MLS-C01 Machine Learning Implementation and Operations Practice Test 2 — 15 Questions

Question 1

A data scientist is trying to create a training job named 'test-model' using an IAM role with the attached policy. The creation fails with an AccessDenied error. What is the most likely cause?

Accepted Answer

The Deny statement uses a wildcard '*' in the condition value, which is not supported for StringNotEquals.. The Deny statement uses 'StringNotEquals' with a wildcard '*' in the condition value, which is not supported for the 'StringNotEquals' condition operator in IAM policies. The 'StringNotEquals' operator requires exact string matching and does not support wildcards; using '*' will cause the condition to never match, effectively making the Deny statement non-functional or causing unexpected behavior. This mismatch leads to an AccessDenied error because the policy evaluation fails to properly deny or allow the action.

Answer

The Resource is set to '*' and should be specific.

Answer

The Deny statement uses 'StringNotEquals' which should be 'StringEquals'.

Answer

The IAM role does not have permission to assume the SageMaker execution role.

Question 2

A company runs a machine learning pipeline on Amazon SageMaker. The pipeline consists of three steps: data preprocessing (using a custom container), training (using a built-in algorithm), and model evaluation (using a custom container). The pipeline is orchestrated using AWS Step Functions. Recently, the pipeline has been failing intermittently at the model evaluation step with a 'TimeoutError'. The evaluation step runs a Python script that loads the trained model and a test dataset from S3, computes metrics, and writes results back to S3. The step is configured with a timeout of 600 seconds. The test dataset size has grown over time. The data science team suspects that the timeout is due to the increased data size. They want a solution that minimizes changes to the existing infrastructure and avoids increasing the timeout arbitrarily. Which approach should the team take?

Accepted Answer

Modify the evaluation script to process the test dataset in parallel batches, and use multiprocessing to distribute the workload within the same container.. Option C is correct because it addresses the root cause—the evaluation script's inability to process the growing dataset within the 600-second timeout—by parallelizing the workload within the same container. This approach minimizes infrastructure changes (no instance type or timeout increase) and leverages Python's multiprocessing to reduce wall-clock time, directly tackling the 'TimeoutError' without arbitrary timeout extensions.

Answer

Increase the timeout to 1200 seconds and use a larger instance type for the evaluation step.

Answer

Increase the timeout to 1800 seconds to accommodate the larger dataset.

Answer

Switch the evaluation step to use the 'ml.m5.4xlarge' instance type for more memory and compute.

Question 3

A media company uses Amazon SageMaker to train a deep learning model for video classification. The training job uses a single ml.p3.2xlarge instance and processes 50 GB of labeled video data stored in Amazon S3. The training completes successfully in 12 hours. However, the data scientists report that the model’s accuracy is lower than expected. They suspect the training data contains labeling errors. To improve model accuracy without incurring significant additional cost, they want to identify and remove mislabeled training examples before retraining. They have a small budget of $50 and need to complete the analysis within 2 hours. Which approach should the data scientists take?

Accepted Answer

Train a small, fast model on a random sample of the data (e.g., 1 GB) using a cheaper instance like ml.m5.xlarge, then use the model's prediction confidence to flag low-confidence examples as potential mislabels for manual review.. Option C is correct because training a small, fast model on a 1 GB random sample using a cheaper instance (ml.m5.xlarge) allows the team to quickly identify low-confidence predictions, which are strong indicators of mislabeled examples. This approach fits within the $50 budget and 2-hour time constraint, as it avoids processing the full 50 GB dataset and leverages a lightweight model for rapid iteration. By flagging only suspicious samples for manual review, the team can efficiently clean the training data without incurring the cost of re-labeling the entire dataset.

Answer

Use SageMaker Ground Truth to create a new labeling job for the entire dataset, then compare the new labels with the original labels to identify discrepancies.

Answer

Use SageMaker Clarify to generate a bias report for the training data and remove instances that contribute to bias.

Answer

Manually review all 50 GB of video data to correct labels.

Question 4

A company wants to deploy a machine learning model that performs real-time inference with sub-second latency. The model is a deep neural network with 500 MB of weights. The inference endpoint must scale to zero when not in use to minimize cost. Which AWS service should the company use?

Accepted Answer

Use Amazon SageMaker Serverless Inference to host the model.. Amazon SageMaker Serverless Inference is designed for workloads with intermittent traffic patterns, automatically scaling to zero when idle and scaling up for real-time requests. It supports models up to 1 GB in size and provides sub-second latency for inference, making it ideal for this 500 MB deep neural network. This service eliminates the need to manage underlying infrastructure while meeting the latency and cost requirements.

Answer

Deploy the model as an AWS Lambda function with provisioned concurrency.

Answer

Host the model on Amazon ECS with Fargate and use a target tracking scaling policy.

Answer

Create an Amazon SageMaker real-time endpoint with automatic scaling policies.

Question 5

A data science team is training a large deep learning model using Amazon SageMaker. The training job is taking a long time because the model has many layers and the dataset is large. The team wants to reduce training time by distributing the training across multiple GPUs on a single instance, as well as across multiple instances. Which TWO actions should the team take? (Choose two.)

Accepted Answer

Configure the training job to use SageMaker's model parallelism (SMP) library for pipeline or tensor parallelism.. Option B is correct because the SageMaker model parallelism (SMP) library is specifically designed to split large deep learning models across multiple GPUs using pipeline or tensor parallelism. This allows the team to train models that are too large to fit on a single GPU and to reduce training time by parallelizing computation across devices within and across instances.

Answer

Use SageMaker's distributed data parallelism (SMDDP) library to shard the model across GPUs.

Answer

Use SageMaker's managed training with a single instance containing multiple GPUs and enable data parallelism.

Answer

Set the instance type to a single GPU instance and rely on automatic model parallelism.

Question 6

An ML engineer is troubleshooting why an automated CI/CD pipeline cannot deploy an updated model to an existing SageMaker endpoint. The pipeline uses the IAM role that has the attached policy shown in the exhibit. What is the MOST likely cause of the failure?

Accepted Answer

The pipeline tries to update an existing endpoint, but the sagemaker:UpdateEndpoint action is not allowed.. The pipeline is attempting to deploy an updated model to an existing SageMaker endpoint, which requires the sagemaker:UpdateEndpoint action. The IAM policy shown in the exhibit (not provided here but implied) does not include this action, so the API call fails with an access denied error. Without explicit permission to update the endpoint, the CI/CD pipeline cannot modify the deployed configuration.

Answer

The pipeline tries to create a new endpoint, but the sagemaker:CreateEndpoint action is denied.

Answer

The pipeline tries to delete the old endpoint, but the sagemaker:DeleteEndpoint action is denied by a Deny statement.

Answer

The pipeline attempts to invoke the endpoint, but the sagemaker:InvokeEndpoint action is denied.

Question 7

Drag and drop the steps to train a model using Amazon SageMaker built-in algorithm in the correct order.

Question 8

Drag and drop the steps to evaluate a trained model using SageMaker Model Monitor in the correct order.

Question 9

Match each SageMaker built-in algorithm to its primary use case.

Question 10

Match each SageMaker built-in metric to its meaning.

Question 11

A data scientist is training a linear regression model on a dataset with 100 features. The model shows high variance on the test set. Which action is MOST likely to reduce overfitting?

Accepted Answer

Apply L2 regularization (Ridge regression). Option B is correct because adding L2 regularization (ridge regression) penalizes large coefficients and reduces model complexity, which helps with overfitting. Option A (adding more features) would increase variance. Option C (increasing training iterations) doesn't address overfitting. Option D (using a more complex model) would worsen overfitting.

Answer

Use a more complex model like XGBoost

Answer

Increase the number of training iterations

Answer

Add more feature engineering to increase model complexity

Question 12

A company is using Amazon SageMaker to deploy a real-time inference endpoint for a computer vision model. The endpoint receives bursts of traffic with up to 500 requests per second, but the load is unpredictable. Which scaling strategy is MOST cost-effective while maintaining low latency?

Accepted Answer

Configure automatic scaling with a target tracking policy and add a buffer to handle bursts. Option C is correct because SageMaker can add instances in response to increased load, and using a buffer helps absorb sudden spikes. Option A (provisioned concurrency) is for serverless but not SageMaker. Option B (manual scaling) is not cost-effective for unpredictable traffic. Option D (multi-model endpoints) is for serving multiple models, not for scaling.

Answer

Manually provision enough instances to handle peak load

Answer

Use provisioned concurrency on SageMaker Serverless Inference

Answer

Use a multi-model endpoint to reduce the number of instances

Question 13

A machine learning team is building a fraud detection system using Amazon SageMaker. The training data is highly imbalanced (99% legitimate, 1% fraudulent). They need to maximize the recall of the fraud class while keeping precision above 90%. Which approach should they take?

Accepted Answer

Train a model using the original data, then adjust the decision threshold on the validation set to maximize recall while precision > 90%. Option D is correct because adjusting the model threshold after training to favor recall while monitoring precision is the most direct way to meet the business requirement. Option A (SMOTE) can help but may not guarantee precision. Option B (weighted loss) is good but less direct than threshold tuning. Option C (random undersampling) may discard too much data.

Answer

Undersample the majority class to create a balanced dataset and train a Random Forest

Answer

Train an XGBoost model with scale_pos_weight parameter set to 99

Answer

Use SMOTE to oversample the fraud class and then train a logistic regression

Question 14

A data scientist wants to use Amazon SageMaker to train a deep learning model on a large dataset stored in S3. The training job is expected to take several hours. Which storage option should be used to minimize data loading time and cost?

Accepted Answer

Use Pipe mode to stream data directly from S3 during training. Option C is correct because Pipe mode streams data directly from S3 without downloading, minimizing time and cost. Option A (File mode) downloads entire dataset, increasing time and cost. Option B (Amazon EFS) is unnecessary and adds complexity. Option D (Amazon EBS) is not directly integrated with SageMaker.

Answer

Attach an Amazon EBS volume with the dataset pre-loaded

Answer

Use File mode to copy data to the training instance's local storage

Answer

Mount an Amazon EFS file system to the training instance

Question 15

An ML engineer is deploying a model to a SageMaker endpoint for real-time inference. The model requires a custom inference script that preprocesses input data and postprocesses predictions. Which SageMaker feature should be used to implement this custom logic?

Accepted Answer

Create a SageMaker model with a custom inference script that includes pre- and post-processing functions. Option B is correct because a custom inference script is packaged in the inference code and used by SageMaker to handle requests. Option A (built-in algorithm) does not allow custom logic. Option C (SageMaker Processing) is for batch jobs. Option D (SageMaker Ground Truth) is for labeling.

Answer

Use SageMaker Ground Truth to transform inference requests

Answer

Use SageMaker Processing jobs to preprocess data before inference

Answer

Use a built-in SageMaker algorithm with the default inference code

MLS-C01 Machine Learning Implementation and Operations Practice Test 2 — 15 Questions

Exhibit

MLS-C01 Machine Learning Implementation and Operations Practice Test 2 — 15 Questions

Exhibit