MLA-C01 Practice Test 3 — 30 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

Question 11

A team is deploying a machine learning model using Amazon SageMaker. They need to serve predictions with sub-100ms latency for a real-time application. The model is a large ensemble that requires 4 GB of memory. The team expects traffic of 100 requests per second initially, but it may double during peak hours. Which instance type and deployment configuration should the team choose to minimize cost while meeting the latency requirement?

Accepted Answer

Deploy on one ml.c5.large instance with an Application Auto Scaling target tracking policy based on memory utilization. The ml.c5.large instance provides 4 GB of memory, which meets the model's requirement, and its compute-optimized nature ensures low-latency inference. Using Application Auto Scaling with a target tracking policy based on memory utilization allows the instance to scale out during traffic spikes (up to 200 requests per second) while minimizing cost by running a single instance during normal load.

Answer

Deploy on one ml.t2.medium instance with an Application Auto Scaling target tracking policy based on CPU utilization

Answer

Deploy on one ml.p3.2xlarge instance with provisioned concurrency

Answer

Deploy on two ml.m5.large instances behind a load balancer with manual scaling

Question 12

A data scientist is using SageMaker to train an XGBoost model for a regression problem. After training, they evaluate the model on a test set and get an RMSE of 10 and an R² of 0.85. Which additional metric would give the MOST insight into the model's average prediction error magnitude?

Accepted Answer

Mean Absolute Error (MAE). MAE (Mean Absolute Error) gives the average absolute prediction error, which is easy to interpret in the same units as the target. RMSE gives a similar but squared metric, and R² indicates variance explained, but MAE directly answers the average error magnitude.

Answer

AUC

Answer

Confusion matrix

Answer

F1 score

Question 13

A team uses SageMaker Pipelines to train and register a model. They want to conditionally run a hyperparameter tuning step only if the data quality check passes. Which pipeline step type should they use to branch the execution?

Accepted Answer

ConditionStep. The ConditionStep allows comparing values and branching to different steps. If data quality passes, the tuning step runs; otherwise, the pipeline stops or runs an alternative step. Other steps do not provide conditional branching.

Answer

TuningStep

Answer

TrainingStep

Answer

TransformStep

Question 14

A company uses SageMaker Model Monitor to track feature attribution drift with SHAP. They notice that the SHAP values have changed significantly for a feature, while the model performance remains stable. What is the MOST likely interpretation?

Accepted Answer

The model's behavior has changed, which may lead to future performance degradation and warrants investigation. A significant change in SHAP values indicates that the model's internal feature importance has shifted, even if overall performance metrics like accuracy or loss remain stable. This is a classic sign of concept drift or model behavior drift, where the model's decision boundary has changed for that feature, which can lead to future performance degradation as the drift accumulates. SageMaker Model Monitor tracks feature attribution drift separately from data drift, and a change in SHAP values without data drift suggests the model is relying on the feature differently, warranting investigation.

Answer

The ground truth labels are incorrect, causing the drift

Answer

Data drift has occurred for that feature

Answer

The model is learning equally from all features, so no action is needed

Question 15

A company wants to deploy a machine learning model that makes real-time predictions for a mobile app. The model is a deep neural network with a large model size (500 MB). Which SageMaker endpoint configuration is most cost-effective while meeting low-latency requirements?

Accepted Answer

Real-time endpoint with a single instance. A real-time endpoint with a single instance provides the lowest latency for a 500 MB deep neural network model, as it keeps the model loaded in memory and ready for inference without cold starts or multi-model overhead. This configuration is also cost-effective for consistent traffic patterns, as you pay for the instance uptime rather than per-invocation or for multiple model loads.

Answer

Multi-model endpoint

Answer

Serverless inference

Answer

Batch transform

Question 16

A company is training a large Transformer model on SageMaker and wants to use model parallelism to fit the model into memory. The model has 10 billion parameters. Which instance type is MOST cost-effective for this task while supporting SageMaker's model parallelism?

Accepted Answer

ml.p4d.24xlarge. The ml.p4d.24xlarge instances are optimized for large-scale distributed training with high memory and support SageMaker's model parallelism. ml.trn1 instances are designed for training with AWS Trainium, but they use a different chip architecture and may require specific SDKs. ml.g4dn instances are for inference and light training. ml.c5 instances are compute-optimized but lack GPU memory for large models.

Answer

ml.trn1.32xlarge

Answer

ml.c5.18xlarge

Answer

ml.g4dn.12xlarge

Question 17

A machine learning engineer uses Amazon SageMaker Data Wrangler to preprocess a dataset. After applying a transform, the engineer wants to export the data to a feature group in Amazon SageMaker Feature Store for reuse in training and inference. Which export option should they choose?

Accepted Answer

Export to Amazon SageMaker Feature Store. SageMaker Data Wrangler can export directly to a feature group in Feature Store, making the features available for both training (offline) and inference (online).

Answer

Export to Amazon DynamoDB

Answer

Export to Amazon S3 as CSV

Answer

Export to Amazon SageMaker Pipelines

Question 18

A company wants to deploy its trained model to edge devices such as cameras and IoT devices. The model must run efficiently with low latency and minimal memory footprint. Which THREE actions should the company take to prepare the model for edge deployment? (Choose THREE.)

Accepted Answer

Use SageMaker Edge Manager to package and manage the model on devices.. SageMaker Edge Manager is purpose-built to package, optimize, and manage machine learning models on edge devices. It provides model packaging, runtime monitoring, and over-the-air updates, ensuring the model runs efficiently with low latency and minimal memory footprint on resource-constrained hardware like cameras and IoT devices.

Answer

Increase the model's complexity to improve accuracy on edge devices.

Answer

Deploy the model directly as a SageMaker endpoint and have the edge devices call it over the internet.

Question 19

A data engineer is building a feature engineering pipeline in AWS Glue ETL to process streaming data from Amazon Kinesis. The data includes a nested JSON structure with arrays. The engineer needs to flatten the nested structures into a tabular format for machine learning. Which THREE approaches are valid for this task? (Choose 3.)

Accepted Answer

Use Python's json.loads in a map function. Python's json.loads can be used within a PySpark map function to parse nested JSON strings from streaming data in AWS Glue ETL. This allows you to extract and flatten nested fields into a tabular structure by iterating over each record and converting the JSON into a flat dictionary, which can then be mapped to DataFrame columns.

Answer

Use Athena's UNNEST function on the raw data

Answer

Use Amazon SageMaker Processing with scikit-learn

Question 20

A company needs to deploy a new model version to a SageMaker real-time endpoint. They want to route 5% of traffic to the new version initially to monitor for errors before full rollout. Which deployment strategy should they use?

Accepted Answer

Canary deployment with production variants. A canary deployment with production variants allows you to route a specific percentage of traffic (e.g., 5%) to the new model version by adjusting the `InitialVariantWeight` parameter in the production variant configuration. This enables gradual traffic shifting while monitoring errors, and you can later increase the weight to 100% for full rollout. SageMaker real-time endpoints support this natively by hosting multiple model variants behind the same endpoint.

Answer

Blue/green deployment

Answer

Shadow testing

Answer

Multi-model endpoint