MLA-C01 Practice Test 4 — 25 Questions

Question 1

A company is using AWS Step Functions to orchestrate their ML retraining pipeline. They want to trigger retraining when new data arrives, but only if the model's performance has degraded below a threshold. Which THREE AWS services should they use together to achieve this? (Choose three.)

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

Accepted Answer

AWS Step Functions. Amazon EventBridge (C) is the correct event-routing service to detect the arrival of new data (e.g., an S3 PutObject event) and trigger the pipeline, since it can match events and invoke targets such as Step Functions. AWS Step Functions (A) is correct because it orchestrates the multi-step ML retraining workflow, including the conditional logic that checks whether model performance has degraded below the threshold before proceeding. AWS Lambda (B) is correct because it provides the serverless compute to evaluate the performance metric against the threshold and return a decision that Step Functions uses in its Choice state to branch into retraining or stop. Amazon CloudWatch Logs (D) is not correct because it is a logging/monitoring service, not an event trigger or orchestration component for this pipeline. SageMaker Model Registry (E) is not correct because, while it tracks model versions and approval status, it does not itself trigger retraining based on incoming data or performance degradation.

Answer

Amazon CloudWatch Logs

Answer

SageMaker Model Registry

Question 2

A data science team wants to track the lineage of models, including datasets, training jobs, and endpoints, for reproducibility and audit. They need a solution that captures relationships between artifacts automatically during training and deployment. Which service should they use?

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking automatically captures relationships among datasets, training jobs, model artifacts, and endpoints as the pipeline runs, producing a queryable lineage graph for reproducibility and audit. It records entities and associations without custom instrumentation, which is exactly what the team needs. SageMaker Experiments tracks runs and metrics but does not build the full artifact relationship graph.

Answer

Amazon S3 object versioning

Answer

SageMaker Experiments

Answer

SageMaker Model Registry

Question 3

A team has deployed a real-time inference endpoint and wants to automatically scale based on CPU utilization. Which scaling policy type should they use with Application Auto Scaling for SageMaker endpoints?

Accepted Answer

Target tracking scaling. Target tracking scaling is correct because it adjusts capacity to keep a specified metric, such as CPU utilization, at a target value. Application Auto Scaling for SageMaker endpoints supports target tracking, which automatically creates and manages the necessary CloudWatch alarms and scaling policies. This is the recommended approach for maintaining a utilization target without manually defining step adjustments.

Answer

Step scaling

Answer

Predictive scaling

Answer

Simple scaling

Question 4

An ML engineer wants to be notified when the average inference latency of a SageMaker endpoint exceeds 500 ms for 2 consecutive evaluation periods. Which AWS service combination should they use?

Accepted Answer

CloudWatch Alarm + SNS. CloudWatch Alarms evaluate metrics over specified periods and can trigger SNS notifications when a threshold is breached for a defined number of consecutive periods. SageMaker endpoints automatically publish inference latency metrics to CloudWatch, so an alarm on the latency metric with a 500 ms threshold and 2 evaluation periods, wired to an SNS topic, delivers exactly the required notification.

Answer

SageMaker Model Monitor + SNS

Answer

EventBridge + Lambda

Answer

SageMaker Clarify + SNS

Question 5

A company uses Amazon SageMaker Data Wrangler for data preparation. The data science team wants to automatically detect potential bias in their dataset before training a model. Which feature of Data Wrangler should they use?

Accepted Answer

Built-in bias detection with SageMaker Clarify. Amazon SageMaker Data Wrangler includes built-in bias detection powered by SageMaker Clarify. This feature allows data scientists to analyze datasets for potential bias before model training, directly within the Data Wrangler visual interface. Option D is correct because it is the only option that provides automated bias detection at the data preparation stage.

Answer

Feature importance analysis

Answer

Amazon Rekognition

Answer

Model Monitor

Question 6

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?

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.

Answer

Use SageMaker Processing jobs to run custom scripts.

Answer

Use Amazon Athena to query and transform the data.

Answer

Use Amazon EMR with Apache Spark.

Question 7

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.

Answer

Amazon Kinesis Data Analytics to Amazon S3

Answer

AWS Glue ETL job to Amazon S3

Answer

Amazon Kinesis Data Streams to Amazon SageMaker

Question 8

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.

Answer

Concept drift

Answer

Data leakage

Answer

Model decay

Question 9

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.

Answer

EventBridge rule to trigger Lambda

Answer

CloudWatch Logs subscription filter → Lambda function

Answer

SageMaker Model Monitor → Lambda function

Question 10

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.

Answer

Use the same SageMaker instance type

Answer

Use the same hyperparameter values

Answer

Use the same dataset version

Question 11

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

Answer

Increase the `max_run` parameter to allow longer training

Answer

Use `keep_alive_period` to keep the instance alive after training

Question 12

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.

Answer

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

Answer

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

Question 13

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.

Answer

Mutual information

Answer

Recursive feature elimination (RFE)

Answer

Principal component analysis (PCA)

Question 14

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.

Answer

Enable S3 versioning

Answer

Enable S3 access logging

Answer

Use S3 server-side encryption (SSE-S3)

Question 15

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.

Answer

SageMaker shadow testing

Answer

SageMaker blue/green deployment

Answer

SageMaker canary deployment

Question 16

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?

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

Answer

Only tokenization without any removal

Answer

Tokenization and one-hot encoding of words

Question 17

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.

Answer

SageMaker Model Monitor → Lambda

Answer

CloudWatch Events → SageMaker Training Job

Answer

SageMaker Clarify → SNS → Step Functions

Question 18

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.

Answer

Real-time endpoint

Answer

Asynchronous inference

Answer

Serverless inference

Question 19

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.

Answer

Use of mechanical turk for all labeling

Answer

Pre-built annotation workflows for bounding boxes

Answer

Automated data labeling with AWS Lambda

Question 20

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

Answer

SageMaker Model Monitor

Answer

SageMaker Debugger