MLA-C01 Practice Test 7 — 10 Questions

Question 1

Refer to the exhibit. A data scientist is trying to use AWS Glue to read data from the S3 bucket `ml-data-bucket`. The Glue job fails with an access denied error. What is the most likely cause?

Accepted Answer

The policy does not include s3:ListBucket permission on the bucket. The error occurs because the IAM policy attached to the Glue job role grants s3:GetObject on the bucket objects (via the `arn:aws:s3:::ml-data-bucket/*` resource) but does not include the s3:ListBucket permission on the bucket itself (`arn:aws:s3:::ml-data-bucket`). When AWS Glue reads data from S3, it first performs a ListBucket operation to enumerate objects in the bucket or prefix, and without that permission, the request is denied even if GetObject is allowed.

Answer

The policy allows s3:PutObject but the job only reads

Answer

The policy does not specify the bucket ARN without /*

Answer

The Glue job role does not have the required permissions

Question 2

An ML team wants to deploy a model that was trained using XGBoost in SageMaker. They want to use the built-in XGBoost algorithm container for inference. Which inference option requires the least custom code?

Accepted Answer

Deploy to a real-time endpoint using the built-in XGBoost container. The built-in XGBoost container in SageMaker is pre-configured with the XGBoost serving stack, including the necessary inference code and dependencies. Deploying a model trained with XGBoost to a real-time endpoint using this container requires no custom inference script or Docker image, only the model artifact and endpoint configuration. This minimizes custom code to just the SageMaker SDK calls for creating the model and endpoint.

Answer

Create a custom Docker container with XGBoost and deploy to an endpoint

Answer

Attach Elastic Inference to a generic container

Answer

Use SageMaker Python SDK to download the model and run local inference

Question 3

A data scientist is preparing a dataset for binary classification using SageMaker. The dataset has 100 features and 10,000 rows, but the target variable is highly imbalanced (95% negative, 5% positive). Which technique should the data scientist apply during data preparation to address the imbalance?

Accepted Answer

Apply SMOTE to generate synthetic samples for the minority class. SMOTE (Synthetic Minority Oversampling Technique) is the most appropriate technique because it generates synthetic samples for the minority class by interpolating between existing minority instances, which avoids the overfitting risk of simple duplication (oversampling) and the information loss from undersampling. In SageMaker, SMOTE can be applied during data preparation using libraries like imbalanced-learn before training, or via SageMaker Data Wrangler's built-in transform, making it a robust choice for handling class imbalance without discarding data.

Answer

Oversampling the minority class by duplicating examples

Answer

Collect more data to match the number of samples in both classes

Answer

Random undersampling of the majority class

Question 4

A machine learning engineer is using Amazon SageMaker Data Wrangler to prepare a dataset for a regression model. After applying a StandardScaler to numeric features, the target variable has a mean of 50 and standard deviation of 20. Which additional step should the engineer take to reduce model bias?

Accepted Answer

Use SageMaker Clarify to evaluate bias in the dataset. SageMaker Clarify is specifically designed to detect various types of bias in datasets and models, including regression bias. Even after standard scaling, the target variable's distribution (mean=50, std=20) may still contain systemic biases related to sensitive attributes (e.g., race, gender). SageMaker Clarify computes bias metrics such as Conditional Demographic Disparity in Labels (CDDL) and can identify whether the model's predictions are unfairly skewed across demographic groups, which is the direct step needed to reduce model bias.

Answer

Apply MinMaxScaler to the target variable

Answer

Apply one-hot encoding to all categorical features

Answer

Remove all features with a correlation above 0.8

Question 5

A data scientist is using Amazon SageMaker Studio to develop a model. The training job is taking longer than expected. The data scientist suspects that the data is being downloaded from Amazon S3 each time the training starts. What is the BEST way to reduce data loading time?

Accepted Answer

Use SageMaker Pipe Input mode to stream data directly from S3.. SageMaker Pipe input mode streams data directly from S3 into the training algorithm without first downloading it to the training instance's local storage. This eliminates the bottleneck of copying entire datasets, reducing startup time and disk usage. It is the most direct and efficient way to address the issue of repeated downloads from S3.

Answer

Enable S3 transfer acceleration and cache the data in S3.

Answer

Use a larger instance type with more network bandwidth.

Answer

Use Amazon FSx for Lustre to mount a high-performance file system.

Question 6

A company deploys a model with SageMaker and wants to monitor for concept drift. They have noticed that the relationship between input features and the target variable has changed, causing model accuracy to degrade. However, the input data distribution remains stable. Which type of drift is this, and what is the most appropriate response strategy?

Accepted Answer

Concept drift; retrain the model with newly collected labeled data. This is concept drift because the relationship between input features and the target variable has changed while the input data distribution remains stable. The most appropriate response is to retrain the model with newly collected labeled data that reflects the current relationship, as concept drift requires updating the model's learned mapping from features to labels.

Answer

Concept drift; ignore the change as long as input distribution remains stable

Answer

Data drift; update the baseline statistics and continue monitoring

Answer

Data drift; retrain the model with the latest training data

Question 7

A financial services company is deploying a credit risk model using SageMaker. They require that the model always uses the latest approved version from the Model Registry. They also need to maintain a detailed audit trail of all model version transitions (e.g., from PendingApproval to Approved). The deployment should be fully automated and must roll back immediately if the new model's error rate exceeds the old model's error rate by more than 2% during a canary deployment. Which solution meets these requirements with the least custom code?

Accepted Answer

Use SageMaker Pipelines with a conditional step to deploy the model after approval, and include a canary deployment using a weight endpoint variant. Use CloudWatch alarms to trigger automatic rollback.. SageMaker Pipelines natively supports conditional execution and canary deployments using endpoint weight variants, which together enable automated rollback triggered by CloudWatch alarms when the error rate exceeds the 2% threshold. This approach requires minimal custom code by leveraging built-in SageMaker capabilities for model registry integration, deployment, and monitoring.

Answer

Use AWS CodePipeline with a deployment action that uses AWS CloudFormation to update the endpoint. Add a manual approval step for rollback.

Answer

Create an AWS Lambda function that is triggered by Model Registry events, deploys the model to a staging endpoint, runs a canary test, and if successful, updates the production endpoint.

Answer

Use an Amazon EKS cluster with a custom inference container and use ArgoCD for automated deployments.

Question 8

A company is deploying a large NLP model on SageMaker for real-time inference. They want to reduce inference latency and cost by optimizing the model for the target hardware. The model is trained in PyTorch. Which SageMaker feature should they use to compile the model for best performance on the chosen instance?

Accepted Answer

SageMaker Neo. SageMaker Neo is the correct choice because it is specifically designed to compile trained models (including PyTorch models) into an optimized binary for a target hardware instance, reducing inference latency and improving throughput. Neo applies hardware-specific optimizations such as operator fusion, memory layout tuning, and quantization, which directly address the need for best performance on the chosen SageMaker instance.

Answer

AWS Step Functions

Answer

Amazon Elastic Inference

Answer

SageMaker Triton Inference Server

Question 9

A data engineer is using SageMaker Pipelines to automate data preparation. Which TWO statements about data validation within a pipeline are correct?

Accepted Answer

The pipeline can be configured to fail if data quality checks do not meet thresholds. SageMaker Pipelines allows you to define conditions that evaluate the output of data quality checks (e.g., using Amazon SageMaker Model Monitor or custom validation scripts). If the checks fail to meet specified thresholds (e.g., missing values exceed 5%), the pipeline can be configured to fail, stopping execution and preventing downstream steps from processing invalid data.

Answer

Data validation can only be performed on training data, not inference data

Answer

Data validation steps cannot pass results to subsequent steps

Answer

Data validation requires a trained model to evaluate predictions

Question 10

A data engineer is using Amazon SageMaker Data Wrangler to create a data preparation flow for a dataset with 500 columns, many of which are highly correlated. The goal is to reduce dimensionality while preserving interpretability. Which built-in transform in Data Wrangler should be applied?

Accepted Answer

Feature Selection (correlation-based). The goal is to reduce dimensionality while preserving interpretability. SageMaker Data Wrangler's built-in Feature Selection (correlation-based) transform identifies and removes highly correlated columns, directly reducing the number of features without transforming the original variables into new, uninterpretable components. This preserves the meaning of each selected column, which is essential when interpretability is a priority.

Answer

Imputation

Answer

Principal Component Analysis (PCA)

Answer

StandardScaler