MLA-C01 Practice Test 9 — 10 Questions

Question 1

A company has 10 TB of log data in compressed JSON format stored in Amazon S3. The data needs to be processed and transformed into a structured format for machine learning. The processing requires complex transformations, including parsing nested JSON and joining with a reference table. The company wants to minimize infrastructure management. Which approach should the company use?

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

Accepted Answer

Use AWS Glue ETL with PySpark.. AWS Glue ETL with PySpark (Option D) is the best choice because it provides a fully serverless environment, minimizing infrastructure management. Glue can handle complex transformations like parsing nested JSON and joining with reference tables using PySpark, and it scales automatically for large datasets (10 TB). Amazon EMR (Option C) requires cluster management and provisioning, which contradicts the goal of minimizing management overhead.

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Use SageMaker Processing jobs to run custom scripts.

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Use Amazon Athena to query and transform the data.

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Use Amazon EMR with Apache Spark.

Question 2

A company is setting up a data pipeline to ingest streaming clickstream data from their website for real-time analytics and machine learning. The data must be reliably ingested, transformed, and stored in Amazon S3 for batch processing. Which combination of AWS services should be used?

Accepted Answer

Amazon Kinesis Data Firehose to Amazon S3. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed service designed to reliably ingest streaming data, transform it (e.g., convert to Parquet/ORC, compress, or invoke AWS Lambda for custom transformations), and automatically deliver it to Amazon S3 without requiring custom code or manual scaling. This directly meets the requirement for real-time ingestion, transformation, and storage in S3 for batch processing.

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Amazon Kinesis Data Analytics to Amazon S3

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AWS Glue ETL job to Amazon S3

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Amazon Kinesis Data Streams to Amazon SageMaker

Question 3

A machine learning engineer at a retail company is monitoring a production model that predicts inventory demand. The model's prediction accuracy has dropped significantly over the past week. The engineer checks the model's input data and notices a new product category was introduced with a different distribution. Which concept is most likely causing the performance degradation?

Accepted Answer

Covariate shift. B is correct because covariate shift occurs when the distribution of the input features changes while the relationship between features and the target remains the same. In this scenario, the introduction of a new product category with a different distribution alters the input data distribution, causing the model to encounter unseen patterns and degrade in prediction accuracy.

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Concept drift

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Data leakage

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Model decay

Question 4

A company wants to automate remediation when a SageMaker endpoint's latency exceeds a threshold for more than 5 minutes. The team needs to be notified and a Lambda function should be invoked to scale up the endpoint. Which combination of services should be used?

Accepted Answer

CloudWatch Alarm → SNS topic → Lambda function. CloudWatch Alarms evaluate SageMaker endpoint metrics (e.g., ModelLatency) against thresholds over evaluation periods; when the alarm fires after 5 minutes of breach, it publishes to an SNS topic, which can fan out to both email/SMS subscribers for notification and a Lambda function for automated scaling. This is the canonical AWS pattern for metric-driven remediation.

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EventBridge rule to trigger Lambda

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CloudWatch Logs subscription filter → Lambda function

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SageMaker Model Monitor → Lambda function

Question 5

A data scientist needs to ensure that the same train/test split is used across multiple experiments for reproducibility in SageMaker. Which approach should they take?

Accepted Answer

Set a random seed in the training script. Setting a random seed in the training script ensures that the pseudo-random number generator used for splitting the dataset produces the same sequence of random indices across runs. This guarantees an identical train/test split regardless of instance type, hyperparameters, or dataset version, which is essential for reproducibility in SageMaker experiments.

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Use the same SageMaker instance type

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Use the same hyperparameter values

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Use the same dataset version

Question 6

A company is training a large NLP model on SageMaker and wants to reduce costs by using Spot Instances. Which TWO configurations should they implement to handle Spot interruptions gracefully?

Accepted Answer

Set `use_spot_instances=True` and `max_wait` in the estimator. Option B is correct because in the SageMaker SDK estimator you must explicitly set use_spot_instances=True to enable managed Spot training, and max_wait defines the maximum wall-clock time SageMaker will wait for the Spot capacity (including interruptions and restarts), which is required to let training resume after an interruption. Option E is correct because enabling checkpointing (e.g., via checkpoint_s3_uri) periodically saves model state to Amazon S3, so when a Spot instance is reclaimed the job can restart from the last checkpoint instead of from scratch, which is the core mechanism for handling interruptions gracefully. Option A is not correct because using a single large instance does not meaningfully reduce interruption probability and actually increases the cost/impact of a single interruption. Option C is not correct because increasing max_run only sets the maximum training duration; it does not help the job survive or recover from a Spot interruption. Option D is not correct because keep_alive_period is used for managed warm pools to reduce cold-start latency between jobs, not to preserve training state across Spot interruptions.

Answer

Use a single large instance to reduce interruption probability

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Increase the `max_run` parameter to allow longer training

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Use `keep_alive_period` to keep the instance alive after training

Question 7

Refer to the exhibit. A data scientist used a SageMaker training job with a custom Scikit-learn script. The training job failed with the error shown. What is the most likely cause of this failure?

Accepted Answer

The training script is reading the CSV file incorrectly, causing a shape mismatch.. The error 'shape mismatch' typically occurs when the number of columns in the CSV file does not match the number of features expected by the Scikit-learn model. In SageMaker, the training script often loads data using pandas or numpy, and if the CSV has extra columns (e.g., an index column, header row misinterpreted, or trailing delimiter), the feature matrix shape will be inconsistent with the model's input dimensions, causing a ValueError during fit or transform.

Answer

The InputDataConfig specifies ContentType text/csv but the actual file is not CSV.

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The SageMaker training image is outdated and does not support Scikit-learn 1.0.

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The training data contains missing values that need to be imputed.

Question 8

A company runs a regression model to predict house prices. They have 50 features including 'zip_code' (high cardinality), 'square_footage', and 'year_built'. They want to select the most important features to reduce overfitting. Which feature selection method is computationally efficient for high-dimensional data and can handle multicollinearity?

Accepted Answer

Lasso regression (L1 regularization). Lasso regression (L1 regularization) is computationally efficient for high-dimensional data because it performs both feature selection and regularization simultaneously by shrinking less important feature coefficients to zero. It can handle multicollinearity by selecting only one feature from a correlated group, effectively reducing overfitting while maintaining model interpretability.

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Mutual information

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Recursive feature elimination (RFE)

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Principal component analysis (PCA)

Question 9

A company stores its model training data in Amazon S3. To meet compliance requirements, all data in transit between the S3 bucket and SageMaker must be encrypted. What should the company enforce?

Accepted Answer

Enforce HTTPS for all S3 access. Enforcing HTTPS for all S3 access ensures that data in transit between the S3 bucket and SageMaker is encrypted using TLS. This meets the compliance requirement for encrypting data in transit, as HTTPS uses TLS to protect data as it travels over the network.

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Enable S3 versioning

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Enable S3 access logging

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Use S3 server-side encryption (SSE-S3)

Question 10

A data scientist wants to compare the performance of two model versions (V1 and V2) in production by splitting traffic between them. They want to gradually increase the percentage of traffic to the new version while monitoring metrics. Which SageMaker feature enables this?

Accepted Answer

SageMaker production variants with traffic splitting. SageMaker production variants with traffic splitting let you host multiple model versions behind a single endpoint and assign a percentage of invocations to each variant. To compare V1 and V2 and gradually shift traffic, you create an endpoint configuration with two production variants and set the initial traffic distribution (e.g., 90/10), then update the weights as you gain confidence. This is the native SageMaker mechanism for A/B comparison and gradual rollout.

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SageMaker shadow testing

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SageMaker blue/green deployment

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SageMaker canary deployment