MLS-C01 Machine Learning Implementation and Operations Practice Test 19 — 15 Questions

Question 1

A company wants to monitor SageMaker endpoints for data drift. Which TWO services can be used together to detect and alert on drift?

Accepted Answer

SageMaker Model Monitor. SageMaker Model Monitor (option B) continuously monitors models for data and quality drift. Amazon CloudWatch Alarms (option D) can be set up on Model Monitor's metrics to trigger alerts when drift is detected. SageMaker Data Wrangler (option A) is for data preparation, not monitoring. AWS CodePipeline (option C) is for CI/CD. Amazon CloudWatch Logs (option E) is for log storage and analysis, not for alerting on drift.

Answer

SageMaker Data Wrangler

Answer

AWS CodePipeline

Answer

Amazon CloudWatch Logs

Question 2

A data scientist needs to deploy a model with a custom inference container. Which THREE requirements must the container meet for SageMaker hosting?

Accepted Answer

Implement a /ping endpoint for health checks. SageMaker requires custom inference containers to implement the /ping endpoint for health checks (C), serve on port 8080 (D), and implement the /invocations endpoint for predictions (E). Option A is for training containers, not inference. Option B is unnecessary; the container can load the model using any method.

Answer

Provide a training script at /opt/ml/input/data

Answer

Use the SageMaker Python SDK to load the model

Question 3

A machine learning team is using SageMaker Pipelines to orchestrate a multi-step workflow. The pipeline fails with a 'ThrottlingException' when submitting a training job. Which TWO actions can reduce the likelihood of throttling?

Accepted Answer

Implement retry logic with exponential backoff in the pipeline. ThrottlingException occurs when the API request rate exceeds service limits. Implementing retry logic with exponential backoff (option B) helps handle transient throttling by automatically retrying requests with increasing delays. Reducing the number of concurrent pipeline steps (option D) decreases the rate of API calls, reducing the likelihood of hitting rate limits. Option A (Model Registry) is unrelated to throttling. Option C (increasing parallel training jobs) would increase concurrent API calls, worsening throttling. Option E (requesting a quota increase) raises the limit but does not reduce the immediate likelihood of throttling; it is a longer-term mitigation, not a direct action to reduce throttling in the current pipeline.

Answer

Use SageMaker Model Registry to version models

Answer

Increase the number of parallel training jobs

Answer

Request a service quota increase for training jobs

Question 4

A data scientist is training a deep learning model on Amazon SageMaker using the built-in Object Detection algorithm. The training job is failing with a 'ResourceLimitExceeded' error when trying to launch multiple GPU instances. Which of the following is the MOST likely cause?

Accepted Answer

The account has reached the limit for the number of GPU instances in the current AWS Region.. The 'ResourceLimitExceeded' error indicates that the account has exceeded its service limit for the number of GPU instances (or any SageMaker training instances) in the current AWS Region. Option A is incorrect because a syntax error would result in a different error (e.g., 'ModuleNotFoundError' or 'SyntaxError'). Option B is incorrect because the dataset size does not directly cause a resource limit error; it might cause out-of-memory issues but not a resource limit exceeded. Option D is incorrect because insufficient S3 bucket permissions would cause an 'AccessDenied' error, not 'ResourceLimitExceeded'.

Answer

The training script has a syntax error.

Answer

The dataset is too large for the selected instance type.

Answer

The S3 bucket containing the training data has insufficient permissions.

Question 5

A machine learning team is deploying a real-time inference endpoint on Amazon SageMaker for a model that requires low latency (<100 ms). The model is a PyTorch model with custom pre- and post-processing logic. The team uses a SageMaker Model with a custom inference container. After deployment, they observe that the endpoint takes over 500 ms for the first request, but subsequent requests are fast (~50 ms). What is the MOST likely cause?

Accepted Answer

The container has a cold start delay because the model needs to be loaded into memory from Amazon S3 on the first request.. The first request triggers a cold start where the custom inference container initializes and loads the model from Amazon S3 into memory, causing high latency. Subsequent requests are fast because the model remains cached. Option A is wrong because the instance type primarily affects throughput and steady-state latency, not transient cold starts. Option B is wrong because the issue is not about memory exhaustion—the endpoint handles subsequent requests well. Option D is wrong because auto-scaling adds instances but does not eliminate the cold start for the initial request on a new instance.

Answer

The instance type is too small to handle the model size.

Answer

The model is too large and exceeds the instance memory.

Answer

The endpoint is not configured with auto-scaling.

Question 6

A company is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket. The data scientist wants to use the Pipe mode for training to stream data directly from S3 instead of downloading it first. Which of the following is a prerequisite for using Pipe mode?

Accepted Answer

The training data must be in RecordIO-protobuf or TFRecord format.. Pipe mode in Amazon SageMaker streams data directly from S3 to the training algorithm without downloading it first. This mode requires the data to be in a format that supports random access and chunked reading, such as RecordIO-protobuf or TFRecord. Option D is correct because these formats enable efficient streaming. Option A is incorrect: SageMaker can uncompress data on the fly if needed, and compression is not a prerequisite. Option B is incorrect: the S3 bucket does not need public read access; SageMaker uses IAM roles to access the data. Option C is incorrect: Pipe mode works best with multiple sharded files, not a single large file, to allow parallel streaming.

Answer

The training data must be compressed using Gzip.

Answer

The S3 bucket must have public read access.

Answer

The training data must be stored as a single large file.

Question 7

A data scientist is performing hyperparameter tuning using Amazon SageMaker Automatic Model Tuning (AMT). The job uses a random search strategy. After 20 training jobs, the best objective metric value has plateaued. The data scientist wants to explore more of the hyperparameter space. Which action should the data scientist take?

Accepted Answer

Change the tuning strategy from Random to Bayesian.. Changing the tuning strategy from Random to Bayesian allows the tuning job to use previous results to guide the search toward more promising hyperparameter regions, which can explore the space more efficiently after plateauing. Option B is incorrect because enabling early stopping terminates underperforming trials early but does not alter the search strategy itself; it may even reduce exploration. Option C is incorrect because decreasing the maximum number of training jobs reduces the total exploration of the hyperparameter space. Option D is incorrect because increasing the number of parallel training jobs only speeds up the process but still uses the same random search strategy, which is unlikely to escape the plateau.

Answer

Enable early stopping.

Answer

Decrease the maximum number of training jobs.

Answer

Increase the number of parallel training jobs.

Question 8

A machine learning engineer is deploying a model to an Amazon SageMaker endpoint. The model requires GPU for inference. Which instance type should be selected?

Accepted Answer

ml.p3.2xlarge. Ml.p3.2xlarge is a GPU-enabled instance (part of the P3 family) suitable for inference requiring GPU acceleration. Options B, C, and D (ml.m5.large, ml.c5.xlarge, ml.r5.large) are CPU-only instances and do not provide GPU capabilities.

Answer

ml.m5.large

Answer

ml.c5.xlarge

Answer

ml.r5.large

Question 9

A data scientist is using Amazon SageMaker Ground Truth to create a labeled dataset for object detection. The team has limited budget and wants to minimize labeling costs while ensuring high-quality labels. Which approach is MOST cost-effective?

Accepted Answer

Use active learning to automatically label high-confidence data and send only uncertain data to a private workforce.. Active learning uses the model to automatically label high-confidence data points, while only sending low-confidence or uncertain data to a private workforce for human labeling. This significantly reduces the number of data points requiring manual labeling, thereby minimizing costs while still ensuring high-quality labels through expert review of challenging cases. Option A is incorrect because using only a private workforce for all data is expensive due to the high cost of domain experts. Option B is incorrect because using a public workforce with three workers per data point increases labeling costs without necessarily guaranteeing higher quality than a focused approach. Option D is incorrect because relying solely on automated labeling without human review can introduce errors and reduce label quality, especially for uncertain cases.

Answer

Use only a private workforce of domain experts to label all data.

Answer

Use a public workforce and have each data point labeled by three workers.

Answer

Use the built-in automated labeling feature without human review.

Question 10

A company is using Amazon SageMaker to train a model on data stored in S3. The training job needs to access data from an S3 bucket in a different AWS account. The data owner has granted cross-account access via a bucket policy. However, the training job fails with an AccessDenied error. What is the MOST likely cause?

Accepted Answer

The SageMaker execution role does not have the necessary permissions to access the S3 bucket.. Even with a bucket policy granting cross-account access, the SageMaker execution role must have an IAM policy that allows s3:GetObject (and any other required actions) on the S3 bucket. Without these permissions, the training job will fail with AccessDenied. Option A is incorrect because SSE-KMS encryption would require KMS permissions, but the issue is specifically about access permissions, not encryption. Option C is incorrect because the data does not need to be public; cross-account access via bucket policy is sufficient. Option D is incorrect because cross-account access does not require a VPC endpoint.

Answer

The data is encrypted with SSE-KMS and the SageMaker role lacks KMS permissions.

Answer

The S3 bucket is not configured with public access.

Answer

The S3 bucket is in a different region and requires a VPC endpoint.

Question 11

A data scientist is using Amazon SageMaker to train a model with a custom Docker container. The training script reads data from an S3 bucket and writes the model artifact to an S3 bucket. The training job fails with a 'NoSuchKey' error. What is the MOST likely cause?

Accepted Answer

The training data path specified in the input data channel is incorrect.. The 'NoSuchKey' error indicates that the specified key (file path) does not exist within the S3 bucket, which occurs when the training data path provided in the input data channel is incorrect. Option A is wrong because, while a script compatibility issue could cause errors, it would not manifest as a 'NoSuchKey' S3 error; it would likely result in a failure during container execution. Option C is wrong because if the Docker image were not available in Amazon ECR, SageMaker would fail with an 'ImageNotFoundException' or similar ECR-related error, not a 'NoSuchKey' S3 error. Option D is wrong because insufficient S3 permissions (e.g., missing s3:GetObject) would result in an 'AccessDenied' error, not 'NoSuchKey'.

Answer

The training script is not compatible with the Docker image.

Answer

The Docker image is not available in Amazon ECR.

Answer

The SageMaker execution role does not have s3:GetObject permission.

Question 12

A company is using Amazon SageMaker to train a machine learning model. The training job is configured to use the File mode to download data from S3 to the training instances. The training data is stored in a single S3 bucket with multiple prefixes. Which TWO actions are required to ensure the training job can access the data? (Choose TWO.)

Accepted Answer

Grant the SageMaker execution role s3:GetObject permission for the data bucket.. Options A and C are correct. Option A: The SageMaker execution role must have the s3:GetObject permission for the data bucket to read the training data. Option C: When using File mode, the training job must specify the S3 data channel with the correct prefix to indicate the location of the data. Option B is incorrect because Pipe mode is not required for File mode. Option D is incorrect because concatenating all data into a single file is unnecessary for File mode. Option E is incorrect because File mode does not require RecordIO-protobuf format.

Answer

Configure the training job to use Pipe mode.

Answer

Concatenate all data files into a single file.

Answer

Convert the data to RecordIO-protobuf format.

Question 13

A data scientist is using Amazon SageMaker to deploy a model for real-time inference. The endpoint receives a large number of requests with variable traffic patterns. The team wants to minimize cost while ensuring low latency. Which THREE actions should the team take? (Choose THREE.)

Accepted Answer

Use a multi-model endpoint to host multiple models on the same instance.. Options A, B, and E are correct. Option A: Using a multi-model endpoint allows multiple models to be hosted on the same instance, reducing costs by sharing resources. Option B: Enabling auto-scaling based on invocation count dynamically adjusts capacity to match variable traffic patterns, minimizing cost while maintaining low latency. Option E: Creating a production variant with a smaller instance type reduces per-instance cost. Option C is incorrect because setting the initial variant weight to 1 and increasing the number of instances does not directly minimize cost; it is a traffic distribution strategy. Option D is incorrect because a single large instance may be over-provisioned for variable traffic, leading to higher costs.

Answer

Set the initial variant weight to 1 and increase the number of instances.

Answer

Use a single large instance to handle all traffic.

Question 14

A machine learning team is using Amazon SageMaker to train a model. The training job uses spot instances to reduce cost. However, the training job is frequently interrupted. Which TWO actions can help mitigate the impact of spot interruptions? (Choose TWO.)

Accepted Answer

Use managed spot training with SageMaker's 'ManagedSpotTraining' parameter set to True.. Managed spot training (C) automatically manages the lifecycle of spot instances, including saving checkpoints and relaunching training when capacity becomes available. Checkpointing (D) saves model state periodically to Amazon S3, allowing training to resume from the last checkpoint after an interruption, minimizing progress loss. Option A (increasing instances) does not prevent interruptions and raises cost. Option B (larger instance type) does not guarantee lower interruption rates and is cost-inefficient. Option E (on-demand instances) avoids interruptions but defeats the purpose of cost reduction.

Answer

Increase the number of training instances.

Answer

Use a larger instance type that is less likely to be interrupted.

Answer

Switch to on-demand instances.

Question 15

A company is deploying a machine learning model using SageMaker. The model is a PyTorch model that requires GPU for inference. The company wants to minimize costs while ensuring low latency. Which instance type should be used for the SageMaker endpoint?

Accepted Answer

ml.g4dn.xlarge. Ml.g4dn.xlarge is a GPU instance with an NVIDIA T4, optimized for inference, and is more cost-effective than ml.p3.2xlarge while still providing low latency for PyTorch models. Option A (ml.m5.large) is wrong as it is a CPU instance without GPU support. Option B (ml.p3.2xlarge) is wrong because although it has a GPU, it is more expensive and not necessary for low-latency inference; ml.g4dn.xlarge offers similar performance at lower cost. Option C (ml.c5.2xlarge) is also a CPU instance and unsuitable.

Answer

ml.m5.large

Answer

ml.p3.2xlarge

Answer

ml.c5.2xlarge