MLA-C01 Practice Test 37 — 15 Questions

Question 1

A machine learning team is building a model to predict customer churn. The dataset includes a feature 'customer_tenure' with values ranging from 1 to 100 months, and 'monthly_spend' ranging from $10 to $5000. The model will use gradient boosting. Which feature scaling approach is most appropriate?

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

Accepted Answer

No scaling is required for gradient boosting. Gradient boosting, as a tree-based ensemble method, makes split decisions based on feature values rather than distances or gradients that depend on feature magnitudes. Therefore, it is inherently scale-invariant, and no feature scaling is required. Applying scaling like MinMax or StandardScaler would not improve model performance and could introduce unnecessary computational overhead.

Answer

Apply log transformation to 'monthly_spend' only

Answer

Apply MinMaxScaler to both features

Answer

Apply StandardScaler to both features

Question 2

A machine learning team deploys a model for loan approval. They want to monitor data drift on the real-time endpoint using SageMaker Model Monitor. Which set of actions should they take to set up data quality monitoring?

Accepted Answer

Enable data capture on the endpoint, generate a baseline from training data, create a data quality monitoring schedule, and set up a CloudWatch Alarm on violations. SageMaker Model Monitor requires a baseline from training data, then schedules monitoring jobs that compare live endpoint captures against that baseline. Alerts are sent via CloudWatch Alarms.

Answer

Use SageMaker Clarify to detect data drift on the endpoint

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Create a model quality monitoring schedule directly on the endpoint without any baseline

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Enable data capture and rely on SageMaker Model Monitor to automatically infer drift without a baseline

Question 3

A data scientist suspects that a deep learning model is overfitting. They enable SageMaker Debugger and want to detect overfitting automatically. Which built-in rule should they use?

Accepted Answer

Overfit. SageMaker Debugger includes a built-in rule called Overfit that monitors the gap between training and validation loss (or accuracy) and triggers when the model begins to overfit. It is the direct, purpose-built rule for automatic overfitting detection. Enabling it requires the training script to emit both training and validation metrics via the debugger hook.

Answer

ExplodingGradients

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PoorWeightInitialization

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DeadRelu

Question 4

A team uses SageMaker Model Monitor to track data quality. They notice that the monitor's constraint violations are increasing but the model performance remains good. What should they do?

Accepted Answer

Investigate the specific features that are violating constraints to see if they are still relevant.. Increasing constraint violations in SageMaker Model Monitor do not necessarily indicate model degradation; they may reflect benign data drift where feature distributions shift but the model's predictive performance remains intact. Investigating specific violating features allows the team to determine whether the drift is meaningful (e.g., due to a real-world change that the model should adapt to) or irrelevant (e.g., a feature that is no longer used in the inference pipeline). This aligns with the monitoring best practice of separating data quality alerts from model performance metrics to avoid unnecessary retraining or threshold tuning.

Answer

Disable the monitor because it is not affecting performance.

Answer

Relax the constraint thresholds to reduce alerts.

Answer

Retrain the model using the latest data.

Question 5

A data engineer wants to transform a categorical feature with 1,000 possible values into numerical features for a linear model. Which feature engineering technique is most appropriate for this high-cardinality feature?

Accepted Answer

Target encoding. Target encoding replaces each category with the mean of the target variable for that category, which handles high cardinality without exploding dimensionality. One-hot encoding creates 1,000 columns, which is problematic for linear models.

Answer

One-hot encoding

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Ordinal encoding

Answer

Label encoding

Question 6

A data science team deploys a machine learning model to a SageMaker endpoint for real-time inference. They need to monitor the model for feature distribution drift over time to ensure the model's predictions remain accurate. Which AWS service should they use?

Accepted Answer

SageMaker Model Monitor. SageMaker Model Monitor is the correct service because it is specifically designed to continuously monitor machine learning models deployed to SageMaker endpoints for data quality issues, including feature distribution drift. It automatically captures inference data, computes statistics against a baseline, and triggers alerts when drift is detected, ensuring the model's predictions remain accurate over time.

Answer

Amazon CloudWatch Evidently

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AWS Glue DataBrew

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SageMaker Clarify

Answer

SageMaker Debugger

Question 7

A data science team detects that a deployed model's prediction accuracy is degrading over time due to concept drift. They need to implement a retraining strategy. Which THREE actions are recommended best practices for handling concept drift?

Accepted Answer

Monitor prediction quality using ground truth labels when available.. Option B is correct because continuously monitoring prediction quality against ground truth labels (e.g., via SageMaker Model Monitor's ModelQuality baseline and CloudWatch metrics) is the only way to quantify real accuracy degradation and confirm concept drift rather than mere data drift. Option D is correct because SageMaker Pipelines can orchestrate incremental retraining workflows that incorporate newly labeled data, allowing the model to adapt to the changed concept without rebuilding the entire pipeline manually. Option E is correct because SageMaker Model Monitor detects drift (data drift, model quality drift) and can emit CloudWatch alarms that trigger a retraining pipeline, closing the detect-to-retrain loop automatically. Option A is not recommended as a primary strategy because rolling back to an old model does not address the underlying concept drift and the previous version will also degrade on the new distribution. Option C is not recommended because fixed-schedule retraining ignores actual performance signals, wasting compute when drift is absent and lagging when drift is rapid.

Answer

Automatically roll back to a previous model version upon drift detection.

Answer

Retrain the model on a fixed schedule regardless of performance.

Question 8

A data science team deploys a regression model to Amazon SageMaker for real-time inference. After one month, the model's prediction errors increase significantly, but data distributions remain unchanged. Which monitoring approach is MOST suitable for detecting this issue?

Accepted Answer

Set up Amazon SageMaker Model Monitor to track model performance metrics against ground truth labels as they arrive.. Amazon SageMaker Model Monitor can be configured to track model performance metrics (e.g., regression error metrics like RMSE or MAE) against ground truth labels as they arrive. Since the question states that data distributions remain unchanged but prediction errors increase, the issue is likely model degradation (e.g., concept drift or model staleness) rather than data drift. Monitoring ground truth labels directly captures this performance degradation, making option A the most suitable approach.

Answer

Use Amazon SageMaker Clarify to monitor feature attribution drift.

Answer

Enable Amazon CloudWatch to monitor model endpoint latency.

Answer

Configure Amazon SageMaker Model Monitor to track data drift on the input features.

Question 9

A company is using SageMaker endpoints for inference. To reduce costs, they want to use Automatic Scaling. However, they observe that scaling up takes several minutes, causing latency spikes during traffic bursts. What should they do to mitigate this?

Accepted Answer

Configure the endpoint with a target tracking scaling policy and pre-warm additional instances during expected traffic surges.. It combines a target tracking scaling policy with pre-warming additional instances during expected traffic surges. Pre-warming (or provisioning) instances ahead of time ensures that capacity is available when the burst occurs, mitigating the latency spike caused by the several-minute scaling-up delay. This approach directly addresses the cold-start problem in SageMaker auto-scaling.

Answer

Optimize the model to reduce inference time.

Answer

Use larger instance types to handle more requests per instance.

Answer

Set the endpoint to scale down slowly to maintain capacity.

Question 10

A team is deploying a real-time inference endpoint in SageMaker. The model requires access to an S3 bucket containing customer data, which is encrypted with SSE-KMS. The team needs to ensure that the endpoint can decrypt the data. Which IAM role configuration is necessary?

Accepted Answer

Add kms:Decrypt permission to the SageMaker execution role for the specific KMS key.. The SageMaker execution role must have the kms:Decrypt permission for the specific KMS key that encrypted the S3 objects. When the endpoint reads data from the S3 bucket, SageMaker uses its execution role to call KMS to decrypt the data. Without this permission, the endpoint will fail with an access denied error, even if the S3 bucket policy allows s3:GetObject.

Answer

Add kms:GenerateDataKey permission to the SageMaker execution role.

Answer

Attach a policy to the S3 bucket granting s3:GetObject to the KMS key.

Answer

Configure the endpoint to assume the S3 bucket's IAM role.

Question 11

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

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Principal Component Analysis (PCA)

Answer

StandardScaler

Question 12

A data team is using Amazon SageMaker Data Wrangler to prepare a dataset. They need to detect potential bias in the data before training a model. Which feature of Data Wrangler should they use?

Accepted Answer

Bias detection with Amazon SageMaker Clarify. Data Wrangler integrates with Amazon SageMaker Clarify for bias detection. Therefore, the correct answer is D: Bias detection with Amazon SageMaker Clarify. Options A and B are features of Data Wrangler but not specifically for bias detection. Option C (Export to Feature Store) is unrelated to bias detection.

Answer

Visual data profiling

Answer

Built-in transforms for missing values

Answer

Export to Feature Store

Question 13

A team wants to use SageMaker Clarify to monitor bias in their production model predictions. They have configured a bias drift monitor. What does SageMaker Clarify compare to detect bias drift?

Accepted Answer

Current bias metrics against a baseline bias metrics computed from training data. SageMaker Clarify bias drift monitoring compares the current bias metrics (e.g., disparate impact) computed on live data against a baseline bias metric computed from the training data. This detects if bias has drifted over time.

Answer

Current input data distribution against the training data distribution

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Current SHAP feature attributions against baseline SHAP values

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Current predictions against ground truth labels collected in real-time

Question 14

A team is deploying a model using SageMaker Pipelines. They have defined a pipeline with steps: preprocessing, training, evaluation, and conditional registration. The evaluation step produces a JSON file with metrics. If accuracy > 0.9, the model is registered; else, the pipeline fails. Which TWO statements about this pipeline are correct? (Choose TWO.)

Accepted Answer

The evaluation step must output a JSON file in a specific format to be used by the condition step.. Option A is correct because SageMaker Pipelines' ConditionStep consumes a JSON property file produced by a preceding step (typically via a PropertyFile output), and that file must follow the supported JSON structure so the condition can parse and evaluate the metric value. Option B is correct because the condition can reference the accuracy value either through a pipeline parameter (e.g., a ParameterString/ParameterFloat passed in) or through the step's property file using JsonGet (e.g., JsonGet(step_name=eval_step, property_file='evaluation.json', json_path='metrics.accuracy')), which is exactly how the threshold comparison is expressed. Option C is incorrect because conditional branching in SageMaker Pipelines is natively handled by the ConditionStep, not by invoking a separate Lambda function. Option D is incorrect because a failing condition causes the pipeline to fail or take the defined branch; it does not trigger an automatic retry of the training step. Option E is incorrect because registering the model before evaluating the condition would defeat the purpose of gating registration on accuracy > 0.9.

Answer

The conditional step should be implemented as a separate Lambda function called from the pipeline.

Answer

The pipeline will automatically retry the training step if the condition fails.

Answer

The model registration step should be placed before the condition step to ensure the model is always registered.

Question 15

A team is using Amazon SageMaker Data Wrangler to prepare a large dataset. They need to detect potential bias in the data before training. Which capability of Data Wrangler should they use?

Accepted Answer

Integration with Amazon SageMaker Clarify for bias reports. Amazon SageMaker Data Wrangler integrates directly with Amazon SageMaker Clarify to detect bias in datasets. This integration allows you to run bias analysis on your data before training, generating reports that highlight potential imbalances or unfairness in features and target variables. It is the correct capability for the team's stated need.

Answer

Built-in transform for SMOTE oversampling

Answer

Use of Amazon Athena to query data for bias patterns

Answer

Export to Amazon SageMaker Feature Store