MLA-C01 Practice Test 10 — 10 Questions

Question 1

A data scientist is performing text preprocessing for a sentiment analysis model. The dataset contains many stop words and rare words. Which combination of preprocessing steps will reduce dimensionality and improve model performance?

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

Accepted Answer

Tokenization, stop-word removal, and TF-IDF. Tokenization splits raw text into individual terms, stop-word removal eliminates high-frequency but low-information words (e.g., 'the', 'is', 'and'), and TF-IDF assigns weights that downscale terms appearing in many documents while upscaling rare, discriminative terms. Together they reduce the feature space and emphasize sentiment-bearing vocabulary, which directly improves model performance on text classification tasks. This is the standard NLP preprocessing pipeline for sentiment analysis.

Answer

Remove all words shorter than 3 characters and apply label encoding

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Only tokenization without any removal

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Tokenization and one-hot encoding of words

Question 2

A company wants to automatically trigger a retraining pipeline when concept drift is detected in their deployed model. Which combination of services should they use?

Accepted Answer

SageMaker Model Monitor → CloudWatch Alarm → SNS → Lambda. SageMaker Model Monitor detects concept drift by analyzing model predictions against a baseline, then publishes metrics to CloudWatch. A CloudWatch Alarm triggers when drift exceeds a threshold, sending a notification via SNS to invoke a Lambda function, which starts the retraining pipeline. This end-to-end integration ensures automated, event-driven retraining without manual intervention.

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SageMaker Model Monitor → Lambda

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CloudWatch Events → SageMaker Training Job

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SageMaker Clarify → SNS → Step Functions

Question 3

A company wants to run inference on a large dataset stored in S3 using a pre-trained model. The inference can tolerate latency from minutes to hours, and they want a fully managed solution that autoscales to handle large volumes. Which SageMaker inference option is most suitable?

Accepted Answer

Batch transform. Batch transform is the most suitable option because the company needs to run inference on a large dataset stored in S3 with latency tolerance from minutes to hours, and requires a fully managed, autoscaling solution. SageMaker Batch Transform processes the entire dataset as a single job, automatically provisions and scales compute resources, and writes results back to S3 without the need for persistent endpoints.

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Real-time endpoint

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Asynchronous inference

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Serverless inference

Question 4

A company uses Amazon SageMaker Ground Truth to label images for object detection. They want to minimize labeling costs while maintaining high accuracy. Which feature should they enable?

Accepted Answer

Active learning to automatically select samples for labeling. SageMaker Ground Truth's active learning automatically selects the most informative unlabeled samples for human labeling, reducing the total number of labels needed while maintaining model accuracy. This directly lowers labeling costs because fewer human annotations are required for the same or better model performance.

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Use of mechanical turk for all labeling

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Pre-built annotation workflows for bounding boxes

Answer

Automated data labeling with AWS Lambda

Question 5

A team is training a PyTorch model using SageMaker with a custom training script. They want to track hyperparameters and metrics across multiple experiments. Which service should they use?

Accepted Answer

SageMaker Experiments. SageMaker Experiments is purpose-built for tracking ML experiment runs, automatically capturing hyperparameters, metrics, artifacts, and lineage across training jobs. It integrates natively with SageMaker training jobs and custom PyTorch scripts via the SageMaker SDK, letting teams compare runs in the Studio UI. This directly matches the requirement to track hyperparameters and metrics across multiple experiments.

Answer

SageMaker Clarify

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SageMaker Model Monitor

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SageMaker Debugger

Question 6

A team is training a PyTorch model using SageMaker. They have a custom training script that requires specific Python packages not included in the SageMaker default PyTorch container. Which approach should they use?

Accepted Answer

Use the built-in PyTorch estimator and specify a requirements.txt in the source directory. The SageMaker PyTorch estimator supports a requirements.txt file placed in the source_dir; the training toolkit automatically installs those packages into the container at the start of the job. This is the intended, low-effort way to add Python dependencies without rebuilding the image. It satisfies the requirement while keeping the managed PyTorch framework benefits (optimized libraries, GPU support, distributed training).

Answer

Build a custom Docker container from scratch and push it to Amazon ECR

Answer

Use the SageMaker XGBoost estimator and modify the script to use PyTorch

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Use SageMaker Autopilot to automatically handle dependencies

Question 7

A healthcare company uses Amazon SageMaker to deploy a real-time inference endpoint for a diagnostic model. The endpoint is configured with a single ml.p3.2xlarge instance. The model processes patient data and returns a risk score. Recently, the endpoint has been experiencing intermittent 504 errors along with increased latency. The team uses Amazon CloudWatch to monitor the endpoint's InvocationsPerInstance and ModelLatency metrics. They observe that InvocationsPerInstance is well below the throttling threshold, but ModelLatency shows periodic spikes lasting 5-10 seconds. The endpoint's CPU utilization remains below 60%, but memory utilization occasionally spikes to 90% during those spikes. The team has checked the inference code and found no obvious memory leaks or performance bottlenecks in the custom logic. The model itself is a deep neural network hosted using Apache MXNet. The team suspects that the issue might be related to resource contention or an external dependency. What should the team do FIRST to diagnose and resolve the issue?

Accepted Answer

Increase the instance type to a more memory-intensive instance like ml.p3.8xlarge to handle memory spikes.. The endpoint memory spikes to 90% and correlates with 504 errors and latency spikes, so the team should first resolve the resource pressure by moving to a larger memory instance such as ml.p3.8xlarge. The current correct option D is technically inaccurate: Amazon SageMaker Debugger rules and profiling are for training jobs, not real-time inference endpoints. Option A does not address memory pressure. Option C is for data drift and model quality, not performance diagnostics.

Answer

Implement request batching to increase throughput and reduce the number of inference requests.

Answer

Set up SageMaker Model Monitor to track data drift and model quality metrics.

Answer

Enable SageMaker Debugger rules and profiling to monitor memory and CPU utilization at a fine-grained level during inference.

Question 8

A financial services company trains multiple models on SageMaker and needs to track hyperparameters, metrics, and artifacts for each experiment. Which SageMaker feature should they use to organize and compare experiments?

Accepted Answer

SageMaker Experiments. SageMaker Experiments is purpose-built for tracking, organizing, and comparing machine learning experiments — it captures hyperparameters, metrics, input datasets, and output artifacts for each trial and groups them into experiments and runs. This lets data scientists visualize and compare results across many training jobs in a single view.

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SageMaker Model Registry

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SageMaker Pipelines

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SageMaker Debugger

Question 9

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset for classification. They want to detect potential bias in the data before training. Which SageMaker service should they use in conjunction with Data Wrangler to detect bias?

Accepted Answer

Amazon SageMaker Clarify. Amazon SageMaker Clarify is designed to detect bias in datasets and models. It integrates with Data Wrangler to provide bias analysis during the data preparation phase.

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Amazon SageMaker Debugger

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Amazon SageMaker Pipelines

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Amazon SageMaker Model Monitor

Question 10

A company uses SageMaker Autopilot to build a binary classification model. The generated leaderboard shows an ensemble model as the best candidate. The team needs a model that can be deployed for real-time inference with latency < 10ms. What should they do?

Accepted Answer

Select the best single model from the leaderboard (non-ensemble candidate) and deploy it. Ensemble models in SageMaker Autopilot combine multiple base models (e.g., stacking or voting), which increases inference latency because every base model must run and their outputs aggregated. For a strict <10ms real-time latency requirement, the best approach is to select the best-performing single (non-ensemble) model from the leaderboard, which has lower inference overhead. Autopilot's leaderboard explicitly lists both ensemble and individual model candidates, so the team can pick a single model that meets the latency SLA.

Answer

Use SageMaker Inference Recommender to profile the ensemble model and optimize it

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Deploy the ensemble model as a SageMaker endpoint; ensemble models are optimized for low latency

Answer

Retrain the ensemble model with fewer base estimators using a custom container