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

Question 1

A machine learning team is using SageMaker to train a model. They want to ensure that the training data is encrypted at rest in the S3 bucket and that the data is also encrypted during transit. Which configuration should they use?

Accepted Answer

Use SSE-S3 encryption on the S3 bucket and enforce HTTPS. Option D is correct because SSE-S3 encrypts data at rest, and HTTPS ensures encryption in transit. Option A (SSE-KMS) also works but requires KMS keys. Option B (client-side encryption) is not managed by SageMaker. Option C (SSE-C) requires customer keys. Option E (HTTP) is not encrypted.

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 2

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 3

Which TWO of the following are benefits of using SageMaker Managed Spot Training? (Select TWO.)

Accepted Answer

Potential for significant cost savings. Options A and C are correct. Managed Spot Training uses spare EC2 capacity at a lower cost (A) and can significantly reduce training costs (C). Option B is wrong because spot instances can be interrupted. Option D is wrong because spot training may take longer due to interruptions. Option E is wrong because spot instances are not guaranteed to be available.

Answer

No need to checkpoint the model

Answer

Faster training times

Answer

Guaranteed instance availability

Question 4

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. Options A, C, and E are correct. SageMaker can deploy to a real-time endpoint (A), a batch transform job (C), or an asynchronous endpoint (E). Option B is wrong because SageMaker does not deploy directly to Lambda. Option D is wrong because SageMaker does not deploy to EC2 directly without containerization.

Answer

Deploy to AWS Lambda

Answer

Deploy to Amazon EC2 directly

Question 5

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

Accepted Answer

Maximum number of instances. Options A and D are correct. SageMaker training jobs can have a maximum run time (A) and a maximum number of instances (D). Option B is wrong because there is no minimum instance count limit. Option C is wrong because spot instance limit is set separately. Option E is wrong because there is no maximum wait time for training jobs.

Answer

Maximum wait time in seconds

Answer

Minimum number of instances

Answer

Maximum number of spot instances

Question 6

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 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 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

Question 9

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.. Distributed data parallelism (Option B) splits the data across multiple GPUs, reducing per-worker memory load and speeding up training. Option A (batch normalization) does not reduce training time. Option C (Spot Instances) introduces interruptions and may increase total time. Option D (SageMaker Debugger) is for monitoring, not performance.

Answer

Use SageMaker Managed Spot Training to lower cost.

Answer

Use batch normalization layers.

Answer

Enable SageMaker Debugger for real-time monitoring.

Question 10

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.. Enabling SageMaker Inference Recommender (Option D) generates multiple endpoint configurations to find the optimal instance type and model compilation settings. Option A (compilation only) helps but may not find the best instance. Option B (Elastic Inference) is deprecated. Option C (Asynchronous Inference) is for near-real-time, not true low-latency.

Answer

Switch to asynchronous inference endpoint.

Answer

Use SageMaker Elastic Inference to attach an accelerator.

Answer

Compile the model using SageMaker Neo.

Question 11

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.. Enabling SageMaker Model Monitor with data capture (Option D) allows the team to review actual input data and predictions to detect data drift or anomalies. Option A (CloudWatch Logs) only shows container logs, not per-request payloads. Option B (Debugger) is for training, not inference. Option C (increase instances) addresses capacity, not accuracy.

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.

Question 12

A machine learning engineer needs to deploy a TensorFlow model to a SageMaker endpoint. The model expects a specific input format. The engineer has the model artifacts stored in an S3 bucket. Which step is REQUIRED to deploy the model?

Accepted Answer

Create a SageMaker Model object using the TensorFlow serving image.. Creating a SageMaker Model object (Option C) is required to specify the image and artifact location, which is then used to deploy an endpoint. Option A (saving as SavedModel) is already done. Option B (Registering in Model Registry) is optional. Option D (creating a training job) is not needed for deployment.

Answer

Register the model in SageMaker Model Registry.

Answer

Create a SageMaker training job to re-train the model.

Answer

Save the model as a SavedModel format.

Question 13

A company is building a recommendation system using Amazon SageMaker. The data is stored in a large S3 bucket with millions of small CSV files. The team wants to train a factorization machines model. Which data ingestion strategy will be MOST efficient?

Accepted Answer

Use a SageMaker Processing job with a Spark container to read the files and write a single RecordIO file.. Using SageMaker Processing with Spark (Option C) can efficiently read many small files and convert them to a single RecordIO file, which is optimal for SageMaker training. Option A (direct training) would be slow due to many small files. Option B (Athena) is for SQL queries, not data conversion. Option D (Data Wrangler) is for smaller datasets and manual analysis.

Answer

Use Amazon Athena to query the data and output to a single CSV.

Answer

Point the training job directly to the S3 bucket containing the CSV files.

Answer

Use SageMaker Data Wrangler to create a data flow and export to a training dataset.

Question 14

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 15

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