MLA-C01 Practice Test 45 — 15 Questions

Question 1

A company uses SageMaker Model Monitor's feature attribution drift monitoring with SHAP. They receive an alert that the average SHAP value for a particular feature has increased significantly compared to the baseline. The feature's input distribution has not changed. What does this likely indicate?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Concept drift in the model. SHAP values measure the contribution of each feature to the model's predictions. A change in SHAP values while input distribution is stable suggests the model has learned a new reliance on that feature, i.e., the relationship between the feature and the target has changed — concept drift.

Answer

The feature is no longer relevant to predictions

Answer

A bug in the SHAP computation

Answer

Data drift in that feature

Question 2

A team trains a model using SageMaker and wants to ensure that the training job cannot access the internet, but needs to access a private S3 bucket in the same VPC. Which configuration should they use?

Accepted Answer

Enable network isolation and provide VPC subnet and security group. SageMaker training jobs can be configured with VPC settings. Enabling network isolation prevents internet access, but the job still needs VPC connectivity to reach S3 via VPC endpoints. Setting the VPC config allows access to private resources.

Answer

Enable network isolation only, without VPC configuration

Answer

Disable network isolation but restrict security group rules

Answer

Use a public S3 bucket with bucket policies

Question 3

Refer to the exhibit. The training job failed. What is the MOST likely cause?

Accepted Answer

The training data size exceeds the available EBS volume size. The error message in the exhibit indicates an 'OSError: [Errno 28] No space left on device' during the training job. This occurs when the training data size exceeds the available EBS volume size attached to the SageMaker training instance. SageMaker uses EBS volumes for storing training data and intermediate outputs; if the dataset is larger than the provisioned EBS storage, the job fails with this specific disk-full error.

Answer

The learning rate is too high

Answer

The instance type does not have SSD storage

Answer

The instance type does not have enough memory

Answer

The number of epochs is too low

Question 4

A company has a trained machine learning model that needs to be deployed as a real-time inference endpoint on Amazon SageMaker. The endpoint must automatically scale based on incoming traffic. Which SageMaker feature should be used?

Accepted Answer

SageMaker Endpoint Auto Scaling. Amazon SageMaker Endpoint Auto Scaling is the correct feature because it automatically adjusts the number of instances serving a real-time inference endpoint based on the incoming traffic load. It uses Application Auto Scaling policies, which monitor CloudWatch metrics (e.g., InvocationsPerInstance) to scale in or out, ensuring low latency and cost efficiency without manual intervention.

Answer

SageMaker Elastic Inference

Answer

SageMaker Batch Transform

Answer

SageMaker Model Monitor

Question 5

A company has trained a custom model using PyTorch on Amazon SageMaker. The model achieves high accuracy, but the inference latency on a real-time endpoint is above the required 100ms SLA. The model is a large neural network with many layers. The company wants to reduce latency without significantly impacting accuracy. Which approach should the machine learning engineer take?

Accepted Answer

Use SageMaker Neo to compile the model for the target hardware.. SageMaker Neo compiles trained models into an optimized binary for the target hardware (e.g., CPU, GPU, or Inferentia). It applies graph-level optimizations, operator fusion, and quantization-aware tuning to reduce inference latency while preserving model accuracy. This directly addresses the need to lower latency below 100ms without retraining or sacrificing significant accuracy.

Answer

Reduce the batch size used during inference.

Answer

Increase the instance size of the endpoint.

Answer

Implement a cache for frequent inference requests.

Question 6

A company is deploying a deep learning model for real-time inference using Amazon SageMaker. The model is a CPU-intensive XGBoost model that performs well with CPU. However, the team wants to minimize latency further by using hardware acceleration. They are considering Amazon Elastic Inference (EI) or moving to a GPU instance. The model is not optimized for GPU, so significant code changes would be required. Which approach is the MOST cost-effective way to reduce latency without changing the model code?

Accepted Answer

Attach an Elastic Inference accelerator (e.g., ml.eia2.medium) to the existing CPU endpoint.. Amazon Elastic Inference (EI) allows you to attach a low-cost GPU-powered acceleration to an existing SageMaker CPU endpoint without any code changes. Since the XGBoost model is CPU-optimized and not GPU-native, EI provides hardware acceleration for the inference computation (specifically matrix operations) while keeping the model execution on the CPU, thus reducing latency without requiring model modifications.

Answer

Use a GPU instance (ml.p3.2xlarge) and optimize the model with SageMaker Neo compilation.

Answer

Use SageMaker Neo to compile the model for CPU with INT8 quantization.

Answer

Migrate the model to AWS Lambda with a custom runtime and use AVX instructions.

Question 7

A company uses Amazon SageMaker Data Wrangler to prepare data for ML. The dataset contains a timestamp column and sensor readings from IoT devices. The data scientist needs to create features such as moving averages and rolling statistics over time windows. Which Data Wrangler transformation type should be selected?

Accepted Answer

Window function. Window functions in Amazon SageMaker Data Wrangler allow you to compute moving averages, rolling statistics, and other time-window-based aggregations over ordered partitions of data. This is the correct transformation type because it directly supports operations like `SUM() OVER (ORDER BY timestamp ROWS BETWEEN 2 PRECEDING AND CURRENT ROW)` without requiring custom code or losing row-level granularity.

Answer

Join

Answer

Custom Python script

Answer

Group by and aggregate

Question 8

A data scientist is building a text classification model using a pre-trained BERT model from the Hugging Face library on SageMaker. The scientist wants to fine-tune the model on a custom dataset. Which TWO steps are necessary to set up the fine-tuning job? (Select TWO.)

Accepted Answer

Use the HuggingFace estimator provided by SageMaker. The SageMaker HuggingFace estimator is specifically designed to simplify fine-tuning of pre-trained Hugging Face models like BERT. It automatically handles the underlying infrastructure, including the correct PyTorch/TensorFlow and Transformers versions, without requiring custom Docker containers. This is the recommended approach for Hugging Face model fine-tuning on SageMaker.

Answer

Enable SageMaker Clarify for explainability during training

Answer

Build a custom Docker container with PyTorch and Transformers

Answer

Use SageMaker Processing to preprocess the data in parallel

Question 9

A company is using a SageMaker notebook instance to develop models. The security team requires that all data in the notebook be encrypted at rest and in transit, and that internet access be restricted. Which configuration meets these requirements?

Accepted Answer

Use a VPC-only notebook instance with a customer-managed KMS key and disable direct internet access.. A VPC-only SageMaker notebook instance ensures that all network traffic stays within the customer's VPC, and disabling direct internet access prevents any outbound internet connectivity. Using a customer-managed KMS key for EBS encryption meets the encryption-at-rest requirement, while SageMaker automatically encrypts data in transit using TLS 1.2 within the VPC, satisfying both security mandates.

Answer

Use a notebook with internet access enabled but attach a security group that blocks all outbound traffic.

Answer

Use a notebook with a public subnet and a network ACL that denies all inbound traffic.

Answer

Use a VPC-only notebook with default AWS managed key for EBS encryption.

Question 10

A company is deploying a ML model for real-time fraud detection using SageMaker. The model must process requests within 50 ms and scale to handle up to 10,000 requests per second during peak hours. The data includes PII, so all traffic must stay within a VPC. The team has configured the SageMaker endpoint with a VPC and an internet gateway for model downloads. During a load test, the endpoint fails to achieve the required throughput. Which change would most likely resolve the issue?

Accepted Answer

Use VPC endpoints (interface endpoint for SageMaker and gateway endpoint for S3) to keep traffic within AWS backbone.. The endpoint is currently using an internet gateway for model downloads, which forces traffic out to the public internet and back, adding latency and risking throughput failures. By using VPC interface endpoints for SageMaker and gateway endpoints for S3, all traffic stays within the AWS backbone network, reducing network overhead and meeting the 50 ms latency requirement. This also keeps PII traffic within the VPC, satisfying security constraints.

Answer

Remove the VPC configuration and use public endpoints to reduce network overhead.

Answer

Add a NAT gateway to allow the SageMaker endpoint to access the internet efficiently.

Answer

Increase the instance count and use a larger instance type to handle the throughput.

Question 11

A team is using AWS Step Functions to orchestrate a machine learning workflow that includes data preprocessing, training, and model evaluation. The team wants to run the workflow whenever new data arrives in an S3 bucket. Which approach should they use to trigger the Step Functions workflow?

Accepted Answer

Configure the S3 bucket to send events to Amazon EventBridge, and create an EventBridge rule that targets the Step Functions state machine.. Amazon S3 can send event notifications directly to Amazon EventBridge, and EventBridge rules can target AWS Step Functions state machines as a target. This provides a fully managed, serverless integration that allows the Step Functions workflow to be triggered automatically whenever new data arrives in the S3 bucket, without needing intermediate polling or custom code.

Answer

Configure the S3 bucket to send an event notification directly to the Step Functions state machine.

Answer

Use S3 event notifications to send a message to an Amazon SQS queue, and have a Lambda function poll the queue to start the execution.

Answer

Use a CloudWatch Logs metric filter to trigger the Step Functions execution.

Question 12

A company uses SageMaker to train a model. They want to ensure that training data is encrypted at rest and in transit, and that only authorized users can access the training artifacts. Which three steps should they take? (Choose three.)

Accepted Answer

Configure IAM policies to restrict access to SageMaker resources. IAM policies allow you to define fine-grained permissions to control which users or roles can create, describe, or delete SageMaker resources (e.g., training jobs, endpoints). By restricting access via IAM, you ensure that only authorized principals can interact with training artifacts, such as model output in S3 or logs in CloudWatch. This directly addresses the requirement of limiting access to authorized users.

Answer

Use SageMaker Model Monitor

Answer

Use SageMaker Network Isolation

Question 13

A data scientist is building a binary classification model on a highly imbalanced dataset where the positive class represents only 1% of the data. The scientist needs to train the model using Amazon SageMaker's built-in XGBoost algorithm. Which strategy should be used to address the class imbalance?

Accepted Answer

Set the `scale_pos_weight` hyperparameter to `sum(negative cases) / sum(positive cases)`. XGBoost's `scale_pos_weight` hyperparameter is specifically designed to handle class imbalance by adjusting the weight of the positive class during training. Setting it to `sum(negative cases) / sum(positive cases)` (i.e., 99/1 = 99) tells the algorithm to penalize misclassifications of the minority class more heavily, effectively balancing the gradient updates. This is the recommended approach for built-in XGBoost in SageMaker, as it directly modifies the loss function without altering the dataset.

Answer

Undersample the majority class until the dataset is balanced

Answer

Use the `max_delta_step` hyperparameter to increase the learning rate for the majority class

Answer

Use SMOTE to oversample the minority class before passing the data to XGBoost

Question 14

A team deploys a model on a SageMaker real-time endpoint using an ml.m5.xlarge instance. The model has high latency due to a large neural network. The team wants to reduce latency without changing the model code. Which option should they use?

Accepted Answer

Attach Amazon Elastic Inference to the endpoint. Amazon Elastic Inference attaches a fixed amount of GPU acceleration to an EC2 instance, providing cost-effective acceleration for deep learning inference without needing a full GPU instance.

Answer

Increase the instance size to ml.m5.4xlarge

Answer

Use SageMaker Neo to compile the model

Answer

Switch to a GPU instance like ml.g4dn.xlarge

Question 15

A data scientist trained a logistic regression model on a dataset with 100 features. After training, the training accuracy is 0.99 but validation accuracy is 0.75. Which action is MOST likely to reduce overfitting?

Accepted Answer

Increase the regularization strength. The model shows high training accuracy (0.99) but significantly lower validation accuracy (0.75), which is a classic sign of overfitting. Increasing the regularization strength (e.g., L1 or L2 penalty) in logistic regression directly penalizes large coefficients, reducing the model's complexity and improving generalization. This is the most direct way to address overfitting in a logistic regression model.

Answer

Increase the number of features

Answer

Use a more complex model like XGBoost

Answer

Use stratified cross-validation