MLA-C01 Practice Test 24 — 15 Questions

Question 1

A data scientist is using SageMaker Experiments to track multiple training runs. They want to compare the F1 scores across runs. Which component should they use to log the F1 score?

Accepted Answer

Metric. In SageMaker Experiments, metrics are logged using the SageMaker SDK's log_metric method or by reporting through the training job's metric definitions. Hyperparameters are logged separately. Artifacts are for model files or datasets.

Answer

Parameter

Answer

Hyperparameter

Answer

Artifact

Question 2

A machine learning engineer is using Amazon SageMaker Data Wrangler to prepare a dataset with a categorical feature that has over 5,000 distinct values (high cardinality). The engineer needs to transform this feature into a form suitable for a gradient boosting model while preserving as much information as possible. Which transform should be applied?

Accepted Answer

Target encoding with smoothing. For high-cardinality categorical features in tree-based models like gradient boosting, target encoding (mean target per category) is effective because it captures the relationship with the target without exploding feature dimensions. One-hot encoding would create too many columns, and label encoding introduces ordinality issues.

Answer

Label encoding

Answer

One-hot encoding

Answer

Drop the feature

Question 3

A team is evaluating classification models for a medical diagnosis application. The cost of a false negative is much higher than the cost of a false positive. Which metric should be optimized during model selection?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positives correctly identified, which directly minimizes false negatives. In medical diagnosis, missing a disease (false negative) is far more costly than a false alarm, so optimizing recall ensures the model captures as many true positive cases as possible.

Answer

Accuracy

Answer

F1 score

Answer

Precision

Question 4

A machine learning engineer is using Amazon SageMaker Experiments to track multiple training runs. They want to compare the performance of different hyperparameter configurations visually. Which SageMaker tool provides an interactive interface to compare experiments?

Accepted Answer

SageMaker Studio. SageMaker Studio provides an interactive, web-based interface that allows you to visually compare experiment runs, including hyperparameter configurations and performance metrics, through built-in experiment management and visualization tools. This is the correct answer because the question specifically asks for an interactive interface, which Studio offers natively, unlike the other options which are programmatic or monitoring-focused.

Answer

SageMaker Model Monitor

Answer

SageMaker Experiments SDK

Answer

SageMaker Debugger Insights

Question 5

An ML engineer is designing a SageMaker Pipeline for a computer vision model. The pipeline includes steps for data processing, training, evaluation, and registration. The engineer wants to enable caching to avoid reprocessing when step inputs have not changed. For which steps is caching supported? (Select TWO.)

Accepted Answer

Processing step. Caching is supported for the following step types: Processing, Training, Tuning, Transform, and AutoML. Condition steps and Lambda steps do not support caching because they are control flow steps.

Answer

Condition step

Answer

Lambda step

Answer

RegisterModel step

Question 6

A company has a model that receives low traffic but needs to handle sudden spikes. Which deployment option is most cost-effective?

Accepted Answer

SageMaker Serverless Inference. SageMaker Serverless Inference is the most cost-effective option for low-traffic models with sudden spikes because it automatically scales to zero when not in use and scales up instantly to handle bursts, charging only for the compute time consumed per inference request. This eliminates the cost of idle provisioned infrastructure, making it ideal for unpredictable or intermittent traffic patterns.

Answer

SageMaker Real-Time Endpoint with Auto Scaling

Answer

SageMaker Multi-Model Endpoint

Answer

SageMaker Batch Transform

Question 7

Refer to the exhibit. A SageMaker Pipeline fails with 'Invalid output reference' at the TrainingStep. What is the most likely cause?

Accepted Answer

The TrainingStep expects a single artifact but TuningStep produces multiple. In SageMaker Pipelines, a `TrainingStep` that expects a single artifact as input will fail with 'Invalid output reference' if the preceding `TuningStep` produces multiple artifacts (e.g., from multiple training jobs). The pipeline cannot resolve which specific artifact to pass, causing the error.

Answer

The TuningStep output name is misspelled

Answer

The pipeline role lacks permissions

Answer

The instance type is incompatible

Question 8

A healthcare startup is building a model to predict patient readmission within 30 days. The data is stored in Amazon Redshift and includes patient demographics, admission history, lab results, and medication records. The data scientist extracts a sample of 10,000 records to Amazon S3 as CSV files for initial prototyping. During exploratory data analysis, they find that the 'age' column has values like '150', '0', and negative numbers. The 'diagnosis_code' column contains codes like 'E11', 'E11.9', and 'e11' (inconsistent formatting). The 'readmitted' target column has 60% 'Yes' and 40% 'No'. The data scientist wants to use AWS Glue DataBrew for data cleaning. Which combination of steps should they use?

Accepted Answer

In AWS Glue DataBrew: 1) Filter age between 0 and 120 to remove invalid values. 2) Standardize diagnosis_code to uppercase using a formula. 3) Apply Random Oversampling to balance the target column.. It uses AWS Glue DataBrew's built-in capabilities to filter invalid age values (0–120), standardize the diagnosis_code to uppercase via a formula, and apply Random Oversampling to address the 60/40 class imbalance. DataBrew supports filtering, formula-based transformations, and built-in ML transforms like Random Oversampling, making this combination valid and efficient for data cleaning.

Answer

In AWS Glue DataBrew: 1) Impute age with the mean. 2) Apply Standard Scaler to all numeric columns. 3) Use Random Oversampling to balance the target column.

Answer

In AWS Glue DataBrew: 1) Replace age with median. 2) Convert diagnosis_code to uppercase. 3) Apply SMOTE to balance the target column.

Answer

In AWS Glue DataBrew: 1) Remove rows where age is outside 0-120. 2) Drop diagnosis_code column. 3) Use Random Undersampling to balance the target column.

Question 9

A company is building a recommendation system and has trained a matrix factorization model using SageMaker. They want to evaluate the model's performance using precision at k (P@k) and recall at k (R@k). They have a test set of user-item interactions. The data scientist implements a custom evaluation script that computes these metrics, but the precision values are consistently zero. What is the most likely cause?

Accepted Answer

The test set contains only positive interactions.. If the test set contains only positive interactions (i.e., every user-item pair in the test set is a ground-truth positive), then precision at k will be zero unless the model recommends exactly those items. Since the model's top-k recommendations are unlikely to perfectly match the test set's positive items for every user, precision (the fraction of recommended items that are relevant) will be zero. This is a known pitfall when evaluating implicit feedback models without negative samples.

Answer

The model outputs are not being ranked correctly.

Answer

The model is overfitting.

Answer

The k value is too large.

Question 10

A team uses MLflow on SageMaker for experiment tracking. They want to automatically deploy the best-performing model from an MLflow run to a SageMaker endpoint for real-time inference. What is the MOST efficient way to achieve this?

Accepted Answer

Use SageMaker Pipelines with the MLflow integration to register the model and deploy via a Transform step. The MLflow Model Registry can be integrated with SageMaker via the MLflow plugin for SageMaker, which allows direct deployment from the registry to an endpoint. Alternatively, using SageMaker Pipelines with the MLflow integration is more automated and production-grade.

Answer

Use AWS Step Functions to trigger an MLflow run and then call SageMaker CreateEndpoint

Answer

Set up an EventBridge rule to trigger a Lambda that deploys the model whenever a new MLflow run is logged

Answer

Manually export the model artifact from MLflow and upload to S3, then create a SageMaker model and endpoint

Question 11

A data scientist trained a model using SageMaker and wants to automate the retraining process when new data becomes available. Which AWS service is best suited to trigger a SageMaker training job based on an S3 event?

Accepted Answer

Amazon EventBridge with a rule matching S3 object creation.. Amazon EventBridge is the correct choice because it can directly capture S3 events (such as ObjectCreated) via a rule and invoke a SageMaker training job as a target. This event-driven architecture eliminates the need for polling or scheduled checks, enabling immediate retraining when new data arrives in S3.

Answer

AWS Step Functions with a scheduled trigger.

Answer

Amazon Simple Workflow Service (SWF) decider.

Answer

Amazon Simple Queue Service (SQS) with a polling script.

Question 12

A data scientist is training an XGBoost model on a large tabular dataset using SageMaker. The training job is taking too long. The scientist wants to reduce training time while maintaining model quality. Which action should the scientist take?

Accepted Answer

Use SageMaker distributed data parallelism across multiple instances. Using SageMaker's managed spot training can significantly reduce cost, but it may cause interruptions. The best approach to reduce training time is to use distributed data parallelism with multiple instances. Increasing instance type can also speed up training, but distributed training is more scalable. Using Hyperband is for hyperparameter tuning, not for reducing training time directly. Converting to a different algorithm is not necessary.

Answer

Enable SageMaker managed spot training

Answer

Switch to Hyperband for hyperparameter tuning

Answer

Convert the XGBoost model to a Linear Learner model

Question 13

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 14

A company uses Amazon SageMaker Ground Truth to create labeled datasets for object detection. The output must be in COCO format for downstream model training. How should the data preparation process be configured?

Accepted Answer

Select 'Object Detection' task type and specify 'COCO' as the output format in the labeling job configuration. Amazon SageMaker Ground Truth natively supports outputting object detection labeling jobs in COCO format. When you select 'Object Detection' as the task type, the labeling job configuration includes an option to specify 'COCO' as the output format, which automatically structures the labeled data into the required COCO JSON schema without any post-processing.

Answer

Use a built-in transformation to convert from Ground Truth JSON to COCO after labeling

Answer

Use a pre-built AWS Lambda function to transform annotations to COCO

Answer

Write a custom SageMaker Processing script to convert the output to COCO

Question 15

A data scientist needs to convert categorical variables to numerical format for a linear regression model. The dataset contains a 'Country' column with 50 unique values. Which transformation should the engineer use to avoid introducing ordinal relationships?

Accepted Answer

One-hot encoding. One-hot encoding is correct because it creates binary columns for each category, avoiding any implicit ordinal relationship between the 50 unique countries. This is essential for linear regression, which assumes numerical inputs have meaningful order; one-hot encoding ensures the model treats each country as an independent category without ranking.

Answer

Label encoding

Answer

Target encoding

Answer

Ordinal encoding