MLA-C01 Practice Test — Free AWS Certified Machine Learning Engineer Associate MLA-C01 Questions with Explanations

A company wants to use SageMaker Autopilot to automatically build a binary classification model. Which output does Autopilot provide to help understand model decisions?

Which SageMaker built-in algorithm should be used for forecasting time series data with seasonal patterns?

Which SageMaker built-in algorithm is specifically designed for time series forecasting?

A financial services firm is training a fraud detection model using SageMaker. The dataset is highly imbalanced (0.1% fraudulent transactions). The model currently achieves 99.9% accuracy but only catches 5% of fraud cases. Which metric should the team prioritize to evaluate model performance?

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?

A data engineer needs to prepare a large dataset for machine learning. The data is stored in an Amazon RDS MySQL database and needs to be transformed and moved to an S3 bucket in Parquet format for use with SageMaker. Which AWS service is most suitable for this extraction, transformation, and loading (ETL) task?

A data scientist is using SageMaker Data Wrangler to prepare a large dataset. The data contains duplicate rows, which could bias the model. Which built-in step in Data Wrangler can automatically detect and remove duplicates?

A data scientist is training a binary classifier on a highly imbalanced dataset (1:100 class ratio). The dataset contains 500,000 rows and 30 features. The data is stored in S3 in Parquet format. The data scientist wants to use SageMaker's built-in XGBoost algorithm. Which data preparation technique should the data scientist apply to best address the class imbalance without causing data leakage?

A healthcare company is building a model to predict patient readmission rates. The dataset contains a mix of numeric features (age, blood pressure, lab test results) and categorical features (gender, diagnosis code, hospital department). The dataset has 2 million rows. The data is stored in an Amazon S3 bucket, and they use AWS Glue to catalog and preprocess the data. The data scientist notices that the 'diagnosis_code' column has 10,000 unique codes, and 20% of the rows have missing values for 'blood_pressure'. They plan to use a SageMaker built-in XGBoost model. For optimal model performance, which preprocessing steps should they apply using AWS Glue ETL?

A data engineer is building a data pipeline for a machine learning model that requires both structured and unstructured data. The structured data (customer demographics) is in Amazon RDS, and the unstructured data (customer support chat logs) is in Amazon S3 as JSON files. The engineer needs to combine these datasets into a single training dataset stored in S3 in Parquet format. They must also perform feature engineering such as text vectorization on the chat logs. The pipeline should be serverless and cost-effective. Which approach should they use?

A team built a SageMaker Pipeline that includes a training step and a model evaluation step. They want to automatically register a model in SageMaker Model Registry only if the evaluation metric (accuracy) exceeds 0.9. Which pipeline step should be used to implement this conditional logic?

A machine learning engineer needs to deploy a new version of a model gradually, initially sending 5% of traffic to the new version and 95% to the current version, while monitoring for errors. Which deployment pattern should they use?

A data science team uses SageMaker Pipelines to orchestrate their ML workflow. They noticed that even when source data hasn't changed, the pipeline re-runs all steps, wasting compute time. What should they enable to avoid redundant runs?

A financial services company needs to deploy a machine learning model for real-time fraud detection. The model must be highly available across multiple Availability Zones and must support automatic scaling based on request volume. The company also needs to perform canary deployments to test new model versions with a small percentage of traffic before full rollout. Which SageMaker feature should they use?

A retail company has a SageMaker model that predicts customer churn. The model was trained on data that included a 'customer_zipcode' feature. After deployment, the data science team notices that the model's predictions for certain zip codes have become less accurate over time. They suspect that the relationship between zip code and churn has changed due to a recent relocation of a major employer. Which SageMaker monitoring capability should they use to detect this type of drift?

A company wants to deploy a foundation model from SageMaker JumpStart with the lowest possible inference cost, given that latency requirements are flexible. They have a mix of traffic volumes. Which approach should they take?

A company wants to track the lineage of their ML models, including the training dataset, hyperparameters, and training job used to produce each model version. Which AWS service should they use?

An organization needs to ensure that all data transmitted between containers in a SageMaker training job is encrypted. In the training job configuration, which setting should they enable?

A company uses Amazon SageMaker to train and deploy a machine learning model. After deployment, they notice that the model's accuracy drops significantly over time due to changes in the underlying data distribution. Which monitoring solution should they implement to detect this issue automatically?

An ML team is preparing time-series data for a demand forecasting model. They want to evaluate model performance over time without leaking future information into past training windows. Which data splitting strategy is MOST appropriate?