MLA-C01 Practice Test 21 — 15 Questions

Question 1

A company is fine-tuning a large language model using LoRA on SageMaker. They want to reduce GPU memory usage during training. Which configuration change would help?

Accepted Answer

Use QLoRA (quantized LoRA) with 4-bit quantization. QLoRA extends LoRA by quantizing the frozen base model weights to 4-bit (typically NF4) while keeping LoRA adapters in higher precision, dramatically reducing GPU memory required for fine-tuning. This lets you train larger models on smaller GPUs with minimal accuracy loss, directly addressing the goal of reducing memory usage. It is the standard memory-optimization technique for LoRA fine-tuning on SageMaker.

Answer

Enable gradient accumulation

Answer

Increase the sequence length

Answer

Increase the batch size

Question 2

A company operates multiple AWS accounts with SageMaker workloads. They need to implement governance and security controls for model monitoring and maintenance. Which THREE actions should they take to meet compliance requirements?

Accepted Answer

Deploy a SageMaker model registry in a centralized account.. Option A is correct because a SageMaker Model Registry deployed in a centralized account provides a single governance point for cataloging, versioning, and approving models across multiple AWS accounts, which is essential for compliance and maintenance oversight. Option B is correct because AWS CloudTrail records all API activity, including calls to SageMaker and S3, giving the audit trail required to demonstrate who accessed or modified models and data. Option E is correct because AWS Config rules can continuously evaluate model artifacts in S3 for required encryption settings and flag or remediate noncompliant resources, directly enforcing a compliance control. Option C is not required because VPC Flow Logs capture network traffic metadata for notebooks, which is useful for network troubleshooting but does not address model monitoring or maintenance governance. Option D is not required because cross-account IAM trust policies for endpoints are an access mechanism, not a governance or compliance control, and the question asks for monitoring and maintenance actions.

Answer

Enable VPC Flow Logs for SageMaker notebooks.

Answer

Use IAM roles with cross-account trust policies for all SageMaker endpoints.

Question 3

A startup wants to deploy a model that has variable traffic patterns, with some periods of no traffic and occasional spikes. They want to pay only for what they use and do not want to manage instances. Which SageMaker inference option should they choose?

Accepted Answer

Serverless inference. Serverless inference is the correct choice because it automatically scales to zero during periods of no traffic and scales up to handle spikes, charging only for the compute time used. This eliminates the need to manage underlying instances, making it ideal for variable and intermittent traffic patterns.

Answer

Batch transform

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Real-time endpoint with auto-scaling

Answer

Multi-model endpoint

Question 4

A company uses Amazon SageMaker Data Wrangler to prepare data for ML. The dataset contains a timestamp column and sensor readings from IoT devices. The data scientist needs to create features such as moving averages and rolling statistics over time windows. Which Data Wrangler transformation type should be selected?

Accepted Answer

Window function. Window functions in Amazon SageMaker Data Wrangler allow you to compute moving averages, rolling statistics, and other time-window-based aggregations over ordered partitions of data. This is the correct transformation type because it directly supports operations like `SUM() OVER (ORDER BY timestamp ROWS BETWEEN 2 PRECEDING AND CURRENT ROW)` without requiring custom code or losing row-level granularity.

Answer

Join

Answer

Custom Python script

Answer

Group by and aggregate

Question 5

A company deployed a machine learning model on an Amazon SageMaker real-time endpoint. Over several weeks, they notice that inference latency has been gradually increasing, especially during peak business hours. The model and instance type have remained unchanged. What is the most likely cause of the increased latency?

Accepted Answer

The SageMaker endpoint auto scaling is not configured to scale out quickly enough under increasing traffic.. The gradual increase in latency during peak hours, with no change to the model or instance type, strongly indicates that the endpoint is not scaling out fast enough to handle increased traffic. SageMaker real-time endpoints rely on auto scaling policies to add instances based on metrics like invocation count or CPU utilization; if the scale-out step is too slow or the cooldown period is too long, requests queue up and latency rises. This matches the symptom of latency growing over weeks as traffic patterns evolve, rather than a sudden spike.

Answer

The inference script is not using batch processing.

Answer

The model size is too large for the instance type.

Answer

The endpoint has data capture enabled, causing additional overhead.

Question 6

A company wants to use SageMaker to serve real-time predictions with a model that has a large memory footprint. They need to ensure the endpoint can handle traffic spikes. Which scaling policy should they use?

Accepted Answer

Target tracking policy. Target tracking scaling policy is the correct choice because it automatically adjusts the number of instances in the SageMaker endpoint based on a target metric, such as InvocationsPerInstance or ModelLatency, to handle traffic spikes without manual intervention. This policy is ideal for real-time inference with large memory models because it dynamically scales resources up or down to maintain the target metric, ensuring consistent performance during unpredictable traffic bursts.

Answer

Simple scaling policy

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Scheduled scaling policy

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Step scaling policy

Question 7

A company has a trained machine learning model that needs to be deployed as a real-time inference endpoint on Amazon SageMaker. The endpoint must automatically scale based on incoming traffic. Which SageMaker feature should be used?

Accepted Answer

SageMaker Endpoint Auto Scaling. Amazon SageMaker Endpoint Auto Scaling is the correct feature because it automatically adjusts the number of instances serving a real-time inference endpoint based on the incoming traffic load. It uses Application Auto Scaling policies, which monitor CloudWatch metrics (e.g., InvocationsPerInstance) to scale in or out, ensuring low latency and cost efficiency without manual intervention.

Answer

SageMaker Elastic Inference

Answer

SageMaker Batch Transform

Answer

SageMaker Model Monitor

Question 8

A data scientist is using SageMaker Automatic Model Tuning with Hyperband. They want to stop poorly performing trials early to save resources. Which strategy does Hyperband use?

Accepted Answer

Successive Halving. Hyperband is a hyperparameter tuning strategy that uses Successive Halving to allocate resources efficiently. It starts many trials with small resource budgets, then iteratively promotes the best-performing trials to larger budgets while stopping poor performers early, saving compute.

Answer

Grid search

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Random search

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Bayesian optimization

Question 9

A data science team is using SageMaker Experiments to track hyperparameters and metrics for a model training project. They need to compare multiple trials and identify the best model. Which THREE actions are part of a typical workflow? (Select THREE.)

Accepted Answer

Log hyperparameters and metrics using the SageMaker SDK. Option D is correct because a SageMaker Experiments workflow begins by creating an experiment (via the SageMaker SDK, e.g., Experiment.create) that serves as the top-level container grouping runs and trials for the project. Option A is correct because during training the team logs hyperparameters and metrics to the experiment using the SageMaker SDK (for example with Run/Tracker and log_parameter/log_metric calls), which is what makes trials comparable. Option C is correct because the SDK provides APIs such as Experiment.list_runs or Trial/TrialComponent lookups and metric retrieval so the team can list trials and compare their metrics to identify the best model. Option B does not belong because SageMaker Experiments does not automatically generate confusion matrices for each trial; any such artifact must be computed and logged explicitly by the training code. Option E does not belong because automatic deployment of the best trial to an endpoint is not part of the Experiments tracking workflow; deployment is a separate step typically handled via the SageMaker model registry, pipelines, or manual deployment.

Answer

Generate confusion matrices for each trial automatically

Answer

Automatically deploy the best trial to an endpoint

Question 10

An ML engineer is setting up monitoring for a SageMaker endpoint. Which THREE metrics should be monitored to detect performance issues? (Select THREE.)

Accepted Answer

Model latency. Model latency (A) is correct because it directly measures the time the endpoint takes to respond to inference requests, and rising latency is a primary indicator of performance degradation. CPUUtilization (C) is correct because high CPU usage on the hosting instances can throttle inference processing and cause slower responses or timeouts. MemoryUtilization (D) is correct because excessive memory consumption can lead to swapping, out-of-memory errors, or container restarts that degrade endpoint performance. Invocations per second (B) reflects traffic volume rather than endpoint health, so it does not by itself indicate a performance issue. DiskWriteBytes (E) is a storage I/O metric that is largely irrelevant to the in-memory inference workload of a SageMaker endpoint.

Answer

Invocations per second

Answer

DiskWriteBytes

Question 11

A media company uses SageMaker endpoints to serve a model that predicts video engagement. They have two production variants: Variant A (ml.c5.large) for regular traffic and Variant B (ml.c5.xlarge) for burst traffic. They use weighted routing (90% to A, 10% to B). Recently, during peak hours, Variant A's latency increase causes many requests to time out. The metrics show that both variants are under similar CPU load, but the number of concurrent requests to Variant A is very high. The team wants to ensure that burst traffic is handled properly without manual intervention. What should they do?

Accepted Answer

Configure Application Auto Scaling for each variant with a target tracking scaling policy based on the number of concurrent requests per instance.. Application Auto Scaling with a target tracking policy based on concurrent requests per instance automatically adds instances when concurrency rises, directly addressing the high concurrent request load on Variant A. This is the managed, no-manual-intervention solution that scales each variant independently based on the actual bottleneck metric.

Answer

Increase the traffic weight to Variant B to 70% and reduce Variant A to 30%.

Answer

Set a CloudWatch alarm on Variant A's p99 latency and trigger a step scaling policy to add instances.

Answer

Create a separate endpoint for burst traffic and route peak traffic to it via DNS.

Question 12

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. In SageMaker's built-in LightGBM algorithm, the hyperparameter `early_stopping_rounds` controls early stopping. When a validation dataset is provided, training will stop if the evaluation metric does not improve for the specified number of consecutive rounds, preventing overfitting.

Answer

num_iterations

Answer

early_stopping

Answer

num_boost_round

Question 13

An ML engineer needs to compile a trained TensorFlow model to run efficiently on a target edge device with an ARM CPU. Which AWS service should they use?

Accepted Answer

SageMaker Neo. SageMaker Neo compiles trained models from frameworks like TensorFlow, PyTorch, and MXNet into optimized executables for specific target hardware, including ARM CPUs, Intel, and AWS Inferentia. It performs graph-level optimizations and generates a runtime that runs efficiently on the edge device.

Answer

SageMaker Debugger

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AWS Inferentia

Answer

Amazon Elastic Inference

Question 14

A data scientist wants to fine-tune a Llama 2 7B model using SageMaker for a text summarization task. The dataset is 10 GB. The budget is limited, so cost efficiency is important. Which THREE steps should the data scientist take? (Choose THREE.)

Accepted Answer

Use LoRA to reduce the number of trainable parameters. Option C is correct because LoRA (Low-Rank Adaptation) freezes the base Llama 2 7B weights and injects small trainable low-rank matrices into the attention layers, cutting the number of trainable parameters and GPU memory/compute needed for fine-tuning, which directly supports the limited budget. Option D is correct because SageMaker managed spot training uses spare EC2 capacity at up to a 90% discount and supports checkpointing to S3 so training resumes after interruptions, making it a key cost-efficiency measure. Option E is correct because the SageMaker HuggingFace estimator provides a prebuilt PyTorch/TensorFlow container with the transformers, datasets, and peft libraries needed to fine-tune Llama 2 7B for summarization without building a custom container. Option A is not correct because SageMaker Debugger is a monitoring and profiling tool for detecting training issues such as vanishing gradients or resource bottlenecks; it does not itself reduce training time. Option B is not correct because BlazingText is a built-in algorithm for text classification and word2vec embeddings, not for fine-tuning large generative LLMs like Llama 2.

Answer

Use SageMaker Debugger to reduce training time

Answer

Use the SageMaker built-in BlazingText algorithm

Question 15

A team receives alerts that their SageMaker endpoint latency has increased significantly. They check CloudWatch metrics and see Invocations rising, but ModelLatency remains stable. Which metric should they investigate to find the source of the increased latency?

Accepted Answer

OverheadLatency. OverheadLatency measures the time taken by the SageMaker infrastructure to handle requests before and after model inference, including request routing, authentication, and response processing. Since ModelLatency is stable but total endpoint latency has increased, the extra time must be in the overhead component, making OverheadLatency the correct metric to investigate.

Answer

ModelLatency

Answer

5XXError

Answer

4XXError