MLA-C01 Practice Test 36 — 15 Questions

Question 1

A data scientist is using SageMaker Experiments to track multiple training runs for a PyTorch model. They want to compare metrics across runs and identify the best hyperparameters. Which TWO capabilities should they use? (Choose TWO.)

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

Accepted Answer

SageMaker Experiments list and search API to query runs by metric. Option A is correct because the SageMaker Experiments list and search API (e.g., search() with filters on metric values) lets the data scientist query and compare runs across an experiment by metric, which is exactly what is needed to rank runs and identify the best hyperparameters. Option B is correct because the SageMaker SDK's experiment logging capabilities (Run.log_metric, log_parameter, and log_artifact) record the metrics and hyperparameters for each training run so they can later be analyzed and compared. Option C (SageMaker Autopilot) is an automated machine learning service that builds and tunes models automatically, not a tool for comparing metrics across existing runs. Option D (SageMaker Clarify) provides bias detection and explainability, and Option E (SageMaker Model Monitor) detects drift in deployed models; neither supports run-to-run metric comparison or hyperparameter selection.

Answer

SageMaker Autopilot

Answer

SageMaker Clarify

Answer

SageMaker Model Monitor

Question 2

A company needs to update a model in production without any downtime. They currently have a single real-time endpoint serving traffic. Which approach allows them to deploy a new model version and switch traffic gradually while being able to roll back quickly?

Accepted Answer

Use a canary deployment by creating a new production variant with the new model and shifting traffic incrementally. A canary deployment creates a new production variant on the existing endpoint with the new model and shifts a small percentage of traffic to it, allowing gradual validation and quick rollback by shifting traffic back to the original variant. This avoids downtime and provides controlled risk.

Answer

Use a multi-model endpoint and replace the model file

Answer

Stop the endpoint, update the model, and restart the endpoint

Answer

Update the existing endpoint's model directly using UpdateEndpoint

Question 3

A data scientist is training a large model on SageMaker and wants to reduce training time by using multiple GPUs. The model is small enough to fit on a single GPU but training is slow. Which SageMaker feature should be used?

Accepted Answer

Data parallelism using SageMaker's Distributed Data Parallel. SageMaker's Distributed Data Parallel (DDP) is the correct choice because it splits the mini-batch across multiple GPUs, allowing each GPU to hold a copy of the model and process a subset of the data simultaneously. This reduces training time for models that fit on a single GPU by leveraging data parallelism, where gradients are synchronized across GPUs after each step.

Answer

Use a larger instance with more vCPUs

Answer

Model parallelism using SageMaker's Model Parallel

Answer

Use Elastic Inference

Question 4

A machine learning engineer is building a pipeline to preprocess text data for a sentiment analysis model. The data consists of customer reviews. The engineer wants to convert the text into numerical features while preserving the semantic meaning of words. Which technique should be used?

Accepted Answer

Word embeddings (e.g., Word2Vec or GloVe). Word embeddings (like Word2Vec or GloVe) are dense vector representations that capture semantic relationships between words based on their context in a large corpus. For sentiment analysis, preserving semantic meaning (e.g., 'good' and 'excellent' having similar vectors) is critical, and embeddings directly encode this, unlike sparse or count-based methods.

Answer

One-hot encoding of each word

Answer

Bag-of-words with TF-IDF

Answer

Hashing vectorizer

Question 5

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. The lifecycle configuration script is set to run 'on-start', which means it executes every time the SageMaker notebook instance transitions to the 'InService' state, including initial creation and subsequent starts. This is distinct from 'on-create', which runs only once during instance provisioning. Therefore, the script runs on every start, making option A correct.

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 6

A machine learning engineer wants to reduce training costs by using excess EC2 capacity. Which instance purchasing option should they choose for SageMaker training jobs?

Accepted Answer

Spot Instances. Spot Instances use AWS's spare EC2 capacity and are discounted up to 90% versus On-Demand, making them the correct choice when the workload is interruptible. SageMaker training jobs support Managed Spot Training, which automatically checkpoints and resumes jobs when capacity is reclaimed, so the engineer gets the cost savings without losing training progress. Reserved Instances and Dedicated Instances do not offer the same spot-level discount for interruptible training.

Answer

Reserved Instances

Answer

On-Demand Instances

Answer

Dedicated Instances

Question 7

A financial services company must ensure that all data used by Amazon SageMaker training jobs is encrypted at rest. The company wants to use a customer-managed key (CMK) for the encryption. Which steps are necessary to achieve this? (Choose TWO.)

Accepted Answer

Create a CMK in AWS KMS and add the SageMaker service principal to the key policy to allow it to use the key.. Option B is correct because a customer-managed key must exist in AWS KMS before it can be used, and its key policy must grant the SageMaker service principal (sagemaker.amazonaws.com) permissions such as kms:Decrypt, kms:GenerateDataKey, and kms:CreateGrant so the service can encrypt and decrypt the training job's attached EBS storage volumes. Option D is correct because, when launching a training job, you pass the CMK's ARN via the VolumeKmsKeyId parameter (in the CreateTrainingJob request or the ResourceConfig of the estimator) to encrypt the ML storage volume attached to the training instances at rest. Option A is incorrect because there is no EnableDefaultEncryption flag for SageMaker training jobs; encryption must be specified explicitly per job. Option C is incorrect because S3 default encryption protects objects in the bucket, not the training job's attached storage volumes, and does not by itself satisfy the requirement to encrypt the training job's data at rest with a CMK. Option E is incorrect because CloudWatch Logs encryption protects log data, not the training data or the training job's storage volumes.

Answer

Enable SageMaker's default encryption for the training job by setting the EnableDefaultEncryption flag.

Answer

Enable S3 default encryption using the CMK on all buckets containing training data.

Answer

Use CloudWatch Logs encryption to protect the training logs.

Question 8

An ML team uses SageMaker to deploy a model for real-time inference. They want to monitor and improve cost efficiency. Which THREE actions should they take? (Select THREE.)

Accepted Answer

Use SageMaker Inference Recommender to find the optimal instance type and count. Option A is correct because SageMaker Inference Recommender runs load tests and benchmarks to recommend the optimal instance type and instance count for a real-time endpoint, directly improving cost efficiency by avoiding over-provisioning. Option B is correct because configuring auto-scaling on a SageMaker endpoint adjusts the number of instances to match traffic demand, so the team pays only for capacity actually needed during peaks and troughs. Option E is correct because SageMaker Savings Plans offer discounted pricing (up to 64% off) in exchange for a committed hourly spend, reducing the cost of steady-state real-time inference workloads. Option C is not a cost-efficiency action; a CloudWatch dashboard for latency is an observability tool and does not by itself reduce spend. Option D is incorrect because Managed Spot Training applies to training jobs, not to real-time inference endpoint instances, which cannot use spot capacity for persistent endpoints.

Answer

Create a CloudWatch dashboard to monitor endpoint latency

Answer

Use SageMaker Managed Spot Training for endpoint instances

Question 9

Which SageMaker feature compiles a trained model into an optimized binary for a specific hardware target (e.g., Intel, ARM, NVIDIA, or edge devices) to improve inference performance?

Accepted Answer

SageMaker Neo. SageMaker Neo is a model compilation service that optimizes models for specific hardware targets. Amazon Elastic Inference attaches GPU acceleration to endpoints, but does not compile models. Model Monitor monitors quality. SageMaker Clarify explains predictions.

Answer

SageMaker Model Monitor

Answer

Amazon Elastic Inference

Answer

SageMaker Clarify

Question 10

A machine learning team deploys a fraud detection model on a SageMaker endpoint. The model's predictions are used in real-time. The team wants to monitor for data drift by comparing incoming data distributions against a baseline created from the training data. Which SageMaker capability should they use?

Accepted Answer

SageMaker Model Monitor - Data Quality Monitor. SageMaker Model Monitor's Data Quality Monitor is specifically designed to detect data drift by comparing the statistical distribution of incoming inference data against a baseline computed from the training dataset. This capability tracks metrics like mean, variance, and quantiles for each feature, alerting when significant deviations occur. For a fraud detection model requiring real-time monitoring of input distributions, this is the correct choice.

Answer

SageMaker Model Monitor - Model Quality Monitor

Answer

SageMaker Model Monitor - Feature Attribution Drift Monitor

Answer

SageMaker Model Monitor - Bias Drift Monitor

Question 11

A company deploys a model for fraud detection. They want to monitor if the model's predictions become less accurate over time due to changes in the underlying data distribution, but they do not have immediate access to ground truth labels. Which type of drift should they monitor as a proxy?

Accepted Answer

Data drift. Data drift (option C) is the correct proxy to monitor when ground truth labels are unavailable because it detects changes in the input feature distribution over time. If the underlying data distribution shifts, the model's predictions are likely to become less accurate even if the relationship between features and labels remains stable. This allows teams to trigger retraining or investigation before model quality degrades.

Answer

Feature attribution drift

Answer

Model quality drift

Answer

Concept drift

Question 12

A company needs to serve real-time predictions from a large ensemble of three deep learning models, each requiring different inference environments (PyTorch, TensorFlow, MXNet). Which SageMaker endpoint type supports running multiple inference containers together?

Accepted Answer

Multi-container endpoint. Amazon SageMaker multi-container endpoints allow you to run multiple inference containers (e.g., PyTorch, TensorFlow, MXNet) within a single endpoint, each handling different models or inference environments. This is achieved by deploying multiple containers behind a single endpoint with a serial or direct invocation pattern, enabling real-time predictions from the ensemble without managing separate endpoints.

Answer

Multi-model endpoint

Answer

Real-time endpoint with a single container

Answer

Asynchronous endpoint

Question 13

A machine learning engineer is preparing a dataset for a binary classification model. The dataset has 10,000 rows and 200 features, with 5% positive class. The engineer suspects class imbalance may affect model performance. Which TWO actions should the engineer take to mitigate imbalance? (Choose 2.)

Accepted Answer

Apply SMOTE only to training data. Option D is correct because SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples of the minority class and must be applied only to the training data to avoid data leakage into validation/test folds, directly addressing the 5% positive-class imbalance. Option E is correct because setting class weights in the algorithm (e.g., class_weight='balanced' in scikit-learn) penalizes misclassification of the minority class more heavily, which mitigates imbalance without altering the dataset. Option A is incorrect because PCA is a dimensionality-reduction technique for the 200 features and does nothing to change the 5% class ratio. Option B is incorrect because removing low-variance features is a feature-selection step unrelated to class distribution. Option C is incorrect because k-fold cross-validation is an evaluation/resampling strategy that provides more reliable performance estimates but does not itself rebalance classes.

Answer

Perform PCA to reduce dimensions

Answer

Remove features with low variance

Answer

Use k-fold cross-validation

Question 14

A data scientist discovers that a dataset for binary classification contains 95% negative samples and 5% positive samples. Which technique is MOST appropriate to address the class imbalance?

Accepted Answer

Use SMOTE to generate synthetic samples for the minority class. SMOTE (Synthetic Minority Over-sampling Technique) generates new, synthetic minority-class samples by interpolating between existing minority neighbors, which increases the minority representation without simply duplicating rows. This helps the model learn the minority decision boundary and is the standard, most appropriate technique for a 95/5 imbalance. It preserves all majority information while enriching the minority class.

Answer

Remove all samples from the majority class

Answer

Increase the learning rate of the model

Answer

Downsample the majority class to 5% of the original size

Question 15

A machine learning team is using Amazon SageMaker to train a model. They notice that the training job is taking longer than expected and the logs show repeated warnings about 'loss not decreasing'. Which SageMaker feature should they use to diagnose and visualize the training process?

Accepted Answer

Amazon SageMaker Debugger. Amazon SageMaker Debugger is the correct choice because it provides real-time monitoring and visualization of training metrics, including loss values, gradients, and weights. The repeated 'loss not decreasing' warnings indicate a training issue (e.g., vanishing gradients or learning rate problems), and Debugger can capture these tensors and emit alerts or trigger actions (like stopping the job) via built-in or custom rules. It also integrates with SageMaker Studio for interactive visualization of the training progress.

Answer

Amazon SageMaker Clarify

Answer

Amazon SageMaker Experiments

Answer

Amazon SageMaker Model Monitor