AWS Certified Machine Learning Engineer Associate MLA-C01 practice test

A company is running a SageMaker endpoint serving multiple models. They need to monitor for data drift and model quality. Which THREE actions are necessary? (Choose three.)

Trap 1: Deploy a shadow endpoint for comparison

Shadow endpoint is for traffic shifting, not monitoring drift.

Trap 2: Use SageMaker Debugger for monitoring

Debugger is for training debugging, not production monitoring.

A
Deploy a shadow endpoint for comparison
Why wrong: Shadow endpoint is for traffic shifting, not monitoring drift.
B
Enable data capture on the endpoint
Data capture logs inference requests for monitoring.
C
Use SageMaker Debugger for monitoring
Why wrong: Debugger is for training debugging, not production monitoring.
D
Create a SageMaker Model Monitor schedule
Schedule defines how often to run monitoring jobs.
E
Configure baseline constraints from training data
Baseline constraints define expected statistical properties for drift detection.

Question 2hardmultiple choice

A data scientist trained a logistic regression model on a dataset with 100 features. After training, the training accuracy is 0.99 but validation accuracy is 0.75. Which action is MOST likely to reduce overfitting?

Trap 1: Increase the number of features

Adding more features increases model complexity and can worsen overfitting.

Trap 2: Use a more complex model like XGBoost

More complex models are more prone to overfitting, not less.

Trap 3: Use stratified cross-validation

Stratified CV helps evaluate generalization but does not directly reduce overfitting.

A
Increase the number of features
Why wrong: Adding more features increases model complexity and can worsen overfitting.
B
Increase the regularization strength
Stronger regularization (e.g., higher L2 penalty) shrinks coefficients and reduces overfitting.
C
Use a more complex model like XGBoost
Why wrong: More complex models are more prone to overfitting, not less.
D
Use stratified cross-validation
Why wrong: Stratified CV helps evaluate generalization but does not directly reduce overfitting.

Question 3mediummulti select

A team is training a deep learning model on Amazon SageMaker using a custom Docker container. Which three practices should they follow to optimize training performance? (Choose three.)

Trap 1: Use the largest instance type available

Largest instance types increase cost and may not yield proportional performance gains; optimization should consider cost-efficiency.

Trap 2: Increase the number of layers in the model to improve accuracy

Deeper models may improve accuracy but also increase training time and risk overfitting; this is not a general performance optimization practice.

A
Store training data in Amazon S3 in a shuffled and compressed format
Shuffling prevents bias and compression reduces transfer time, improving training performance.
B
Use the largest instance type available
Why wrong: Largest instance types increase cost and may not yield proportional performance gains; optimization should consider cost-efficiency.
C
Increase the number of layers in the model to improve accuracy
Why wrong: Deeper models may improve accuracy but also increase training time and risk overfitting; this is not a general performance optimization practice.
D
Use SageMaker Managed Spot Training with checkpointing
Spot instances are cheaper, and checkpointing allows resuming after interruptions, providing both cost savings and reliability.
E
Use Pipe mode to stream data instead of File mode
Pipe mode streams data directly from S3 to the training container, reducing disk usage and I/O wait.

Question 4mediummultiple choice

A company is using SageMaker to train a neural network for image classification. The training job is taking too long. The team wants to reduce training time without sacrificing model accuracy. Which approach should they recommend?

Trap 1: Increase the batch size to the maximum possible

Very large batch sizes can degrade model accuracy and may not fit in memory.

Trap 2: Use a learning rate scheduler that reduces the learning rate over…

Schedulers help convergence but do not directly reduce training time.

Trap 3: Add more convolutional layers to the model

Adding layers increases computation, slowing training.

A
Increase the batch size to the maximum possible
Why wrong: Very large batch sizes can degrade model accuracy and may not fit in memory.
B
Use a GPU-based instance such as ml.p3.2xlarge
GPUs accelerate matrix operations in neural networks, reducing training time.
C
Use a learning rate scheduler that reduces the learning rate over time
Why wrong: Schedulers help convergence but do not directly reduce training time.
D
Add more convolutional layers to the model
Why wrong: Adding layers increases computation, slowing training.

Question 5easymultiple choice

A team is developing a model to predict customer churn. The dataset has 10,000 samples with 20 features. The target variable is binary with 15% churn rate. The team wants to use logistic regression. Which data preprocessing step is MOST important to ensure proper convergence?

Trap 1: Remove correlated features to reduce multicollinearity

Multicollinearity affects interpretability but not necessarily convergence of logistic regression.

Trap 2: Impute missing values with the median

Missing value imputation is important but not the most critical for convergence.

Trap 3: Apply SMOTE to balance the classes

SMOTE addresses class imbalance but does not affect convergence of logistic regression.

A
Remove correlated features to reduce multicollinearity
Why wrong: Multicollinearity affects interpretability but not necessarily convergence of logistic regression.
B
Impute missing values with the median
Why wrong: Missing value imputation is important but not the most critical for convergence.
C
Apply SMOTE to balance the classes
Why wrong: SMOTE addresses class imbalance but does not affect convergence of logistic regression.
D
Standardize the features to have zero mean and unit variance
Standardization ensures gradient descent converges faster and avoids dominance by large-scale features.

Question 6hardmultiple choice

A data engineer is processing a large dataset in Amazon S3 with AWS Glue ETL. The dataset contains timestamps in multiple time zones. The engineer needs to create a feature for hour-of-day consistent across all records. Which approach ensures correctness?

Trap 1: Convert all timestamps to UTC in the ETL script using Spark's…

from_utc_timestamp converts from UTC, not to UTC; the function name is misleading and can cause incorrect conversions.

Trap 2: Use AWS Glue's built-in transform to parse timestamps with timezone…

While Glue can parse timestamps, it does not automatically normalize to a common timezone for consistent hour extraction.

Trap 3: Use Python's datetime.strptime with tzlocal

tzlocal uses the system time zone, which is not reliable for multiple time zones.

A
Convert all timestamps to UTC in the ETL script using Spark's from_utc_timestamp
Why wrong: from_utc_timestamp converts from UTC, not to UTC; the function name is misleading and can cause incorrect conversions.
B
Use AWS Glue's built-in transform to parse timestamps with timezone offsets
Why wrong: While Glue can parse timestamps, it does not automatically normalize to a common timezone for consistent hour extraction.
C
Use Python's datetime.strptime with tzlocal
Why wrong: tzlocal uses the system time zone, which is not reliable for multiple time zones.
D
Convert all timestamps to UTC during the ETL process, then extract hour
Normalizing to UTC before extracting hour guarantees consistency across time zones.