MLS-C01 Machine Learning Implementation and Operations Practice Test 9 — 15 Questions

Question 1

A team is using SageMaker to train a model. They want to track hyperparameters, metrics, and model artifacts. Which SageMaker feature should they use?

Accepted Answer

SageMaker Experiments. Option D is correct: SageMaker Experiments tracks parameters, metrics, and artifacts. Option A (Model Registry) manages model versions. Option B (Pipelines) orchestrates workflows. Option C (Debugger) monitors training.

Answer

SageMaker Pipelines

Answer

SageMaker Debugger

Answer

SageMaker Model Registry

Question 2

A company is deploying a SageMaker model for real-time inference. The endpoint must be highly available and cost-effective. Which TWO actions should the company take? (Select TWO.)

Accepted Answer

Deploy the endpoint with at least two instances in different Availability Zones. Options A and C are correct. A: Multiple instances across AZs ensures HA. C: Auto-scaling adjusts capacity based on demand, improving cost. B (single instance) lacks HA. D (spot instances) are cheaper but not for real-time HA. E (GPU) is not necessarily cost-effective.

Answer

Use managed spot training for inference

Answer

Use GPU instances for all models even if not required

Answer

Use a single large instance to handle peak load

Question 3

A data scientist is using SageMaker to train a model. The training job needs to access data in an S3 bucket in a different AWS account. The data scientist has set up proper S3 bucket policies and IAM roles. Which THREE steps are necessary to allow SageMaker to access the cross-account S3 bucket? (Select THREE.)

Accepted Answer

Configure the S3 bucket policy to grant access to the SageMaker execution role ARN from the training account. Options A, B, and C are correct. A: Role must have S3 access. B: Bucket policy must allow cross-account access. C: SageMaker execution role must be able to assume the cross-account role. D (VPC endpoint) is not required for cross-account access. E (KMS) is only if encryption is used.

Answer

Create a VPC endpoint for S3 in the training account

Answer

Use an AWS KMS key to encrypt the data in transit

Question 4

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 5

A data scientist attempts to create a SageMaker training job using the IAM policy shown in the exhibit. The training job fails with an access denied error. What is the most likely cause?

Accepted Answer

The IAM policy is missing the s3:ListBucket action. Option C is correct: The policy allows s3:GetObject but not s3:ListBucket, which is required to read objects. Option A (missing sagemaker:DescribeTrainingJob) is not needed for creation. Option B (resource arn for s3 is wrong) is correct but not the cause of access denied. Option D (missing s3:PutObject) is not required for reading training data.

Answer

The S3 bucket policy does not grant access to the SageMaker service principal

Answer

The IAM policy is missing the sagemaker:DescribeTrainingJob permission

Answer

The IAM policy is missing the s3:PutObject permission

Question 6

A SageMaker training job fails with the failure reason shown in the exhibit. What is the most likely cause?

Accepted Answer

There is an error in the custom training script. Option B is correct: ExitCode 1 from UserScript indicates an error in the training script. Option A (insufficient memory) would show OutOfMemory error. Option C (S3 access) would show AccessDenied. Option D (instance limit) would show ResourceLimitExceeded.

Answer

The training instance ran out of memory

Answer

The S3 bucket with training data is not accessible

Answer

The SageMaker service limit for the instance type has been exceeded

Question 7

A SageMaker endpoint has a CloudWatch alarm configured as shown in the exhibit. The alarm fires when the p99 latency exceeds 500 ms for two consecutive minutes. Which action should the data scientist take to reduce latency?

Accepted Answer

Increase the number of instances behind the endpoint. Option A is correct: Increasing instance count reduces load per instance, reducing latency. Option B (GPU) may not help if model is not compute-bound. Option C (batch size) depends on model. Option D (async inference) changes architecture.

Answer

Increase the batch size in the inference request

Answer

Use SageMaker asynchronous inference instead of real-time

Answer

Switch to GPU instances even if the model does not require GPU

Question 8

A data scientist is training a model on Amazon SageMaker and notices that the training job is taking much longer than expected. The instance type is ml.m5.xlarge and the dataset is 10 GB in CSV format. Which action is MOST likely to reduce training time without changing the instance type?

Accepted Answer

Convert the dataset to RecordIO or Parquet format before training.. Option B is correct because converting CSV to optimized formats like Parquet or RecordIO reduces I/O overhead and improves throughput. Option A is wrong because increasing batch size may help but does not address I/O. Option C is wrong because changing to a GPU instance is not allowed per stem. Option D is wrong because reducing epochs reduces accuracy, not training time effectively.

Answer

Change the instance type to ml.p3.2xlarge (GPU) for faster computation.

Answer

Reduce the number of training epochs to speed up convergence.

Answer

Increase the batch size to the maximum supported by the instance memory.

Question 9

A machine learning engineer is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires low latency (under 100 ms) and high throughput (1000 requests per second). Which SageMaker deployment option is MOST suitable?

Accepted Answer

Use SageMaker Inference Recommender to find the optimal instance type and endpoint configuration.. Option C is correct because SageMaker Inference Recommender provides automated testing and recommendations for instance type and configuration to meet latency and throughput requirements. Option A is wrong because Multi-Model Endpoints are designed for multiple small models, not optimized for a single large model's throughput. Option B is wrong because Serverless Inference has a maximum concurrency and may not achieve 1000 TPS with low latency. Option D is wrong because a single endpoint may not handle the load; Auto Scaling helps but does not guarantee optimal instance choice.

Answer

Deploy the model on a single endpoint with automatic scaling based on CPU utilization.

Answer

Use SageMaker Serverless Inference with provisioned concurrency.

Answer

Use a multi-model endpoint to load multiple copies of the model on the same instance.

Question 10

A company is using Amazon SageMaker Ground Truth to create labeled datasets for a text classification task. The labeling job uses a private workforce of 10 annotators. After labeling 10,000 items, the quality of labels is inconsistent. Which approach will MOST effectively improve labeling consistency?

Accepted Answer

Configure the labeling job to use annotation consolidation with majority voting and require multiple annotations per item.. Option D is correct because using a consensus pipeline with majority voting and annotation consolidation reduces individual annotator bias. Option A is wrong because increasing workforce size does not directly improve consistency. Option B is wrong because removing outliers after labeling may discard valid data. Option C is wrong because active learning selects examples for labeling, but does not improve annotator consistency.

Answer

Remove annotations from annotators with low agreement after the job completes.

Answer

Increase the number of annotators to 20 to average out inconsistencies.

Answer

Use active learning to automatically label the most confident samples and only send uncertain ones to annotators.

Question 11

A data scientist trains a model using Amazon SageMaker's built-in XGBoost algorithm. The model overfits on the training data. Which hyperparameter adjustment is MOST likely to reduce overfitting?

Accepted Answer

Increase the value of the lambda (L2 regularization) hyperparameter.. Option A is correct because increasing the L2 regularization lambda penalizes large weights and reduces overfitting. Option B is wrong because increasing max_depth increases model complexity, worsening overfitting. Option C is wrong because increasing num_round can lead to more overfitting. Option D is wrong because increasing subsample may reduce overfitting, but is less direct than regularization.

Answer

Increase the value of the max_depth hyperparameter.

Answer

Increase the value of the subsample hyperparameter to 1.0.

Answer

Increase the value of the num_round hyperparameter.

Question 12

A company is building a recommendation system using Amazon SageMaker. The training data includes user-item interactions stored in a DataFrame with over 100 million rows. The data scientist wants to perform feature engineering, including one-hot encoding of categorical features with high cardinality. Which approach is MOST cost-effective and scalable?

Accepted Answer

Use SageMaker Processing with a Spark container to distribute the encoding job.. Option B is correct because SageMaker Processing with a Spark job can scale horizontally and is cost-effective for large datasets. Option A is wrong because scikit-learn on a single instance may not handle 100M rows. Option C is wrong because Glue is serverless but may be more expensive for large processing. Option D is wrong because EMR is more complex and costly for a simple job.

Answer

Use Amazon EMR with Spark and store the processed data in HDFS.

Answer

Use a SageMaker notebook instance with scikit-learn to perform the encoding in memory.

Answer

Use AWS Glue ETL jobs to perform the encoding and store the result in S3.

Question 13

A machine learning team is using Amazon SageMaker Experiments to track multiple training runs. They need to compare the performance of different models based on metrics like accuracy and F1 score. However, when they view the experiment list in SageMaker Studio, the metrics are not displayed. What is the MOST likely cause?

Accepted Answer

The training script did not use the SageMaker SDK to log the metrics.. Option C is correct because metrics must be explicitly logged via the SageMaker SDK's log_metric() or in training job definition to appear in Experiments. Option A is wrong because permission to view is separate from metric capture. Option B is wrong because the metric definitions in the training job are optional but not required for logging. Option D is wrong because Experiments run on any instance type.

Answer

The training job did not define metric definitions in the algorithm specification.

Answer

The training job is running on an instance type that does not support Experiments.

Answer

The IAM role used by SageMaker does not have permission to write to the Experiments table.

Question 14

A company is deploying a PyTorch model on a SageMaker endpoint for real-time inference. The model is stored as a .pth file in an S3 bucket. The data scientist wants to use the SageMaker PyTorch inference toolkit. Which file is REQUIRED in the model artifacts to serve the model?

Accepted Answer

A file named model.pth containing the model state dictionary.. Option B is correct because the SageMaker PyTorch inference toolkit expects a file named model.pth in the model artifacts. Option A is wrong because inference.py is optional for custom code. Option C is wrong because requirements.txt is optional. Option D is wrong because the model can be loaded from S3.

Answer

A file named model.tar.gz that contains the model and any dependencies.

Answer

A file named inference.py that defines the model loading and prediction logic.

Answer

A file named requirements.txt listing the dependencies.

Question 15

A data scientist is using Amazon SageMaker Autopilot to automatically build a binary classification model. After the Autopilot job completes, the best model has an accuracy of 0.85 on the validation set. However, the data scientist notices a class imbalance (90% negative, 10% positive). Which metric should the data scientist use to evaluate the model's performance on the positive class?

Accepted Answer

Area Under the ROC Curve (AUC). Option D is correct because AUC measures the model's ability to distinguish between classes irrespective of threshold, and is suitable for imbalanced datasets. Option A is wrong because accuracy is misleading when classes are imbalanced. Option B is wrong because precision only considers positive predictions. Option C is wrong because recall only considers actual positives.

Answer

Recall

Answer

Accuracy

Answer

Precision