MLA-C01 Practice Test 13 — 15 Questions

Question 1

Which technique is commonly used to handle missing values in a categorical feature?

Accepted Answer

Mode imputation. Mode imputation is the standard technique for handling missing values in categorical features because it replaces missing entries with the most frequent category, preserving the feature's distribution without introducing artificial values. Unlike numerical imputation methods, mode imputation respects the non-numeric nature of categorical data and maintains the integrity of the original categories.

Answer

One-hot encoding

Answer

Mean imputation

Answer

Standard scaling

Question 2

A team is using Amazon SageMaker for feature engineering. They have a dataset with a column 'TransactionDate' in string format (e.g., '2023-01-15 10:30:00'). They need to create features: year, month, day, hour, and day_of_week. What is the most efficient way to do this in a SageMaker processing job?

Accepted Answer

Use pandas datetime functions and then split. Using pandas datetime functions within a SageMaker processing job is the most efficient approach for this task. SageMaker processing jobs run custom Python scripts, and pandas provides vectorized operations (e.g., `pd.to_datetime()`, `.dt.year`, `.dt.month`, `.dt.day`, `.dt.hour`, `.dt.dayofweek`) that parse the string column and extract all required features in a single pass without external dependencies or data movement.

Answer

Use SageMaker built-in first party algorithms

Answer

Use AWS Glue for transformation

Answer

Use SQL query in Athena on S3 data

Question 3

A team uses SageMaker for training. They need to monitor training progress and view metrics like loss and accuracy. Which SageMaker feature should they use?

Accepted Answer

SageMaker Debugger. SageMaker Debugger is the correct feature because it provides real-time monitoring of training metrics such as loss and accuracy, along with the ability to set alerts and capture tensors for debugging. It integrates directly with the SageMaker training loop, allowing users to visualize metrics via the SageMaker Studio UI or retrieve them programmatically without additional infrastructure.

Answer

SageMaker Ground Truth

Answer

SageMaker Model Monitor

Answer

SageMaker Experiments

Question 4

A machine learning model is deployed on SageMaker and its predictions are used in a production application. The model's accuracy has degraded over time. What is the most likely cause?

Accepted Answer

The model experienced concept drift.. Concept drift occurs when the statistical properties of the target variable change over time, causing the model's predictions to become less accurate. In production ML systems on SageMaker, this is a common issue as real-world data distributions evolve, and the model does not automatically adapt without retraining.

Answer

The training data was not shuffled properly.

Answer

The model was not compiled for inference.

Answer

The endpoint instance type is too small.

Question 5

A machine learning team is preparing numerical features for a linear regression model. Feature 'A' ranges from 0 to 1000, feature 'B' ranges from 0 to 1, and feature 'C' ranges from -10000 to 10000. The team wants to ensure that feature scales do not affect the model's coefficients and that the features are bounded between 0 and 1. Which transformation should they apply?

Accepted Answer

MinMaxScaler. MinMaxScaler applies the formula (x - min) / (max - min), which linearly rescales each feature to the [0, 1] range regardless of its original bounds. This directly satisfies both stated requirements: eliminating scale-driven coefficient distortion in linear regression and bounding all features between 0 and 1. It is the only option that guarantees the [0, 1] output range.

Answer

RobustScaler (based on median and IQR)

Answer

Log transformation

Answer

StandardScaler (z-score normalization)

Question 6

A company is building a real-time fraud detection system. They need to store features with historical context for model training and also support low-latency lookups for inference. Which THREE configurations should they set up in Amazon SageMaker Feature Store? (Select THREE.)

Accepted Answer

Enable point-in-time queries to retrieve historical feature values. Point-in-time queries retrieve historical feature values at a specific time. Online store provides low-latency reads for inference. A feature group organizes features; creating one is necessary. Offline store is for batch but not required for low-latency inference.

Answer

Use the BatchGetRecord API for all feature retrieval

Answer

Disable the offline store to reduce costs

Question 7

A data scientist wants to evaluate the performance of a binary classification model. The dataset is highly imbalanced with only 5% positive class. Which metric should be used to evaluate the model?

Accepted Answer

F1-score. In highly imbalanced datasets (e.g., only 5% positive class), accuracy is misleading because a model that predicts the majority class for all instances would achieve 95% accuracy without any predictive power. The F1-score is the harmonic mean of precision and recall, making it robust to class imbalance by balancing false positives and false negatives. It is the standard metric for binary classification on imbalanced data in AWS SageMaker and other ML platforms.

Answer

Accuracy

Answer

Mean Squared Error

Answer

R-squared

Question 8

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 9

A company has a SageMaker endpoint that uses a trained model to classify images. The endpoint is experiencing high latency and the team suspects it is due to the model size. Which action can the team take to reduce latency without significantly impacting accuracy?

Accepted Answer

Use SageMaker Neo to compile the model for the target instance. SageMaker Neo compiles trained models into an optimized binary for the target hardware, applying techniques like operator fusion, memory layout optimization, and quantization. This reduces model size and inference latency while preserving accuracy, making it the correct choice for addressing high latency caused by model size.

Answer

Switch to a compute-optimized instance type

Answer

Reduce the batch size of inference requests

Answer

Convert the model to ONNX format

Question 10

A company deploys a model on SageMaker that serves predictions to a web application. The model's performance degrades over time due to data drift. The company wants to set up continuous monitoring. Which TWO actions should the company take to monitor and retrain the model effectively? (Choose TWO.)

Accepted Answer

Configure an Amazon EventBridge rule to start a retraining pipeline when the Model Monitor detects violations.. Option C is correct because SageMaker Model Monitor is the purpose-built service for continuous monitoring of a deployed endpoint: it captures inference data (data capture) and runs monitoring schedules that compare incoming data against a baseline to detect data drift and other violations. Option B is correct because Model Monitor emits CloudWatch metrics and events, so an EventBridge rule can match those violation events and automatically trigger a retraining pipeline, closing the loop from detection to remediation. Option A is not appropriate because manual monthly review is not continuous and cannot reliably catch drift in time. Option D is insufficient because CloudWatch Logs Insights only queries logs for anomalies; it does not compute drift against a baseline or trigger retraining. Option E is unrelated because A/B testing compares model variants, not drift detection or retraining.

Answer

Manually review model performance monthly and retrain if necessary.

Answer

Use Amazon CloudWatch Logs Insights to query inference logs for anomalies.

Answer

Deploy the model on multiple endpoints with A/B testing to compare performance.

Question 11

A machine learning team uses SageMaker Pipelines to automate retraining. They want to avoid re-running data processing steps if the data has not changed since the last successful pipeline run. Which built-in feature should they enable?

Accepted Answer

Pipeline caching. Pipeline caching is the correct choice because SageMaker Pipelines can cache the outputs of each step based on a hash of the step's input parameters, configuration, and code. If the hash matches a previous successful run, the cached output is reused, avoiding redundant execution of data processing steps when the underlying data hasn't changed.

Answer

Model lineage tracking

Answer

Parameterized pipeline executions

Answer

Step parallelism

Question 12

A team wants to use a custom PyTorch training script in SageMaker. They need to install additional Python packages not included in the base PyTorch container. Which approach should they take?

Accepted Answer

Use the SageMaker PyTorch estimator with a requirements.txt file. The SageMaker PyTorch estimator supports a 'requirements.txt' file in the source directory (specified via 'source_dir'), which SageMaker automatically installs into the training container before the script runs. This is the simplest, AWS-recommended way to add Python packages without building a custom image. It preserves the managed PyTorch container's optimizations while adding only the extra dependencies.

Answer

Use SageMaker Script Mode with a custom Dockerfile

Answer

Build a custom container with Docker

Answer

Install packages using a lifecycle configuration

Question 13

A data scientist is using SageMaker Experiments to track multiple training runs. They want to compare the F1 scores across runs. Which component should they use to log the F1 score?

Accepted Answer

Metric. In SageMaker Experiments, metrics are logged using the SageMaker SDK's log_metric method or by reporting through the training job's metric definitions. Hyperparameters are logged separately. Artifacts are for model files or datasets.

Answer

Parameter

Answer

Hyperparameter

Answer

Artifact

Question 14

A machine learning engineer runs a training job and notices the loss is NaN after a few steps. Which SageMaker Debugger rule can help identify this issue?

Accepted Answer

Exploding gradients. The SageMaker Debugger built-in 'ExplodingGradients' rule monitors gradient tensors during training and detects when gradient values grow excessively large, which is a classic cause of NaN loss values. When gradients explode, weight updates become enormous, causing numerical overflow and NaN in the loss. The ExplodingGradients rule flags this condition so the engineer can apply gradient clipping or reduce the learning rate.

Answer

Overfit

Answer

Dead ReLU

Answer

Class imbalance

Question 15

A company needs to detect bias in a pre-trained model before deployment. They want to compute metrics like disparate impact and equal opportunity difference. Which AWS service should they use?

Accepted Answer

SageMaker Clarify. SageMaker Clarify is purpose-built to detect bias in datasets and models, computing pre-training and post-training bias metrics such as disparate impact (DI) and equal opportunity difference (EOD). It integrates with SageMaker training and hosting, and can also generate explainability reports using SHAP values. Because the question asks specifically for bias metrics like DI and EOD, Clarify is the correct service.

Answer

Amazon Rekognition

Answer

SageMaker Model Monitor

Answer

SageMaker Debugger