MLA-C01 Practice Test 5 — 10 Questions

Question 1

A data scientist is preparing a dataset for a binary classification model. The dataset has 10,000 samples, but the positive class represents only 2% of the data. The data scientist needs to train a model that will be evaluated on a hold-out test set that preserves the original class distribution. Which data preparation strategy is MOST appropriate?

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

Accepted Answer

Oversample the minority class in the training set using SMOTE, and keep the test set as is.. With a 2% positive class, the training set needs class balancing to help the model learn the minority signal, but the hold-out test set must reflect the true 2% prevalence so evaluation metrics (precision, recall, PR-AUC) are realistic. Applying SMOTE only to the training set and leaving the test set untouched satisfies both requirements.

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Undersample the majority class in the training set to match the minority class size.

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Randomly oversample the minority class in the entire dataset and then split.

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Apply SMOTE to the entire dataset before splitting into training and test sets.

Question 2

A data scientist wants to track the lineage of models, datasets, and training jobs in SageMaker. Which SageMaker feature should they use to capture these relationships as artifacts and actions?

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking creates a graph of artifacts (datasets, models) and actions (training jobs, endpoints) to track the provenance of ML workflows.

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SageMaker Model Registry

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SageMaker Experiments

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SageMaker Feature Store

Question 3

An ML engineer creates a SageMaker inference pipeline with two containers: a preprocessor and a predictor. The preprocessor is a lightweight Python script that transforms input data. How should the engineer structure the endpoints to ensure both containers run sequentially?

Accepted Answer

Create a PipelineModel in SageMaker with both containers listed in order: first preprocessor, then predictor.. SageMaker's PipelineModel allows you to define an ordered sequence of containers that are executed sequentially within a single HTTPS endpoint. When an inference request is made, the preprocessor container transforms the input, and the output is passed directly to the predictor container, all within the same endpoint invocation. This ensures low latency and tight coupling without needing external orchestration.

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Use batch transform with two transform jobs chained together.

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Use an AWS Lambda function as a proxy to invoke the preprocessor and then the predictor separately.

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Combine the preprocessor and predictor into a single Docker container.

Question 4

A team is fine-tuning a Hugging Face BERT model for text classification using SageMaker. They want to use the Hugging Face estimator for convenience. Which parameter must be set to use a custom training script?

Accepted Answer

entry_point. The entry_point parameter specifies the path to the custom training script that the Hugging Face estimator will execute inside the container. Without it, the estimator has no user-supplied Python file to run, so it cannot perform custom fine-tuning logic. framework_version, instance_type, and hyperparameters configure the environment but do not point to the training code itself.

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framework_version

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instance_type

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hyperparameters

Question 5

A machine learning engineer wants to monitor a deployed model for data drift. Which SageMaker feature should they use to automatically detect drift in the input data distribution compared to the training data baseline?

Accepted Answer

SageMaker Model Monitor. SageMaker Model Monitor can be configured to run monitoring jobs that compare live inference data against a baseline created from training data to detect data drift.

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SageMaker Pipelines

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SageMaker Debugger

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SageMaker Clarify

Question 6

A machine learning engineer needs to standardize features to have zero mean and unit variance before training a support vector machine. Which scaling method should they apply?

Accepted Answer

StandardScaler. StandardScaler transforms data to have zero mean and unit variance, which is required for SVM and many other algorithms.

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Normalizer

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RobustScaler

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MinMaxScaler

Question 7

A company wants to use SageMaker to fine-tune a foundation model for a text generation task using RLHF (Reinforcement Learning from Human Feedback). Which THREE components are required in the RLHF pipeline?

Accepted Answer

A pre-trained base model. Option B is correct because RLHF always starts from a pre-trained foundation model (the policy) that already has broad language capabilities; this base model is then fine-tuned with human preference signals rather than trained from scratch. Option D is correct because RLHF requires a reward model trained on human preference comparisons (e.g., chosen vs. rejected responses) to serve as a differentiable proxy for human judgment during optimization. Option E is correct because the policy is optimized against that reward model using a reinforcement learning algorithm such as PPO (Proximal Policy Optimization), typically with a KL penalty to the reference model to prevent reward hacking. Option A is not required: LoRA is a parameter-efficient fine-tuning technique that can optionally be used, but RLHF does not mandate adapters. Option C is not required: distinguishing generated from real text is a GAN discriminator concept, not part of the RLHF pipeline, which relies on preference-based reward modeling instead.

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A LoRA adapter for parameter-efficient fine-tuning

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A classifier to distinguish generated text from real text

Question 8

A data scientist needs to store training data in Amazon S3 and wants to optimize read performance for iterative training jobs. Which S3 feature should they use?

Accepted Answer

S3 Byte-Range Fetches. Byte-range fetches allow the data scientist to parallelize reads by requesting specific byte ranges of an object, which significantly improves read performance for iterative training jobs that need to access large datasets stored in S3. This feature enables multiple concurrent requests to different parts of the same object, reducing latency and increasing throughput compared to single-range reads.

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S3 Transfer Acceleration

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S3 Glacier

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S3 Select

Question 9

A data engineer needs to ingest streaming clickstream data from a website into an S3 data lake for ML training. The data arrives continuously and must be written to S3 in near real-time. Which AWS service is best suited for this task?

Accepted Answer

Amazon Kinesis Data Firehose with S3 as destination. Amazon Kinesis Data Firehose is the most appropriate service for loading streaming data into S3 with minimal effort and near-real-time latency. It can buffer, transform, and compress data before delivery.

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AWS Lambda function writing to S3 on every click event

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Amazon Athena queries running on the website's source database

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AWS Glue ETL job triggered by a cron job every 5 minutes

Question 10

A company wants to update an existing SageMaker real-time endpoint to serve a new model version. They need to route a small percentage of traffic to the new version initially and monitor for errors before switching fully. Which deployment pattern supports this?

Accepted Answer

Canary deployment with weighted production variants. SageMaker real-time endpoints support canary deployments by configuring multiple production variants with weighted traffic distribution. You can assign a small weight (e.g., 5%) to the new model version variant and 95% to the existing one, then monitor CloudWatch metrics for errors before shifting all traffic to the new variant. This matches the requirement for a gradual, monitored rollout.

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Shadow testing

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A/B testing with traffic splitting

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Blue/green deployment