be used to achieve this?

Accepted Answer

Use SageMaker's distributed training libraries. SageMaker's distributed training libraries provide built-in optimized implementations of data parallelism and model parallelism, enabling efficient distribution of deep learning workloads across multiple GPUs with minimal code changes. This directly addresses the need to reduce training time by leveraging multiple GPUs in a SageMaker training job.

Answer

Use Amazon EMR to distribute the training

Answer

Use SageMaker Automatic Model Tuning

Answer

Use SageMaker Hyperparameter Tuning

Question 13

A team is deploying a machine learning model to production using Amazon SageMaker. They want to automatically scale the endpoint based on the incoming request volume, and they also need to ensure that the endpoint can handle sudden bursts of traffic without dropping requests. Which scaling policy should they use?

Accepted Answer

Target tracking scaling policy based on the number of invocations. A target tracking scaling policy based on the number of invocations allows the endpoint to automatically adjust the number of instances to maintain a target metric value (e.g., invocations per instance). This policy can proactively scale out to handle sudden bursts by adding instances before the request queue grows, preventing dropped requests. SageMaker's built-in scaling metric, 'SageMakerVariantInvocationsPerInstance', is ideal for this use case as it directly correlates with traffic volume.

Answer

Scheduled scaling policy for peak hours

Answer

Simple scaling policy based on average latency

Answer

Manual scaling by monitoring CloudWatch alarms

Question 14

A company has a real-time inference endpoint on Amazon SageMaker that uses a custom container. The endpoint is experiencing high latency and occasional 502 errors. The logs from the container show that the model inference time is low, but the overall response time is high. Which step is MOST likely to reduce the latency?

Accepted Answer

Increase the number of instances and enable auto-scaling. Increasing the number of instances and enabling auto-scaling helps distribute the incoming request load, reducing queuing delays at the endpoint. The logs show inference time is low, so the high latency is likely due to request queuing behind other requests. Scaling out addresses this. Option A is wrong because batch transform is designed for offline, batch processing, not real-time inference. Option B is wrong because using a larger instance type may provide more compute but does not directly address queuing; it is also less cost-effective than scaling out. Option C is wrong because the model inference time is already low, so further optimization would have minimal impact on overall latency.

Answer

Switch to batch transform to process requests in batches

Answer

Use a larger instance type for the endpoint

Answer

Optimize the model to reduce inference time

Question 15

A machine learning engineer is deploying a model that was trained on a large dataset stored in Amazon S3. The model needs to be retrained daily with new data. Which approach is the MOST cost-effective for storing the training data while allowing quick access for retraining?

Accepted Answer

Use S3 Intelligent-Tiering. S3 Intelligent-Tiering is the most cost-effective choice because it automatically moves data between access tiers (frequent, infrequent, and archive instant access) based on changing access patterns. Since the model is retrained daily, the training data will be accessed frequently during retraining but may have low access at other times, and Intelligent-Tiering optimizes storage costs without performance impact by charging a small monitoring fee per object.

Answer

Store all data in S3 Standard

Answer

Use S3 Glacier Deep Archive

Answer

Use S3 One Zone-IA