MLA-C01 Practice Test 14 — 15 Questions

Question 1

A team is evaluating classification models for a medical diagnosis application. The cost of a false negative is much higher than the cost of a false positive. Which metric should be optimized during model selection?

Accepted Answer

Recall. Option B is correct because recall (true positive rate) focuses on minimizing false negatives, which is the priority when a missed diagnosis is costly. Option A (precision) minimizes false positives. Option C (accuracy) treats all errors equally. Option D (F1 score) balances precision and recall but does not emphasize recall over precision.

Answer

Accuracy

Answer

F1 score

Answer

Precision

Question 2

A machine learning engineer is using SageMaker's HyperparameterTuningJob to optimize a neural network. The engineer observes that the tuning job is taking too long. Which three actions can reduce the tuning time? (Choose three.)

Accepted Answer

Use early stopping to prune poorly performing training jobs. Options A, B, and C are correct. Reducing the search space (A) decreases the number of configurations to try. Early stopping (B) terminates poorly performing trials early. Increasing concurrent jobs (C) runs multiple trials in parallel. Option D (smaller instance) may slow each trial, increasing total time. Option E (warm start) does not reduce the time of the current tuning job.

Answer

Use a warm start from a previous tuning job

Answer

Switch to a smaller instance type for each training job

Question 3

A machine learning engineer is using SageMaker to train a model with the built-in LightGBM algorithm. The engineer wants to use early stopping to prevent overfitting. The training job is configured with a validation dataset. Which hyperparameter should be set to enable early stopping?

Accepted Answer

early_stopping_rounds. Option B is correct because the SageMaker LightGBM implementation uses the early_stopping_rounds hyperparameter to specify the number of consecutive rounds without improvement before stopping. Option A (num_iterations) sets the maximum number of rounds. Option C (num_boost_round) is an alias for the number of boosting rounds. Option D (early_stopping) is not a valid hyperparameter name.

Answer

num_iterations

Answer

early_stopping

Answer

num_boost_round

Question 4

A data scientist has trained a model that achieves 95% accuracy on the training set but only 70% on the test set. Which of the following is the most likely cause?

Accepted Answer

Overfitting. Option C is correct because a large gap between training and test accuracy indicates overfitting, where the model memorizes the training data but fails to generalize. Option A (underfitting) would show low accuracy on both. Option B (data leakage) could cause high accuracy on both if leak is consistent. Option D (convergence to local minimum) is a training issue but does not directly explain the gap.

Answer

Data leakage

Answer

Convergence to local minimum

Answer

Underfitting

Question 5

A data engineer is preparing a dataset for training a regression model. The dataset contains numerical features with missing values. Which two methods are appropriate for handling missing values? (Choose two.)

Accepted Answer

Replace missing values with the mean of the feature. Options B and E are correct. Imputing missing values with the feature mean (B) is a common and straightforward technique. Using a model that supports missing values natively (E), like XGBoost or LightGBM, can handle missing data without explicit imputation. Option A (removing rows) may discard valuable data. Option C (constant imputation) can introduce bias. Option D (one-hot encoding) is for categorical features.

Answer

Perform one-hot encoding on the feature

Answer

Replace missing values with a constant, such as -999

Answer

Remove all rows with missing values

Question 6

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 captures real-time training metrics and provides alerts, making it ideal for monitoring progress. Other features serve different purposes.

Answer

SageMaker Ground Truth

Answer

SageMaker Model Monitor

Answer

SageMaker Experiments

Question 7

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 consistent low latency and is cost-effective for a single large model. Other options either do not meet latency requirements or are designed for multiple models.

Answer

Multi-model endpoint

Answer

Serverless inference

Answer

Batch transform

Question 8

A data scientist needs to store training data in Amazon S3 and wants to optimize read performance for iterative training jobs. Which S3 feature should they use?

Accepted Answer

S3 Byte-Range Fetches. S3 Byte-Range Fetches enable parallel reads of parts of objects, improving read performance for large files during training. Other options are for speed, archival, or querying.

Answer

S3 Transfer Acceleration

Answer

S3 Glacier

Answer

S3 Select

Question 9

Refer to the exhibit. The training job failed. What is the MOST likely cause?

Accepted Answer

The training data size exceeds the available EBS volume size. The log indicates no usable scratch space, meaning the default EBS volume is full. The retry with local SSD suggests that the instance might not have local SSD, or it also fails. The most likely cause is that the training data or intermediate files exceed the available EBS volume size.

Answer

The learning rate is too high

Answer

The instance type does not have SSD storage

Answer

The instance type does not have enough memory

Answer

The number of epochs is too low

Question 10

Refer to the exhibit. The data scientist wants to update the endpoint to use a new model version without downtime. Which approach should they use?

Accepted Answer

Create a new endpoint configuration with a second variant and update the endpoint. To update an endpoint without downtime, create a new endpoint configuration that includes both the old and new model variants, then update the endpoint to use that configuration. The endpoint gradually shifts traffic to the new variant. Deleting the endpoint causes downtime, direct model name update is not allowed, and Model Monitor does not handle deployments.

Answer

Delete the existing endpoint and create a new one

Answer

Update the endpoint's model name directly

Answer

Use SageMaker Model Monitor to automatically switch

Question 11

Refer to the exhibit. A user launches a SageMaker notebook instance with this lifecycle configuration. What happens?

Accepted Answer

The script runs every time the notebook is started. SageMaker lifecycle configurations run every time the notebook instance is started (including after stop and start). They do not run only on first start or after kernel resets.

Answer

The script runs after each kernel reset

Answer

The script runs only on the first start

Answer

The script runs only when creating the instance

Question 12

A company wants to forecast monthly sales that show clear seasonality. Which algorithm is most suitable?

Accepted Answer

ARIMA (Seasonal ARIMA). ARIMA (specifically SARIMA for seasonality) is designed for univariate time series forecasting with seasonal patterns. Linear regression may capture trends but not seasonality, random forest can be used but not optimal for time series, K-means is clustering.

Answer

Random forest

Answer

K-means clustering

Answer

Linear regression

Question 13

A data scientist is training a large transformer model using SageMaker's model parallelism library. The training job is failing with an out-of-memory (OOM) error. Which two actions can help resolve the OOM error? (Choose two.)

Accepted Answer

Reduce the sequence length. Options C and E are correct. Activation checkpointing (C) trades compute for memory by recomputing activations during backpropagation rather than storing them. Reducing sequence length (E) directly decreases memory usage for attention layers. Option A (decrease pipeline parallelism degree) can increase per-stage memory. Option B (increase batch size) increases memory. Option D (smaller instance) reduces available memory, worsening OOM.

Answer

Increase the batch size per GPU

Answer

Switch to a smaller instance type

Answer

Decrease the pipeline parallelism degree

Question 14

A company uses SageMaker to train a model. The training job is failing with an error "ResourceLimitExceeded". What is the most likely cause?

Accepted Answer

The account has reached the limit for number of training instances. The error indicates that a service limit has been reached, commonly the number of concurrent training instances. Other options would produce different error messages.

Answer

The model artifact is too large to upload

Answer

Invalid hyperparameters

Answer

The training data size exceeds the available instance storage

Question 15

A machine learning engineer trains a binary classifier and obtains an accuracy of 95% on the test set. The dataset is imbalanced with 95% positive class. What is the most important metric to evaluate the model's performance?

Accepted Answer

F1 score. Accuracy is misleading on imbalanced datasets because a model that always predicts the majority class achieves high accuracy. F1 score balances precision and recall, providing a more reliable measure.

Answer

R-squared

Answer

Accuracy

Answer

RMSE