MLA-C01 Practice Test 22 — 15 Questions

Question 1

After deploying a model to a SageMaker endpoint, the operations team notices high inference latency. They suspect it is due to insufficient instance capacity. Which first step should they take to diagnose the issue?

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

Accepted Answer

Review Amazon CloudWatch metrics for the endpoint, such as CPUUtilization and Invocations.. Amazon CloudWatch metrics for a SageMaker endpoint, such as `CPUUtilization`, `MemoryUtilization`, and `Invocations`, directly indicate whether the instance is overloaded. High `CPUUtilization` combined with a high `Invocations` count and increased latency strongly suggests insufficient instance capacity. This is the standard first diagnostic step for capacity-related performance issues.

Answer

Check AWS CloudTrail logs for API errors.

Answer

Use Amazon SageMaker Debugger to analyze inference performance.

Answer

Retrain the model with more training data.

Question 2

A company wants to reduce costs for a real-time inference endpoint that experiences predictable traffic spikes during business hours and low traffic at night. Which auto-scaling policy is MOST cost-effective while maintaining performance?

Accepted Answer

Scheduled scaling that increases instances before business hours and decreases after. Scheduled scaling directly aligns capacity with the predictable traffic pattern (business hours vs. night), allowing you to proactively add instances before demand increases and remove them afterward. This avoids the cost of over-provisioning during low-traffic periods and the latency of reactive scaling, making it the most cost-effective approach for a known, recurring schedule.

Answer

Step scaling based on CPU utilization

Answer

Manual scaling by the operations team

Answer

Target tracking with a custom metric for response time

Question 3

A team uses SageMaker Clarify to monitor bias drift in production. They schedule weekly analysis. After a month, Clarify reports a significant increase in a bias metric. What should the team do first?

Accepted Answer

Review the analysis report to understand which feature and segment contributed to the drift.. When SageMaker Clarify reports a significant increase in a bias metric, the first step is to review the analysis report to identify which feature and segment drove the drift. This diagnostic step informs whether the drift is due to data distribution shift, a specific subgroup, or a false positive. Only after understanding the cause should the team consider retraining or adjusting monitoring.

Answer

Disable the bias monitor because the metric may be noisy.

Answer

Immediately retrain the model with a balanced dataset.

Answer

Increase the frequency of analysis to daily.

Question 4

A data scientist is using SageMaker Automatic Model Tuning to optimize hyperparameters for an XGBoost model. They want to maximize AUC. Which search strategy is MOST appropriate for efficient exploration?

Accepted Answer

Bayesian optimization. Bayesian optimization is the most appropriate search strategy for efficiently exploring hyperparameter space because it builds a probabilistic surrogate model of the objective function (AUC) and uses an acquisition function to intelligently select the next hyperparameter combination to evaluate. This makes it far more sample-efficient than grid or random search, which is critical when each training run is expensive. SageMaker Automatic Model Tuning supports Bayesian optimization as its default and recommended strategy.

Answer

Random search

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Grid search

Answer

Hyperband

Question 5

A retail company is building a machine learning model to predict customer churn. The data engineering team has extracted customer transaction data from Amazon Aurora and stored it as CSV files in Amazon S3. The data includes customer IDs, transaction amounts, timestamps, and product categories. A data scientist discovers that the dataset contains several missing values in the 'transaction_amount' column for about 15% of the records. The data scientist also notices that the 'customer_id' column has some duplicate entries. The team wants to prepare the data for training a churn model using Amazon SageMaker. The data is approximately 50 GB in size. What should the data scientist do to handle the missing values and duplicates efficiently while preparing the data for training?

Accepted Answer

Use an AWS Glue ETL job to read the data from S3, apply transformations to fill missing values with the mean or median, and drop duplicate customer IDs, then write the cleaned data back to S3.. AWS Glue ETL jobs are serverless and designed to handle large-scale data transformations (like 50 GB) without requiring manual cluster management. Glue can read CSV files from S3, apply transformations to impute missing values with the mean or median, drop duplicate customer IDs, and write the cleaned data back to S3, all while scaling automatically to handle the data volume efficiently.

Answer

Use a SageMaker notebook instance with Pandas to load the entire dataset into memory, fill missing values with the median, and drop duplicate customer IDs.

Answer

Drop all records with missing values in the transaction_amount column and remove duplicate customer IDs using an Athena SQL query, then store the result in S3.

Answer

Use an Amazon EMR cluster with Spark to read the CSV files, impute missing transaction amounts with the mean or median, and remove duplicate customers.

Question 6

A company wants to use SageMaker Autopilot for a regression problem. They require an explainability report that shows feature importance globally. Which Autopilot feature should they enable?

Accepted Answer

Explainability report generation. SageMaker Autopilot's explainability report generation feature produces SHAP-based feature importance values that quantify each input feature's global contribution to model predictions. Enabling it during the AutoML job causes Autopilot to emit a model explainability report alongside the best candidate, satisfying the requirement for a global feature-importance view. The other options relate to candidate exploration, model combination, or tuning, none of which produce explainability artifacts.

Answer

AutoML candidate generation

Answer

Ensembling mode

Answer

Hyperparameter optimization

Question 7

A company wants to track the lineage of their ML models for reproducibility and auditability. Which THREE services or features should they use together to achieve this? (Choose THREE.)

Accepted Answer

Amazon S3 versioning. Amazon S3 versioning (A) is correct because it preserves every version of the datasets and model artifacts stored in S3, so a given training run can be tied to the exact immutable object version used, which is essential for reproducibility and auditability. SageMaker Experiments (B) is correct because it records experiment runs, trial components, parameters, metrics, and input/output artifacts, giving the structured record of each training attempt needed to reproduce results. SageMaker ML Lineage Tracking (D) is correct because it automatically creates and stores entities and relationships (trials, trial components, artifacts, contexts, actions) forming a queryable lineage graph from data through training to the deployed model. AWS CloudTrail (C) only logs API activity and control-plane events for auditing who did what, not the data/model lineage relationships, so it does not by itself provide reproducibility lineage. AWS Config (E) evaluates and records resource configuration compliance over time, which is unrelated to tracking ML artifact provenance and experiment history.

Answer

AWS CloudTrail

Answer

AWS Config

Question 8

An ML engineer is fine-tuning a foundation model using RLHF on SageMaker. Which THREE components are essential for this workflow? (Select THREE.)

Accepted Answer

A reward model trained on the preference data. Option A is correct because RLHF requires a reward model that has been trained on human preference data to serve as the scalar reward signal guiding policy optimization. Option C is correct because PPO (Proximal Policy Optimization) is the standard reinforcement learning algorithm used to update the policy (the language model) against the reward model while constraining updates via a KL penalty to the reference model. Option D is correct because a preference dataset containing human rankings (e.g., chosen vs. rejected responses) is the foundational input used to train the reward model in the first place. Option B is not essential to the RLHF training workflow itself; a validation set is useful for evaluation but is not a required RLHF component. Option E is not essential because PEFT methods like LoRA are an optional efficiency technique for fine-tuning, not a required element of the RLHF pipeline.

Answer

A large validation dataset for final evaluation

Answer

A PEFT technique like LoRA

Question 9

A data engineer is using AWS Glue to run an ETL job that joins two large datasets and writes the output to S3 for ML training. The job is failing due to out-of-memory errors. Which THREE actions can help resolve this issue? (Select THREE.)

Accepted Answer

Filter unnecessary records early in the transformation. Option A is correct because filtering unnecessary records early in the transformation reduces the volume of data shuffled and held in memory during the join, directly lowering memory pressure on the Spark executors. Option B is correct because increasing the number of DPUs for the Glue job adds more workers and memory capacity, allowing Spark to distribute the join workload across more executors and avoid out-of-memory failures. Option C is correct because partitioning the input data on the join keys co-locates matching keys within the same partition, minimizing the shuffle and memory footprint required to perform the join. Option D is not appropriate because a Python shell job runs single-node, non-distributed Python and cannot handle large-scale joins across two large datasets. Option E is not appropriate because using a smaller worker type reduces the memory and compute resources available per worker, which would worsen the out-of-memory condition.

Answer

Switch from Spark to Python shell

Answer

Use a smaller worker type

Question 10

A data scientist wants to use SageMaker Autopilot to automatically build a regression model. The dataset contains 200 features and 50,000 rows. Which output does SageMaker Autopilot provide?

Accepted Answer

A leaderboard of candidate models with metrics and explainability reports. SageMaker Autopilot automatically explores different algorithms and hyperparameter combinations to find the best model for a given dataset. It provides a leaderboard of candidate models, each with performance metrics, and generates explainability reports that show how features influence predictions. This allows data scientists to understand and select the most suitable model for deployment.

Answer

Only the best model without any metrics

Answer

A single optimal model with no further tuning

Answer

A Python script for manual training

Question 11

An organization needs to ensure that all data used for inference on a SageMaker endpoint is encrypted at rest. The endpoint uses a SageMaker-provided container. Which configuration should be applied?

Accepted Answer

Specify a KMS key in the endpoint configuration. SageMaker endpoints use AWS KMS for encryption at rest. By specifying a KMS key in the endpoint configuration, the data in the attached ML storage volume is encrypted. Inter-container traffic encryption is for encryption in transit.

Answer

Use a custom container with built-in encryption

Answer

Enable network isolation mode

Answer

Enable inter-container traffic encryption

Question 12

A machine learning engineer is training a TensorFlow model using SageMaker with distributed training. They need to implement data parallelism across multiple GPUs. Which SageMaker feature should they use to distribute the training?

Accepted Answer

SageMaker Distributed Data Parallelism. SageMaker Distributed Data Parallelism is the correct choice because it is purpose-built to distribute training across multiple GPUs by replicating the model on each GPU and splitting the input data across them. It uses the AllReduce algorithm with custom communication optimizations (e.g., balanced fusion of gradients) to synchronize gradients efficiently, reducing communication overhead compared to standard Horovod or torch.distributed. This feature is integrated into the SageMaker training toolkit and works with TensorFlow, PyTorch, and MXNet, making it the right tool for data parallelism.

Answer

SageMaker Automatic Model Tuning

Answer

SageMaker Debugger

Answer

SageMaker Model Parallelism

Question 13

A company deploys a model with SageMaker and wants to monitor for concept drift. They have noticed that the relationship between input features and the target variable has changed, causing model accuracy to degrade. However, the input data distribution remains stable. Which type of drift is this, and what is the most appropriate response strategy?

Accepted Answer

Concept drift; retrain the model with newly collected labeled data. This is concept drift because the relationship between input features and the target variable has changed while the input data distribution remains stable. The most appropriate response is to retrain the model with newly collected labeled data that reflects the current relationship, as concept drift requires updating the model's learned mapping from features to labels.

Answer

Concept drift; ignore the change as long as input distribution remains stable

Answer

Data drift; update the baseline statistics and continue monitoring

Answer

Data drift; retrain the model with the latest training data

Question 14

An organization wants to automate ML retraining using an event-driven architecture. Which THREE services should they combine? (Select THREE.)

Accepted Answer

SageMaker (training jobs or pipelines). Amazon EventBridge [CORRECT] is the event-driven backbone: it receives events (e.g., new data in S3, a schedule, or a custom PutEvents call) and routes them to targets via rules, which is exactly what an event-driven retraining architecture requires. AWS Lambda [CORRECT] acts as the lightweight, serverless compute glue that responds to those EventBridge events and invokes the training workflow, so no servers need to be managed. SageMaker (training jobs or pipelines) [CORRECT] performs the actual model retraining and orchestration, since SageMaker Pipelines can be triggered programmatically to run training jobs and register updated models. AWS Glue is a data-integration/ETL service and, while useful for preparing data, it is not required to deliver the event-driven retraining trigger or the training itself. Amazon CloudWatch Logs is for storing and monitoring log data and does not provide event routing or ML training capability.

Answer

AWS Glue

Answer

Amazon CloudWatch Logs

Question 15

Which THREE steps should be taken to optimize a large-scale distributed training job on SageMaker? (Choose 3.)

Accepted Answer

Use GPU instances with high bandwidth and memory (e.g., ml.p4d.24xlarge).. Option B is correct because large-scale distributed training is compute- and memory-intensive, so GPU instances like ml.p4d.24xlarge provide high-bandwidth networking (up to 400 Gbps) and large GPU memory (A100 40 GB each) that accelerate training and reduce communication bottlenecks. Option D is correct because Elastic Fabric Adapter (EFA) enables low-latency, high-throughput inter-node communication using OS bypass, which is essential for scaling distributed training across many instances. Option E is correct because choosing the right distributed training strategy — Horovod for data parallelism, SageMaker distributed data parallel for optimized AllReduce, or model parallel for models too large for a single GPU — directly determines training efficiency and scalability. Option A is not appropriate because attaching multiple EBS volumes with provisioned throughput addresses storage I/O, not the inter-node GPU communication and compute parallelism that dominate large-scale distributed training. Option C is not appropriate because batch transform is an inference-time feature and does not optimize the training job itself.

Answer

Attach multiple EBS volumes with throughput provisioning.

Answer

Enable batch transform for offline inference after training.