MLA-C01 Practice Test 28 — 15 Questions

Question 1

A company's SageMaker endpoint is experiencing increased latency during peak hours. The endpoint uses a single ml.m5.large instance. The deployment is critical and must maintain low latency. Which action is MOST effective to reduce latency without sacrificing cost efficiency?

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

Accepted Answer

Add an auto-scaling policy based on request count. Adding an auto-scaling policy based on request count directly addresses the root cause of increased latency during peak hours: insufficient compute capacity. Auto-scaling dynamically adds more ml.m5.large instances when request count rises, distributing the load and reducing latency, while scaling down during off-peak hours to maintain cost efficiency. This is the most effective solution for a critical deployment that must maintain low latency without sacrificing cost.

Answer

Deploy multiple variants with A/B testing

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Use Elastic Inference to attach an accelerator

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Switch to a ml.c5.large instance

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Enable SageMaker Model Monitor

Question 2

A model deployed on SageMaker uses custom inference code. The endpoint is showing intermittent 500 errors. CloudWatch logs reveal 'TimeoutError: Request timed out after 60 seconds'. The model takes on average 55 seconds to process. What is the most effective solution?

Accepted Answer

Optimize the inference code to reduce latency.. The model's average processing time of 55 seconds is dangerously close to the default SageMaker model container timeout of 60 seconds. Any transient spike in latency or resource contention can push the inference time beyond the timeout, causing intermittent 500 errors. Optimizing the inference code to reduce latency below the timeout threshold directly addresses the root cause by creating a safety margin, making the endpoint more resilient to normal variability.

Answer

Increase the invocation timeout in the SageMaker API call.

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Increase the SageMaker endpoint's model container timeout setting.

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Increase the endpoint's instance count.

Question 3

A machine learning engineer has configured a SageMaker Model Monitor schedule for data quality monitoring as shown in the exhibit. The schedule is set to run hourly. However, the engineer notices that the monitoring jobs are not producing output in the specified S3 bucket. What is the most likely cause?

Accepted Answer

The DataAnalysisStartTime and DataAnalysisEndTime are set to a past date, so no data is analyzed.. The DataAnalysisStartTime and DataAnalysisEndTime parameters define the time window for which SageMaker Model Monitor analyzes data. When both are set to a past date that has already passed, the monitoring job finds no new data to analyze within that window, resulting in no output being written to the S3 bucket. The schedule runs hourly, but the analysis window is fixed to a historical period, so each execution produces no results.

Answer

The output_path is incorrectly placed; it should be under the MonitoringOutputConfig.

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The MonitoringType should be 'ModelQuality' to enable data quality monitoring.

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The cron expression is incorrectly formatted for an hourly schedule.

Question 4

An organization is deploying a large language model on SageMaker and needs to optimize inference costs while maintaining low latency. Which three strategies should they consider? (Select THREE.)

Accepted Answer

Use SageMaker Inference Recommender to find optimal instance and configuration.. A is correct because SageMaker Inference Recommender runs load tests against your model to recommend the most cost-effective instance type and configuration (e.g., instance count, container parameters) that meets your latency and throughput requirements. This eliminates guesswork and ensures you are not over-provisioning or under-provisioning resources, directly optimizing inference costs.

Answer

Enable SageMaker Model Parallelism for inference.

Answer

Use SageMaker Batch Transform for real-time requests.

Question 5

A financial services company is developing a real-time fraud detection model using XGBoost on SageMaker. They have millions of transactions daily and train a model weekly on 6 months of historical data. The training dataset is 500 GB in CSV format stored in S3. The training job uses an ml.p3.16xlarge instance with 8 GPUs, but training takes over 12 hours, which is too long for the weekly cadence. The data scientist notices that GPU utilization averages only 15% during training. The training script uses the SageMaker XGBoost container with default hyperparameters. Which combination of actions would MOST likely reduce training time? (Choose the best answer.)

Accepted Answer

Convert the training data to Parquet format, use Pipe input mode in the training job, and increase the instance count to run distributed training.. Converting CSV to Parquet reduces data size and improves I/O efficiency, Pipe input mode streams data directly to the algorithm without downloading, and increasing instance count enables distributed training across multiple GPUs. These changes directly address the low GPU utilization (15%) by reducing data loading bottlenecks and parallelizing computation, which is the core issue with the current single-instance, CSV-based training.

Answer

Increase the instance type to ml.p3dn.24xlarge and use EFA networking.

Answer

Tune hyperparameters using SageMaker Automatic Model Tuning to reduce training epochs.

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Use SageMaker Debugger to profile the training and adjust the batch size to maximize GPU memory usage.

Question 6

A data engineer is using AWS Glue to prepare a dataset for machine learning. The dataset has several columns with outliers. The engineer wants to detect and handle outliers in a scalable manner. Which TWO approaches should the engineer consider? (Select TWO.)

Accepted Answer

Use AWS Glue DynamicFrame with Apache Spark to compute interquartile range (IQR) and filter outliers.. AWS Glue DynamicFrames, built on Apache Spark, provide a scalable, distributed computing environment to compute statistical measures like the interquartile range (IQR) across large datasets. This allows the engineer to programmatically filter outliers without manual intervention, leveraging Spark's parallel processing for efficient handling of data at scale.

Answer

Manually remove outliers by inspecting the data in Amazon S3.

Answer

Train a neural network to identify anomalies and remove them.

Answer

Use pandas in a SageMaker notebook to calculate z-scores and filter outliers.

Question 7

A company is deploying a large number of small models (each < 100 MB) for different customers. They want to minimize costs and management overhead while serving traffic that varies significantly. Which SageMaker endpoint type should they choose?

Accepted Answer

A multi-model endpoint on a GPU instance. A multi-model endpoint (MME) on a GPU instance is the best choice because it allows you to host multiple small models (< 100 MB each) on a single endpoint, sharing the underlying GPU instance to reduce costs. SageMaker MME dynamically loads and unloads models based on traffic, which minimizes management overhead and handles variable traffic patterns efficiently without provisioning separate endpoints per model.

Answer

A batch transform job

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A multi-variant endpoint to route traffic to different model versions

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A serverless endpoint

Question 8

A company wants to share a trained model across multiple AWS accounts for inference. The model is stored in a central account's S3 bucket and needs to be deployed in other accounts' SageMaker endpoints. What is the recommended approach?

Accepted Answer

Attach a resource policy to the model in the central account allowing the other accounts' SageMaker service principals to access it. SageMaker allows you to attach a resource-based policy directly to the model resource in the central account, granting the SageMaker service principal from other accounts permission to call `sagemaker:CreateModel` and `sagemaker:CreateEndpointConfig` using the shared model. This approach avoids copying artifacts and leverages AWS Identity and Access Management (IAM) cross-account trust, where the central account's model policy explicitly allows the remote account's SageMaker service role to access the model and its underlying S3 objects.

Answer

Use AWS RAM to share the model artifact S3 bucket

Answer

Use SageMaker Model Registry with cross-account sharing enabled

Answer

Copy the model artifacts to each account's S3 bucket and create separate models

Question 9

A data scientist is using Amazon SageMaker to train a linear regression model. After training, the scientist notices that the training and validation errors are both low, but the model performs poorly on new test data. What is the MOST likely cause?

Accepted Answer

There is data leakage from the validation set into the training set. Data leakage from the validation set into the training set would allow the model to learn patterns that are not present in truly unseen data, leading to artificially low training and validation errors but poor generalization to new test data. In SageMaker, this can occur if the dataset is not properly split before feature engineering or if preprocessing (e.g., scaling or imputation) is applied to the entire dataset before splitting, causing the validation set to influence the training process.

Answer

The features are not scaled properly

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The model is overfitting the training data

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The model has high bias

Question 10

A machine learning team trains a model in SageMaker and wants to track every step — from dataset version to hyperparameters to final model artifact — for reproducibility and audit compliance. Which SageMaker feature should they use?

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking is the correct choice because it is specifically designed to create a directed acyclic graph (DAG) of every step in the ML workflow, including dataset versions, hyperparameters, training jobs, and model artifacts. This enables full reproducibility and audit compliance by capturing the provenance of each entity and their relationships, which is exactly what the question requires.

Answer

SageMaker Feature Store

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SageMaker Experiments

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SageMaker Model Registry

Question 11

A company uses AWS Glue ETL jobs to transform data for machine learning. They have a dataset with a column 'income' that is heavily right-skewed. Which transformation should be applied to make the distribution more Gaussian-like?

Accepted Answer

Log transformation (natural log). A log transformation is appropriate for heavily right-skewed data because it compresses the long tail by applying a concave function, pulling extreme values closer to the mean and making the distribution more symmetric. In AWS Glue ETL, you can apply this using Spark SQL's `LOG` function or a Python UDF with `numpy.log`, which directly addresses the skewness to better approximate a Gaussian distribution for downstream ML models.

Answer

Standardization (z-score)

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Min-max scaling to [0,1]

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Equal-width binning

Question 12

A data science team uses SageMaker Pipelines for automated training. They need to conditionally register a model only if evaluation metrics exceed a threshold. Which pipeline step type should they use after the evaluation step?

Accepted Answer

Condition step. The Condition step evaluates a condition and branches the pipeline; if the condition is met, the pipeline proceeds to register the model.

Answer

Processing step

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Transform step

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RegisterModel step

Question 13

A team wants to automatically retrain a model when new labeled data arrives. Which SageMaker feature can orchestrate this workflow?

Accepted Answer

SageMaker Pipelines. SageMaker Pipelines is a purpose-built CI/CD service for machine learning that allows you to define, orchestrate, and automate end-to-end ML workflows, including retraining models when new labeled data arrives. You can create a pipeline that triggers on new data events (e.g., via an S3 event notification or a Lambda function) and automatically executes steps such as data processing, training, evaluation, and model registration. This makes it the correct choice for orchestrating an automated retraining workflow.

Answer

SageMaker Model Monitor

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SageMaker Debugger

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SageMaker Autopilot

Question 14

During data quality assessment, a data scientist discovers that a numeric feature has many missing values. The feature is expected to have a skewed distribution. The scientist wants to impute missing values in a way that preserves the distribution shape and does not introduce bias toward the center. Which imputation strategy is MOST appropriate?

Accepted Answer

Median imputation. Median imputation is robust to skewness and does not pull imputed values toward the tail, preserving the overall distribution shape. Mean imputation can be affected by outliers and skewness. Mode imputation is for categorical data, and dropping rows may remove useful data.

Answer

Mode imputation

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Mean imputation

Answer

Drop rows with missing values

Question 15

A data scientist is performing feature selection for a linear regression model and wants to remove features that are highly correlated with each other to reduce multicollinearity. Which technique is BEST suited for this purpose?

Accepted Answer

Correlation analysis. Correlation analysis measures pairwise linear relationships between features. By identifying pairs with high correlation (e.g., >0.9), the scientist can remove one feature from each pair to reduce multicollinearity.

Answer

Lasso regularization

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Mutual information

Answer

Recursive Feature Elimination (RFE)