MLA-C01 Practice Test 13 — 15 Questions

Question 1

A machine learning engineer is configuring auto-scaling for a SageMaker real-time endpoint. The endpoint is expected to have steady traffic during business hours and low traffic at night. The engineer wants to minimize costs by scaling in during low traffic, but the model container has a long start-up time (about 5 minutes). Which scaling policy should the engineer use to prevent request drops during sudden traffic spikes?

Accepted Answer

Use a target tracking scaling policy based on average invocations per minute with a warm-up of 300 seconds.. Option B is correct because target tracking scaling policies in SageMaker automatically adjust capacity to maintain a target metric value, and the warm-up time of 300 seconds accounts for the 5-minute container start-up latency. This prevents request drops during sudden traffic spikes by ensuring new instances are fully initialized before they receive traffic, while still allowing the endpoint to scale in during low traffic to minimize costs.

Answer

Use a step scaling policy based on invocations per minute with a step that adds two instances at a time.

Answer

Use a scheduled scaling action to add instances before business hours and remove them after.

Answer

Use a simple scaling policy based on average CPU utilization with a cooldown period of 5 minutes.

Question 2

An ML team is running multiple SageMaker endpoints for various models. The monthly cost is higher than expected. Which TWO actions would help reduce costs without negatively impacting performance?

Accepted Answer

Consolidate multiple small models into a single Multi-Model Endpoint on a larger instance.. Option A is correct because SageMaker Multi-Model Endpoints allow you to host multiple small models on a single endpoint behind a common serving container, sharing the underlying instance resources. This reduces the number of endpoints and instances needed, lowering costs without degrading performance, as models are loaded and unloaded dynamically based on traffic.

Answer

Increase the number of minimum instances to handle traffic spikes without scaling.

Answer

Limit the maximum number of concurrent invocations per endpoint.

Answer

Use a scheduled scaling to turn off endpoints during non-business hours.

Question 3

A company has deployed a SageMaker real-time endpoint for a model that predicts customer churn. The endpoint uses a single ml.m5.large instance. After deployment, the team notices that during peak hours, the endpoint returns 5xx errors for about 20% of requests. The endpoint has not been configured with any scaling policy. The team needs to resolve this issue with minimal cost increase. Which solution should the team implement?

Accepted Answer

Enable Auto Scaling for the endpoint with a target tracking policy based on the average InvocationsPerInstance metric.. Option B is correct because enabling Auto Scaling with a target tracking policy based on the average InvocationsPerInstance metric dynamically adjusts the number of instances in response to traffic spikes, preventing 5xx errors during peak hours without over-provisioning. This approach minimizes cost by scaling only when needed, unlike manual instance upgrades or batch transforms that either increase baseline cost or introduce latency.

Answer

Deploy the model to a multi-model endpoint to reduce resource utilization.

Answer

Increase the instance type to ml.m5.xlarge to handle more concurrent requests.

Answer

Use SageMaker batch transform instead of real-time inference to process peak traffic asynchronously.

Question 4

A company deploys a SageMaker endpoint that is InService, but inference requests are returning 503 Service Unavailable errors when traffic is high. The endpoint uses three ml.m5.large instances with target tracking scaling based on CPU utilization. The team has confirmed the model container is healthy. Which TWO possible issues could cause 503 errors?

Accepted Answer

The instance memory is insufficient for the model, causing the container to run out of memory under load.. Option B is correct because the endpoint might not have enough instances to handle peak load if scaling policies have cooldown delays. Option E is correct because if the model is large and instances run out of memory, new requests may be rejected. Option A is wrong because execution role permissions cause 403 or 500 errors, not 503. Option C is wrong because 503 indicates server overload, not client request timeout. Option D is wrong because 400 errors are from client side.

Answer

The model output is too large for the response buffer.

Answer

The execution role does not have permission to invoke SageMaker endpoint.

Answer

The inference request size exceeds the maximum payload size limit.

Question 5

A company deploys a deep learning model to a real-time SageMaker endpoint. After deployment, users report high inference latency. Which action is the MOST effective first step to reduce latency?

Accepted Answer

Compile the model using SageMaker Neo to optimize for the target instance.. SageMaker Neo compiles the trained model to optimize it for the target instance hardware, reducing inference latency without requiring additional resources. This is the most effective first step because it directly addresses model execution efficiency, often yielding significant speedups for deep learning models.

Answer

Switch to a larger instance type with more GPU memory.

Answer

Enable SageMaker Model Monitor to capture inference data.

Answer

Increase the number of instances in the endpoint to handle more requests.

Question 6

A company is adopting Amazon SageMaker Pipelines to automate their ML workflow. They want to choose three key benefits that SageMaker Pipelines provides over traditional manual scripts and ad-hoc steps. Which THREE benefits are correct?

Accepted Answer

Model lineage tracking from raw data to trained model artifacts.. Option A is correct because SageMaker Pipelines automatically captures and tracks the lineage of every artifact, including datasets, processing jobs, training jobs, and model versions. This lineage is stored in SageMaker's metadata store, enabling full traceability from raw data to the final model artifact, which is critical for auditability and compliance in ML workflows.

Answer

Automated deployment of models to endpoints upon pipeline completion.

Answer

Automatic scaling of compute resources based on data volume.

Question 7

An ML team uses AWS Step Functions to orchestrate a multi-step inference pipeline: data preprocessing, model inference, and postprocessing. The pipeline runs on demand for single records. The team notices that the pipeline occasionally fails due to timeouts in the preprocessing step. They want to implement retries with exponential backoff and a maximum retry count of 3 for that step. How should they configure this?

Accepted Answer

Modify the Step Functions state machine definition to add a Retry field on the preprocessing state with a maximum retry count of 3 and an exponential backoff rate of 2.0.. Option B is correct because AWS Step Functions natively supports retry logic with exponential backoff directly in the state machine definition. By adding a `Retry` field on the preprocessing state with `MaxAttempts: 3` and `BackoffRate: 2.0`, the service automatically retries the step on specified errors (e.g., `States.Timeout` or `Lambda.ServiceException`) with exponentially increasing wait times, without requiring custom code or external orchestration.

Answer

Implement retry logic inside the preprocessing Lambda function code.

Answer

Wrap the preprocessing step in a SageMaker Pipeline step with retry policy.

Answer

Add a Catch in the state machine to rerun the entire pipeline if preprocessing fails.

Question 8

Your team manages a SageMaker real-time endpoint for a financial services application that requires low latency for fraud detection. The model is a 1 GB XGBoost model. The endpoint is deployed on two ml.m5.xlarge instances with target tracking auto-scaling based on average CPU utilization at 70%. During peak hours, the endpoint receives a sudden burst of traffic that increases from 500 requests per second to 2000 requests per second within 30 seconds. Many requests start failing with 503 errors. The CPU utilization metric shows that the instances are at 90% before the scaling policy launches new instances. However, by the time the new instances are added (approximately 3 minutes), the burst has subsided. You need to prevent these failures during future bursts while keeping costs reasonable. Which action would be MOST effective?

Accepted Answer

Reduce the target tracking scaling metric to 45% CPU utilization and set a warm-up time of 120 seconds.. Option A is correct because reducing the target tracking scaling metric to 45% CPU utilization triggers scaling actions earlier, before the burst pushes CPU to 90%. Setting a warm-up time of 120 seconds ensures new instances are fully initialized and ready to serve traffic, preventing the 503 errors caused by the 3-minute lag in instance availability.

Answer

Change the scaling policy to step scaling with a lower cooldown (60 seconds) and add an alarm on invocation count.

Answer

Replace the two m5.xlarge instances with one m5.2xlarge instance and keep the same scaling policy.

Answer

Implement scheduled scaling to add two instances 5 minutes before the expected peak hour.

Question 9

A company is deploying a deep learning model for real-time inference using Amazon SageMaker. The model is a CPU-intensive XGBoost model that performs well with CPU. However, the team wants to minimize latency further by using hardware acceleration. They are considering Amazon Elastic Inference (EI) or moving to a GPU instance. The model is not optimized for GPU, so significant code changes would be required. Which approach is the MOST cost-effective way to reduce latency without changing the model code?

Accepted Answer

Attach an Elastic Inference accelerator (e.g., ml.eia2.medium) to the existing CPU endpoint.. Option B is correct because Amazon Elastic Inference (EI) allows you to attach a low-cost GPU-powered acceleration to an existing SageMaker CPU endpoint without any code changes. Since the XGBoost model is CPU-optimized and not GPU-native, EI provides hardware acceleration for the inference computation (specifically matrix operations) while keeping the model execution on the CPU, thus reducing latency without requiring model modifications.

Answer

Use a GPU instance (ml.p3.2xlarge) and optimize the model with SageMaker Neo compilation.

Answer

Use SageMaker Neo to compile the model for CPU with INT8 quantization.

Answer

Migrate the model to AWS Lambda with a custom runtime and use AVX instructions.

Question 10

A company is building a CI/CD pipeline for ML models using AWS CodePipeline and SageMaker. The pipeline should include steps to automatically retrain, evaluate, and deploy models. Which THREE components are essential for this pipeline? (Choose three.)

Accepted Answer

SageMaker Pipelines to orchestrate training and evaluation steps.. SageMaker Pipelines is essential because it provides a native orchestration service to define, automate, and manage the end-to-end ML workflow, including training, evaluation, and conditional deployment steps. It integrates directly with other SageMaker components and CodePipeline, enabling a seamless CI/CD pipeline without requiring custom orchestration logic.

Answer

Amazon CloudWatch to log API calls.

Answer

AWS Lambda function to trigger evaluation.

Question 11

A machine learning engineer is training a model using SageMaker's built-in XGBoost algorithm. The training job fails with an error indicating insufficient memory. Which parameter should be adjusted to reduce memory usage?

Accepted Answer

max_depth. Option A is correct because decreasing max_depth reduces the size of each tree, which lowers memory consumption during training. Option B (subsample) reduces the number of rows per iteration but may not directly address tree size. Option C (colsample_bytree) reduces the number of features per tree but is less effective than max_depth. Option D (num_round) increases the number of trees but does not directly reduce per-tree memory.

Answer

subsample

Answer

num_round

Answer

colsample_bytree

Question 12

A team is tuning hyperparameters for a neural network using SageMaker's HyperparameterTuningJob with Bayesian optimization. After several trials, the objective metric has not improved significantly. Which action is most likely to help continue making progress?

Accepted Answer

Switch to random search strategy. Option D is correct because switching to random search introduces exploration and can help escape local optima that Bayesian optimization might be stuck exploiting. Option A (switch to Bayesian) is already in use. Option B (warm start) uses previous results but does not change the search strategy. Option C (expand ranges) might help if the optimum lies outside current ranges, but stagnation often requires more exploration.

Answer

Expand the hyperparameter ranges

Answer

Use a warm start with previous tuning results

Answer

Switch to Bayesian search

Question 13

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. Option D is correct because SageMaker's Distributed Data Parallel (DDP) replicates the model across multiple GPUs and splits mini-batches, speeding up training for models that fit on a single GPU. Option A (Model Parallel) is for models too large for one GPU. Option B (larger instance) may provide more vCPUs but not necessarily more GPUs. Option C (Elastic Inference) accelerates inference, not training.

Answer

Use a larger instance with more vCPUs

Answer

Model parallelism using SageMaker's Model Parallel

Answer

Use Elastic Inference

Question 14

A machine learning engineer is training a deep learning model using TensorFlow in SageMaker. The training runs on an ml.p3.16xlarge instance (8 GPUs). The engineer notices that GPU utilization is low (~30%) and time per epoch is high. The model uses a custom training loop. Which configuration change is most likely to improve GPU utilization?

Accepted Answer

Increase the batch size to match GPU memory. Option C is correct because increasing the batch size increases the computational work per GPU, keeping them busier and improving utilization. Option A (mixed precision) can improve throughput but not necessarily utilization if batch size remains small. Option B (SageMaker Managed Warm Pools) is for inference. Option D (reducing data loading workers) could worsen data starvation, decreasing utilization.

Answer

Reduce the number of data loading workers

Answer

Use mixed precision training

Answer

Enable SageMaker Managed Warm Pools

Question 15

A data scientist is performing feature engineering on a dataset containing a categorical feature with high cardinality (over 1000 unique values). Which encoding method is most appropriate to use as input for a tree-based model?

Accepted Answer

Label encoding. Option A is correct because label encoding assigns integer labels to categories, and tree-based models can effectively split on these ordinal-like values without creating a large number of features. Option B (one-hot encoding) would produce too many features. Option C (target encoding) risks data leakage. Option D (binary encoding) creates fewer features than one-hot but still many and may not be as interpretable for trees.

Answer

One-hot encoding

Answer

Target encoding

Answer

Binary encoding