MLS-C01 Practice Test 35 — 15 Questions

Question 1

A company is using Amazon SageMaker to train and deploy machine learning models. The data science team wants to track and compare model versions, hyperparameters, and metrics across multiple training jobs. Which TWO AWS services should they use together to achieve this? (Choose TWO.)

Accepted Answer

Amazon S3. Options A and C are correct. SageMaker Experiments provides tracking of training jobs, metrics, and parameters. Amazon S3 stores the experiment outputs and artifacts. Option B is wrong because AWS Glue is for ETL, not for experiment tracking. Option D is wrong because Amazon RDS is a relational database. Option E is wrong because CloudWatch Logs is for logs, not for experiment tracking.

Answer

Amazon RDS

Answer

Amazon CloudWatch Logs

Answer

AWS Glue

Question 2

A data scientist is using Amazon SageMaker to train a model and wants to track experiments, including parameters and metrics. Which THREE actions should be taken? (Choose three.)

Accepted Answer

Create an experiment in SageMaker Experiments.. Options A, B, and D are correct. SageMaker Experiments tracks parameters and metrics; adding the SDK to the training script logs them; creating an experiment integrates tracking. Option C is wrong because CloudWatch is for monitoring but not designed for experiment tracking. Option E is wrong because SageMaker Studio is the interface, not the tracking mechanism itself.

Answer

Use SageMaker Studio to manually record experiments.

Answer

Use Amazon CloudWatch Logs to store experiment data.

Question 3

A company is using SageMaker to train a large NLP model. The training job is taking too long due to high I/O wait time. The data is stored as CSV files in S3. Which optimization should the company implement to reduce I/O wait time?

Accepted Answer

Use SageMaker Pipe mode to stream data directly from S3. Option B is correct because Pipe mode streams data directly from S3, reducing disk I/O. Option A (File mode) writes to disk first, causing I/O wait. Option C (ShardedByS3Key) is for distributed training but does not reduce I/O. Option D (RecordIO) reduces file size but still uses File mode. Option E (SageMaker batch transform) is for inference, not training.

Answer

Convert CSV files to RecordIO format

Answer

Use SageMaker batch transform before training

Answer

Use SageMaker File mode with larger instance storage

Answer

Use SageMaker ShardedByS3Key data distribution

Question 4

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. Option C is correct because the policy allows s3:PutObject on 'my-bucket/*', but the training job may need to write to a different bucket or path. Option A is wrong because the actions are allowed. Option B is wrong because s3:PutObject is allowed. Option D is wrong because the bucket is specified. Option E is wrong because the training job role is separate from the notebook role.

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 5

An ML team is using SageMaker Autopilot to automatically build a binary classification model. The dataset has 500,000 rows and 200 columns, with a severe class imbalance (1% positive). Which configuration should the team set to address the imbalance?

Accepted Answer

Specify the 'objective' as 'F1' or 'AUC' to optimize for imbalanced data.. SageMaker Autopilot's AutoML job allows specifying objective metric such as F1 or AUC for imbalanced data (Option D). Option A (undersampling) is not built into Autopilot. Option B (class weights) is not directly configurable. Option C (SMOTE) is not supported by Autopilot.

Answer

Set the 'problem_type' to 'MulticlassClassification' to handle imbalance.

Answer

Use the 'AutoML' job with 'EnsembleMode' and 'SMOTE' sampling.

Answer

Configure the data split to use stratified sampling based on the target.

Question 6

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. Option C is correct because SageMaker Experiments provides experiment tracking, comparison, and visualization. Option A (Hyperparameter Tuning) only tunes, not tracks. Option B (Debugger) is for debugging. Option D (Model Monitor) is for monitoring after deployment. Option E (Ground Truth) is for labeling.

Answer

SageMaker Ground Truth

Answer

SageMaker Model Monitor

Answer

SageMaker Hyperparameter Tuning

Answer

SageMaker Debugger

Question 7

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. Option B is correct because the error indicates that the data channel path is incorrect or not configured. Option A is wrong because if the file mode was used, the path would exist. Option C is wrong because S3 permissions would cause a different error. Option D is wrong because a wrong instance type would not cause this error. Option E is wrong because hyperparameters would not cause a missing file error.

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 8

A company wants to use Amazon Rekognition to detect objects in images stored in an S3 bucket. The images are uploaded by users. Which IAM policy statement is necessary to allow Rekognition to read from the bucket?

Accepted Answer

s3:GetObject. Rekognition needs s3:GetObject permission (Option B) to read images. Option A (s3:PutObject) is for writing. Option C (s3:ListBucket) is for listing. Option D (s3:DeleteObject) is for deletion.

Answer

s3:PutObject

Answer

s3:DeleteObject

Answer

s3:ListBucket

Question 9

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. Option D is correct because enabling auto-scaling adds instances during spikes, reducing latency. Option A (larger instance) may help but is not cost-effective. Option B (batch transform) is for async inference. Option C (Elastic Inference) accelerates inference but does not handle spikes. Option E (SageMaker Neo) optimizes for edge devices.

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 10

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. Option C is correct because the error indicates GPU memory exhaustion, often due to batch size being too large. Option A (insufficient CPU) would not cause CUDA errors. Option B (S3 bandwidth) is unrelated. Option D (CPU memory) is not the issue. Option E (driver version) would give a different error.

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 11

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. Option B is correct because a target tracking scaling policy with a specified target value for the metric allows the endpoint to automatically adjust capacity to maintain the target metric, and it can handle bursts by adding more instances proactively. Option A is wrong because a simple scaling policy based on average latency may not handle bursts quickly. Option C is wrong because a scheduled scaling policy is for predictable traffic patterns. Option D is wrong because manual scaling is not automatic.

Answer

Scheduled scaling policy for peak hours

Answer

Simple scaling policy based on average latency

Answer

Manual scaling by monitoring CloudWatch alarms

Question 12

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. Option A is correct because using Pipe mode with RecordIO streams data directly, reducing I/O. Option B (shuffle) does not reduce I/O. Option C (FSx for Lustre) provides high-performance file system but adds complexity. Option D (fast file mode) is still File mode. Option E (multiple instances) may increase throughput but not per-instance I/O.

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 13

Which TWO SageMaker features can be used to monitor and debug training jobs? (Choose 2.)

Accepted Answer

SageMaker Debugger. SageMaker Debugger (Option A) captures tensors and metrics during training. CloudWatch Logs (Option E) provide training script logs. Options B (Model Monitor), C (Clarify), and D (Ground Truth) are for inference, explainability, and labeling respectively.

Answer

SageMaker Model Monitor

Answer

SageMaker Ground Truth

Answer

SageMaker Clarify

Question 14

A machine learning engineer is deploying a model on SageMaker and needs to ensure that the endpoint can handle a sudden spike in traffic. The engineer expects traffic to increase by 10x during a promotional event. Which scaling strategy should be used?

Accepted Answer

Use scheduled scaling to add instances before the event, combined with dynamic scaling for the remaining duration.. Option A is correct because scheduled scaling can add additional instances before the expected spike. Option B is wrong because dynamic scaling may not react quickly enough to a sudden 10x spike. Option C is wrong because manual scaling requires human intervention. Option D is wrong because a single large instance provides no redundancy and may not handle the spike.

Answer

Use a single large instance type instead of multiple smaller instances.

Answer

Manually increase the instance count before the event.

Answer

Use only dynamic scaling based on the average latency metric.

Question 15

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. Option C is correct because the endpoint configuration requires at least one production variant, which is present, but the endpoint name is specified when creating the endpoint, not in the config. Option A is wrong because the model name is specified. Option B is wrong because instance count is set. Option D is wrong because accelerator type is optional. Option E is wrong because data capture config is optional.

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