MLA-C01 Practice Test 29 — 15 Questions

Question 1

A company is using SageMaker Autopilot to automatically build a regression model on a dataset. They want to understand which features are most important for the model's predictions. Which feature of Autopilot can provide this insight?

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

Accepted Answer

Autopilot explainability report. SageMaker Autopilot can generate explainability reports that include feature importance, either through SHAP or other methods, depending on the model type.

Answer

Autopilot candidate definition notebook

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Autopilot model leaderboard

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Autopilot data exploration report

Question 2

A data scientist is performing feature selection for a linear regression model and wants to remove features that are highly correlated with each other to reduce multicollinearity. Which technique is BEST suited for this purpose?

Accepted Answer

Correlation analysis. Correlation analysis directly measures the linear relationship between pairs of features, producing a correlation matrix that identifies highly correlated pairs (e.g., |r| > 0.8) for removal. Since multicollinearity in linear regression is specifically about linear dependence among predictors, correlation analysis is the most direct and interpretable technique for detecting and removing redundant features.

Answer

Lasso regularization

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Mutual information

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Recursive Feature Elimination (RFE)

Question 3

A data scientist is preparing a dataset for a multi-class classification problem. The dataset contains a categorical feature with 50,000 unique values (high cardinality). The scientist wants to reduce dimensionality while preserving predictive information. Which TWO approaches are appropriate? (Choose 2)

Accepted Answer

Target encoding. Target encoding (Option A) is appropriate because it replaces each of the 50,000 categories with a statistic derived from the target variable (e.g., the mean target value per category), collapsing the feature into a single numeric column and thereby reducing dimensionality while retaining predictive signal about the classes. Count encoding (Option C) is also appropriate because it maps each category to its frequency in the dataset, producing one numeric column that captures useful information (rare vs. common categories) without expanding the feature space. One-hot encoding (Option B) is not suitable here since it would create roughly 50,000 sparse binary columns, which is the opposite of dimensionality reduction. Ordinal encoding based on alphabetical order (Option D) imposes an arbitrary, meaningless ordering on the categories and does not reduce dimensionality in a way that preserves predictive information. Label encoding (Option E) similarly assigns arbitrary integer IDs to categories, introducing a false ordinal relationship and offering no principled preservation of predictive information.

Answer

One-hot encoding

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Ordinal encoding based on alphabetical order

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Label encoding

Question 4

A machine learning engineer wants to deploy a pre-trained foundation model for text summarization using SageMaker JumpStart. Which of the following is a primary cost consideration when deploying such a model?

Accepted Answer

The cost of GPU instances required for low-latency inference. When deploying a pre-trained foundation model via SageMaker JumpStart, the model is already trained, so fine-tuning cost is optional and not primary. The main ongoing cost is the compute instance used for inference, especially GPU instances needed for low-latency, high-throughput text summarization. Data transfer for inference requests is typically negligible compared to compute, and S3 storage for model artifacts is a minor one-time cost.

Answer

The cost of fine-tuning the model on custom data

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The cost of data transfer for inference requests

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The cost of storing the model artifacts in S3

Question 5

A company is building a real-time fraud detection system using Amazon Kinesis Data Streams. The data must be joined with a reference table (e.g., customer profile) that is stored in Amazon DynamoDB and updated frequently. The enriched data will be used for ML predictions. Which THREE AWS services should the company use to build this streaming pipeline? (Select THREE.)

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink. Amazon Kinesis Data Streams (C) is the ingestion backbone for the real-time fraud events, providing durable, low-latency, ordered streaming with shards and configurable retention so downstream consumers can process records continuously. Amazon Kinesis Data Analytics for Apache Flink (A) is the correct processing engine because it supports stateful stream processing and can perform asynchronous lookups against DynamoDB (via the Flink DynamoDB connector or async I/O) to enrich each event with the frequently updated customer profile before feeding ML predictions. Amazon DynamoDB (E) is the reference table store, offering single-digit-millisecond key-value reads at scale so the Flink job can join each streaming record with current customer profile data. Amazon Kinesis Data Firehose (B) is not appropriate here because it is a managed delivery service for loading data into destinations like S3, Redshift, or Splunk and does not perform stateful joins or ML enrichment. AWS Glue ETL (D) is a batch/serverless Spark-based ETL service and is not designed for sub-second, continuously running stream joins against a rapidly changing DynamoDB table.

Answer

Amazon Kinesis Data Firehose

Answer

AWS Glue ETL

Question 6

A data science team uses SageMaker Pipelines for automated training. They need to conditionally register a model only if evaluation metrics exceed a threshold. Which pipeline step type should they use after the evaluation step?

Accepted Answer

Condition step. The Condition step evaluates a condition and branches the pipeline; if the condition is met, the pipeline proceeds to register the model.

Answer

Processing step

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Transform step

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RegisterModel step

Question 7

A data scientist is building a time-series forecasting model for daily sales data. The data spans two years. To evaluate the model's performance, the data scientist needs to simulate a realistic rolling forecast scenario. Which data splitting strategy should be used?

Accepted Answer

Walk-forward validation. Walk-forward validation (also called rolling-origin or expanding-window validation) repeatedly trains on past data and tests on the next time period, then moves the window forward. This mimics a realistic rolling forecast where the model is retrained as new data arrives, making it the correct choice for time-series evaluation.

Answer

Random 80/20 train-test split

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Stratified k-fold cross-validation

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Hold-out split based on time (e.g., train on first 18 months, test on last 6 months)

Question 8

A machine learning engineer wants to automatically trigger a retraining pipeline whenever new training data arrives in an S3 bucket. The pipeline uses SageMaker Pipelines. Which AWS service should be used to detect the S3 event and start the pipeline?

Accepted Answer

Amazon EventBridge. Amazon EventBridge can be configured to listen for S3 events (e.g., PutObject) and then invoke a Lambda function that starts the SageMaker Pipeline execution. Step Functions could orchestrate the pipeline but is not needed to trigger on S3 events. SageMaker Pipelines does not natively listen to S3 events. CloudWatch Events is the older name for EventBridge.

Answer

SageMaker Pipelines native S3 trigger

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AWS Step Functions

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Amazon CloudWatch Logs

Question 9

Which SageMaker feature allows you to automatically tune hyperparameters using Bayesian optimization?

Accepted Answer

SageMaker Automatic Model Tuning. SageMaker Automatic Model Tuning (AMT) is the feature that automatically searches for the best hyperparameters using strategies like Bayesian optimization. It runs multiple training jobs with different hyperparameter combinations and evaluates them against a chosen objective metric to find the optimal set. This reduces the manual effort of tuning and improves model performance.

Answer

SageMaker Autopilot

Answer

SageMaker Experiments

Answer

SageMaker Debugger

Question 10

A company deploys a large NLP model on a SageMaker real-time endpoint using an ml.p3.2xlarge instance. To reduce inference cost without sacrificing throughput, they want to compile the model for their target hardware. Which service should they use?

Accepted Answer

SageMaker Neo. SageMaker Neo is the correct service because it compiles trained machine learning models into an optimized binary for a specific target hardware (e.g., ml.p3.2xlarge with NVIDIA GPUs). This reduces inference latency and cost by applying hardware-specific optimizations such as kernel fusion and memory layout tuning, while preserving the original model's throughput. The compilation process uses Apache TVM under the hood to generate efficient code for the target instance type.

Answer

Triton Inference Server on SageMaker

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Amazon Elastic Inference

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SageMaker Inference Recommender

Question 11

An ML engineer needs to split a dataset into training, validation, and test sets. The dataset has a time-based column that should not be leaked. Which split method is most appropriate?

Accepted Answer

Temporal split based on date. A temporal split ensures that the time-based column is not leaked by preserving the chronological order of the data. This method uses the date column to assign earlier records to the training set and later records to the validation and test sets, preventing future information from influencing the model during training.

Answer

Stratified split based on target

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Random split with 70/20/10

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K-fold cross-validation

Question 12

A data scientist is using SageMaker built-in XGBoost algorithm for a regression problem. Which metric is most appropriate as the objective metric for hyperparameter tuning?

Accepted Answer

RMSE. RMSE (Root Mean Squared Error) is the most appropriate objective metric for hyperparameter tuning in a regression problem because it directly measures the average magnitude of prediction errors, with larger errors penalized more heavily. SageMaker's built-in XGBoost algorithm supports RMSE as an evaluation metric for regression, and it is commonly used as the objective metric for tuning jobs.

Answer

NDCG

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AUC

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F1

Question 13

A data scientist is training a binary classification model using Amazon SageMaker. The dataset has a severe class imbalance (95% negative, 5% positive). The model achieves 99% accuracy but fails to identify positive cases correctly. Which action should the data scientist take to improve the model's ability to detect positive cases?

Accepted Answer

Use the F1 score as the evaluation metric and adjust the classification threshold based on the precision-recall curve.. In a severely imbalanced dataset (95% negative, 5% positive), accuracy is misleading. The F1 score balances precision and recall, and adjusting the classification threshold based on the precision-recall curve allows the model to prioritize recall for the minority class, directly improving detection of positive cases. This approach is recommended in SageMaker when using built-in algorithms or custom models with imbalanced data.

Answer

Switch to a logistic regression model with balanced class weights.

Answer

Use accuracy as the evaluation metric and retrain the model.

Answer

Apply SMOTE (Synthetic Minority Over-sampling Technique) to the training data.

Question 14

A company deploys a model using SageMaker real-time endpoint with auto scaling. They observe that during a traffic spike, the endpoint quickly scales up to 10 instances, but after the spike, it takes a long time to scale down, leading to high costs. The scaling policy is based on a simple average CPU utilization threshold. Which adjustment would optimize the scaling down behavior?

Accepted Answer

Decrease the scale-in cooldown period to allow the endpoint to scale down faster when utilization drops.. Decreasing the scale-in cooldown period allows the endpoint to respond more quickly to sustained drops in CPU utilization. By default, SageMaker auto scaling uses cooldown periods to prevent rapid fluctuations; a long scale-in cooldown delays the termination of instances after utilization falls, keeping costs high. Reducing this cooldown lets the endpoint scale down faster when the spike subsides, directly addressing the problem.

Answer

Increase the scale-in cooldown period to prevent premature scale-down.

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Use a step scaling policy with a larger step adjustment for scale-in.

Answer

Change the scaling policy to use memory utilization instead of CPU.

Question 15

Refer to the exhibit. A user is unable to invoke a SageMaker endpoint. The IAM policy shown is attached to the user. Which permission is missing to allow invocation?

Accepted Answer

sagemaker:InvokeEndpoint. To invoke a SageMaker endpoint, the user needs the `sagemaker:InvokeEndpoint` permission. The IAM policy shown lacks this action, which is required for making real-time inference requests to the endpoint. Without it, any attempt to call the endpoint via the SDK or CLI will fail with an access denied error.

Answer

sagemaker:DescribeEndpoint

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sagemaker:CreateEndpoint

Answer

sagemaker:ListEndpoints