MLA-C01 Practice Test 50 — 15 Questions

Question 1

A company wants to use SageMaker to deploy a model that requires GPU acceleration for inference but wants to minimize costs by using a smaller attached GPU. Which options can they use? (Select TWO.)

Accepted Answer

Amazon Elastic Inference. Amazon Elastic Inference (Option A) allows you to attach a smaller, configurable GPU acceleration resource to a SageMaker endpoint, enabling GPU-accelerated inference without the cost of a full GPU instance. This directly meets the requirement of minimizing costs by using a smaller attached GPU.

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SageMaker Neo compilation

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Quantize the model to INT8 precision

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Use SageMaker serverless inference with GPU

Question 2

A data scientist is training a deep learning model on Amazon SageMaker and notices that the training loss decreases but the validation loss starts increasing after a certain number of epochs. The model is likely overfitting. Which SageMaker feature can they use to detect and diagnose this issue during training?

Accepted Answer

SageMaker Debugger. SageMaker Debugger is the correct choice because it provides real-time monitoring of training metrics, including loss values, and can automatically detect anomalies such as overfitting (where training loss decreases but validation loss increases). It allows you to set rules (e.g., `OverfitRule`) that trigger alerts or stop training when overfitting is detected, enabling proactive diagnosis during the training job.

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SageMaker Model Monitor

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SageMaker Automatic Model Tuning

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SageMaker Experiments

Question 3

A financial services company needs to enforce that only approved model versions are deployed to production. They use SageMaker Model Registry to track versions, with an approval workflow. Which action must they take in the model registry to ensure only approved models can be deployed?

Accepted Answer

Set the model version status to 'Approved' in the Model Registry. The SageMaker Model Registry uses a status field to control the lifecycle of model versions. By setting the model version status to 'Approved', the company can enforce that only approved models are deployable, as SageMaker's deployment APIs (e.g., CreateModel, CreateEndpointConfig) can be configured to require an 'Approved' status. This integrates with the approval workflow, ensuring that unapproved or pending versions are blocked from production deployment.

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Tag the model version as 'production-ready'

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Manually move the model artifact to a production S3 bucket

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Use AWS IAM policies to restrict deployment to specific model ARNs

Question 4

A company uses Amazon SageMaker to deploy a real-time inference endpoint. They notice increased latency in predictions during peak hours. Which should they investigate first to address the issue?

Accepted Answer

Review the endpoint auto-scaling policy. Increased latency during peak hours is a classic symptom of insufficient compute capacity to handle the request volume. The first step is to review the endpoint's auto-scaling policy to ensure it is configured to scale out instances proactively or reactively based on a relevant metric like 'SageMakerVariantInvocationsPerInstance'. If the policy has a high cooldown period or a low target metric value, it may not add instances quickly enough, causing requests to queue and latency to spike.

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Check the data labeling job status

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Modify the training instance type

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Increase the model artifact size

Question 5

A company wants to reduce costs for a SageMaker real-time endpoint that receives predictable traffic patterns: high during business hours and low at night. The model is a small PyTorch model. Which cost-saving strategy is most suitable?

Accepted Answer

Configure auto-scaling with a scheduled scaling policy to add instances during business hours and reduce at night. Auto-scaling with a schedule can adjust instance count based on time, matching capacity to demand. This is more efficient than manual scaling or using a larger instance.

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Use a single large instance to handle peak load

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Use a multi-model endpoint with multiple models

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Switch to batch transform jobs and run nightly

Question 6

An e-commerce company uses a multi-model endpoint on Amazon SageMaker to serve several deep learning models. After a new model version is deployed, the endpoint starts returning 503 errors for some models. Monitoring shows that the endpoint's memory utilization is near 100%. What should the team do to resolve this issue while minimizing operational overhead?

Accepted Answer

Increase the number of instances for the endpoint and configure an auto-scaling policy based on memory utilization.. Increasing the number of instances and configuring an auto-scaling policy based on memory utilization directly addresses the root cause (memory exhaustion) by distributing the load across more instances. SageMaker's auto-scaling can use custom CloudWatch metrics (like memory utilization) to dynamically adjust capacity, which minimizes operational overhead by automating scaling without manual intervention.

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Deploy each model on its own separate endpoint to isolate memory usage.

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Use Amazon SageMaker Model Monitor to detect memory leaks and send alerts.

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Use SageMaker's built-in model scaling feature to allocate more memory to the affected model.

Question 7

A company is using an Amazon SageMaker pipeline for automated retraining. The pipeline fails intermittently due to transient errors in the training job. Which steps should the team take to ensure the pipeline completes successfully? (Choose THREE.)

Accepted Answer

Enable managed spot training for cost savings and use checkpointing to resume from interruptions.. Enabling managed spot training with checkpointing allows the training job to resume from the last saved state if it is interrupted due to spot instance reclaimation. This directly addresses transient errors by providing fault tolerance, ensuring the pipeline can complete even if the underlying compute is preempted.

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Use a larger instance type for the training job to reduce the chance of failure.

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Implement automatic model checkpointing by setting the CheckpointConfig in the pipeline step.

Question 8

Which TWO tools are specifically designed for debugging and analyzing training jobs in SageMaker?

Accepted Answer

SageMaker Experiments. SageMaker Debugger is specifically designed to monitor and debug training jobs by capturing tensors, gradients, and other metrics in real time, while SageMaker Experiments tracks and analyzes training job parameters, metrics, and artifacts for comparison and reproducibility. Both tools directly address debugging and analysis of training jobs, unlike the other options which focus on automation, bias detection, or inference monitoring.

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SageMaker Autopilot

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SageMaker Clarify

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SageMaker Model Monitor

Question 9

A retail company is building a machine learning model to predict customer churn. The data engineering team has extracted customer transaction data from Amazon Aurora and stored it as CSV files in Amazon S3. The data includes customer IDs, transaction amounts, timestamps, and product categories. A data scientist discovers that the dataset contains several missing values in the 'transaction_amount' column for about 15% of the records. The data scientist also notices that the 'customer_id' column has some duplicate entries. The team wants to prepare the data for training a churn model using Amazon SageMaker. The data is approximately 50 GB in size. What should the data scientist do to handle the missing values and duplicates efficiently while preparing the data for training?

Accepted Answer

Use an AWS Glue ETL job to read the data from S3, apply transformations to fill missing values with the mean or median, and drop duplicate customer IDs, then write the cleaned data back to S3.. AWS Glue ETL jobs are serverless and designed to handle large-scale data transformations (like 50 GB) without requiring manual cluster management. Glue can read CSV files from S3, apply transformations to impute missing values with the mean or median, drop duplicate customer IDs, and write the cleaned data back to S3, all while scaling automatically to handle the data volume efficiently.

Answer

Use a SageMaker notebook instance with Pandas to load the entire dataset into memory, fill missing values with the median, and drop duplicate customer IDs.

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Drop all records with missing values in the transaction_amount column and remove duplicate customer IDs using an Athena SQL query, then store the result in S3.

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Use an Amazon EMR cluster with Spark to read the CSV files, impute missing transaction amounts with the mean or median, and remove duplicate customers.

Question 10

A data scientist is preparing a large dataset for training a binary classification model. The dataset has a severe class imbalance (95% negative, 5% positive). Which data preparation technique should the scientist use to address this imbalance without losing too much data?

Accepted Answer

SMOTE (Synthetic Minority Over-sampling Technique). SMOTE (Synthetic Minority Over-sampling Technique) is the best choice because it generates synthetic examples for the minority class by interpolating between existing minority instances and their k-nearest neighbors, rather than simply duplicating data. This addresses the severe 95:5 class imbalance without losing data (as undersampling would) and without the overfitting risk of naive random oversampling. The synthetic samples help the model learn a more general decision boundary for the positive class.

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Random undersampling of the majority class

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Random oversampling of the minority class

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Apply class weights during model training

Question 11

A data scientist is training a large language model using SageMaker and wants to reduce training costs. The training job is expected to run for several days. Which TWO actions should the data scientist take to minimize costs? (Choose TWO.)

Accepted Answer

Enable managed spot training. Using spot instances can save up to 90% compared to on-demand. Choosing a cheaper instance type like ml.g5 reduces cost. Managed spot training in SageMaker handles interruptions automatically. GPU instances are not always necessary; the cheapest instance that meets requirements should be selected. Checkpointing is needed for spot instance resilience.

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Use the most powerful GPU instance available to finish faster

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Increase the number of instances to reduce time

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Disable checkpointing to save storage costs

Question 12

A marketing company is preparing a dataset to train a logistic regression model to predict whether a customer will click on an online ad. The dataset includes 1 million records with features: customer_age (numeric), income (numeric), education_level (ordinal: high school, bachelor, master, PhD), and ad_category (categorical: 50 unique values). The data is stored in a CSV file in Amazon S3. The data scientist plans to use Amazon SageMaker's built-in linear learner algorithm. The data scientist needs to preprocess the data before training. What is the correct sequence of data preparation steps that should be applied to this dataset to ensure optimal model performance?

Accepted Answer

Standardize customer_age and income to have zero mean and unit variance, one-hot encode ad_category, ordinal encode education_level (e.g., map to 1-4), then combine all features into a feature matrix.. It applies appropriate preprocessing for a logistic regression model using SageMaker's linear learner. Standardizing numeric features (zero mean, unit variance) is essential for linear models to ensure convergence and equal feature influence. One-hot encoding the categorical ad_category (50 unique values) avoids imposing ordinal relationships, while ordinal encoding education_level respects its natural order. This combination prepares a feature matrix suitable for the linear learner's optimization.

Answer

Drop any duplicate records, apply min-max scaling to all numeric features, and use target encoding for ad_category based on click rates.

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Apply PCA to all numeric and categorical features after converting categories to numeric indices, then standardize the principal components.

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Apply min-max scaling to customer_age and income, label encode education_level and ad_category, then use recursive feature elimination to reduce dimensionality.

Question 13

A data scientist is using Amazon SageMaker Debugger to monitor training metrics. They want to stop training automatically if the model is overfitting. Which action should they take?

Accepted Answer

Configure a custom rule that triggers a STOP training action when validation loss stops decreasing. SageMaker Debugger allows you to define custom rules that can invoke a STOP training action when a specified condition is met, such as validation loss ceasing to decrease. This enables automatic termination of a training job to prevent overfitting, as the model is no longer improving on unseen data.

Answer

Define a Debugger rule that monitors the loss plateau

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Create a SageMaker Training Compiler

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Use a built-in rule that checks for vanishing gradients

Question 14

A company deployed a machine learning model on an Amazon SageMaker real-time endpoint. Over several weeks, they notice that inference latency has been gradually increasing, especially during peak business hours. The model and instance type have remained unchanged. What is the most likely cause of the increased latency?

Accepted Answer

The SageMaker endpoint auto scaling is not configured to scale out quickly enough under increasing traffic.. The gradual increase in latency during peak hours, with no change to the model or instance type, strongly indicates that the endpoint is not scaling out fast enough to handle increased traffic. SageMaker real-time endpoints rely on auto scaling policies to add instances based on metrics like invocation count or CPU utilization; if the scale-out step is too slow or the cooldown period is too long, requests queue up and latency rises. This matches the symptom of latency growing over weeks as traffic patterns evolve, rather than a sudden spike.

Answer

The inference script is not using batch processing.

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The model size is too large for the instance type.

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The endpoint has data capture enabled, causing additional overhead.

Question 15

A data science team deploys a regression model to Amazon SageMaker for real-time inference. After one month, the model's prediction errors increase significantly, but data distributions remain unchanged. Which monitoring approach is MOST suitable for detecting this issue?

Accepted Answer

Set up Amazon SageMaker Model Monitor to track model performance metrics against ground truth labels as they arrive.. Amazon SageMaker Model Monitor can be configured to track model performance metrics (e.g., regression error metrics like RMSE or MAE) against ground truth labels as they arrive. Since the question states that data distributions remain unchanged but prediction errors increase, the issue is likely model degradation (e.g., concept drift or model staleness) rather than data drift. Monitoring ground truth labels directly captures this performance degradation, making option A the most suitable approach.

Answer

Use Amazon SageMaker Clarify to monitor feature attribution drift.

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Enable Amazon CloudWatch to monitor model endpoint latency.

Answer

Configure Amazon SageMaker Model Monitor to track data drift on the input features.