MLA-C01 ML Model Development Practice Test 8 — 15 Questions

Question 1

An ML engineer creates a SageMaker inference pipeline with two containers: a preprocessor and a predictor. The preprocessor is a lightweight Python script that transforms input data. How should the engineer structure the endpoints to ensure both containers run sequentially?

Accepted Answer

Create a PipelineModel in SageMaker with both containers listed in order: first preprocessor, then predictor.. Option A is correct because SageMaker inference pipelines chain containers in the order specified in the pipeline model. You create a PipelineModel with a list of containers. Option B is wrong because Lambda is not needed; the pipeline handles sequencing. Option C is wrong because batch transform is for batch processing, not sequential inference. Option D is wrong because using a single container would require bundling, which is less modular.

Answer

Use batch transform with two transform jobs chained together.

Answer

Use an AWS Lambda function as a proxy to invoke the preprocessor and then the predictor separately.

Answer

Combine the preprocessor and predictor into a single Docker container.

Question 2

Refer to the exhibit. A data scientist ran a SageMaker training job using a built-in algorithm. The job failed with the above error. What is the most likely cause?

Accepted Answer

The input CSV file has missing or mismatched column headers.. Option B is correct because the error explicitly states the data format is incorrect; the CSV headers do not match the expected schema. Option A is wrong because the error does not mention memory. Option C is wrong because the error is about data format, not permissions. Option D is wrong because the algorithm is built-in and should support CSV if headers match.

Answer

The S3 bucket lacks proper permissions for SageMaker to read the training data.

Answer

The built-in algorithm does not support CSV input format.

Answer

The training instance ran out of memory.

Question 3

Which TWO options are recommended best practices for monitoring model performance in production on SageMaker? (Choose 2.)

Accepted Answer

Use SageMaker Clarify for bias monitoring and feature importance drift.. Options A and C are correct. SageMaker Model Monitor can track data quality and model quality drifts. Option B is wrong because CloudWatch alarms can notify but are not a complete monitoring solution. Option D is wrong because manual review is not scalable. Option E is wrong because retraining without monitoring may be premature.

Answer

Retrain the model daily based on recent data without evaluation.

Answer

Set up a CloudWatch alarm on the endpoint's Invocations metric.

Answer

Manually compare prediction distributions weekly.

Question 4

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).. Options A, C, and D are correct. Using EFA (Elastic Fabric Adapter) reduces network latency, choosing the right distribution strategy (e.g., data parallelism vs model parallelism) improves scaling, and using GPU-optimized instances provides high compute. Option B is wrong because attaching additional EBS volumes does not directly help distributed training performance. Option E is wrong because batch transform is for inference, not training.

Answer

Attach multiple EBS volumes with throughput provisioning.

Answer

Enable batch transform for offline inference after training.

Question 5

Which TWO SageMaker Pipelines steps are essential for automating a complete ML workflow from data processing to model deployment? (Choose 2.)

Accepted Answer

A ProcessingStep to run data preprocessing and feature engineering.. Options B and D are correct. ProcessingStep runs data processing, and CreateModelStep or RegisterModelStep deploys the model. Step A is wrong because TrainingStep is for training, but included in typical pipeline, but the question asks essential steps; actually a pipeline must handle processing and model creation. However, without training step, no model. Let's refine: The question implies a complete workflow, so likely all mentioned but we need two. Possibly they want the ones that are not optional. Correct answer: B (ProcessingStep) and D (CreateModelStep) are core. Option A (TrainingStep) is also core, but since we choose two, we need to pick the most fundamental? The instructions say 'essential' maybe both processing and training are essential. Let's reconsider: I'll make A incorrect because it is not essential if using a built-in algorithm? No, training is essential. This is tricky. I'll restructure the options to make two clearly essential: ProcessingStep and RegisterModelStep (or CreateModelStep). But to avoid confusion, I'll set the correct answer as: B (ProcessingStep) and D (ModelStep for deployment). However, TrainingStep is also essential. Since it's 'choose 2', I need to ensure only two are fully correct. Let me change the options so that training step is not listed as a separate option, or make it a distractor. I'll adjust: Options: A) ConditionStep for branching, B) ProcessingStep, C) TuningStep, D) CreateModelStep, E) TransformStep. Then correct are B and D, because condition is optional, tuning optional, transform optional. Yes that works. I'll update the question stem to include typical steps. Let's finalize.

Answer

A TuningStep for hyperparameter tuning.

Answer

A TransformStep for batch inference on the training data.

Answer

A ConditionStep to decide whether to train a model based on data quality.

Question 6

A financial services company is training a large natural language processing (NLP) model using PyTorch on a SageMaker distributed training job. The cluster consists of 4 ml.p3.16xlarge instances (8 GPUs each). The training job runs successfully but takes 72 hours, exceeding the allotted 48-hour window. The team must reduce training time without sacrificing model quality. The model architecture has 1.5 billion parameters and currently uses the SageMaker data parallel library with Horovod for all-reduce. Observing CloudWatch metrics, the team notices that GPU utilization averages only 45% and network throughput is near maximum. Which action will most effectively reduce training time?

Accepted Answer

Switch to SageMaker model parallel library with pipeline parallelism to reduce communication overhead.. Option C is correct because with low GPU utilization and high network bandwidth consumption, the bottleneck is likely communication overhead. Model parallelism splits the model across GPUs, reducing the need for frequent all-reduce of large gradients, thus improving GPU utilization. Option A is wrong because increasing instance count would increase communication overhead and likely not improve utilization. Option B is wrong because data parallelism already uses GPUs; increasing batch size may cause memory overflow. Option D is wrong because enabling EFA improves network, but network is already near maximum; the bottleneck is not network speed but the frequency of communication.

Answer

Enable Elastic Fabric Adapter (EFA) for faster inter-node connectivity.

Answer

Increase the batch size to improve GPU utilization.

Answer

Increase the number of instances from 4 to 8 to add more GPUs.

Question 7

A retail company uses SageMaker to train a multi-class image classification model with a custom ResNet-50 implemented in TensorFlow. The training data is 500 GB of images stored in S3. The data scientist uses a ml.p3.2xlarge instance with a single GPU. The training takes 10 hours per epoch, and the model does not converge after 5 epochs. The scientist needs to accelerate training and improve model accuracy. The current implementation loads images individually from S3 using TensorFlow's tf.data API. The scientist also notices high I/O wait time. Which combination of actions should the scientist take? (Assume the scientist is aware of best practices.) The answer is a single choice from A-D.

Accepted Answer

Use SageMaker Pipe mode for data ingestion and upgrade to a ml.p3.8xlarge instance.. Option B is correct because using SageMaker Pipe mode streams data directly from S3 to the training container, reducing I/O bottlenecks. Additionally, switching to a multi-GPU instance like ml.p3.8xlarge speeds up computation. Option A is wrong because increasing epochs does not address I/O or speed. Option C is wrong because batch transform is for inference. Option D is wrong because recordIO is not natively supported by TensorFlow tf.data without conversion, and EFS adds network latency.

Answer

Increase the number of epochs to 20 and enable early stopping with patience 5.

Answer

Convert images to RecordIO format and store them on Amazon EFS for faster access.

Answer

Deploy the model on a SageMaker endpoint and use batch transform for offline predictions.

Question 8

A company is training a binary classifier in SageMaker and observes that the training loss decreases but validation loss increases after a few epochs. What is the most likely issue?

Accepted Answer

Overfitting. Option A is correct because overfitting occurs when the model performs well on training data but poorly on validation data. Option B is wrong because underfitting would show high loss on both datasets. Option C is wrong because a high learning rate may cause divergence but not necessarily validation loss increase. Option D is wrong because data imbalance typically affects both training and validation metrics.

Answer

Learning rate too high

Answer

Underfitting

Answer

Data imbalance

Question 9

A data scientist needs to ensure that the same train/test split is used across multiple experiments for reproducibility in SageMaker. Which approach should they take?

Accepted Answer

Set a random seed in the training script. Option C is correct because setting a random seed in the training script ensures reproducibility of the data split. Options A, B, and D are incorrect because instance type, dataset version, and hyperparameters do not control the random split.

Answer

Use the same SageMaker instance type

Answer

Use the same hyperparameter values

Answer

Use the same dataset version

Question 10

A company uses SageMaker to train a model with a large dataset stored in S3. They notice that the training job is taking longer than expected and the GPU utilization is low. Which action would most likely improve GPU utilization?

Accepted Answer

Increase the batch size. Option D is correct because increasing the batch size can help saturate GPU compute. Options A is wrong because decreasing batch size would lower GPU utilization. B is wrong because using a smaller instance provides less compute. C is wrong because disabling distributed training reduces parallelism.

Answer

Disable distributed training

Answer

Use a smaller instance type

Answer

Decrease the batch size

Question 11

A team is deploying a model that requires low-latency inference for real-time predictions. They are using a SageMaker endpoint with a single instance. During testing, they observe high latency. Which change would most effectively reduce latency?

Accepted Answer

Switch to a larger instance type. Option B is correct because switching to a larger instance type provides more compute capacity, reducing inference latency. Option A is wrong because multi-model endpoints may increase latency due to model loading. C is wrong because Batch Transform is for batch, not real-time. D is wrong because Elastic Inference adds GPU acceleration but may not reduce latency as much as compute upgrade.

Answer

Use a multi-model endpoint

Answer

Add Elastic Inference

Answer

Enable SageMaker Batch Transform

Question 12

A data scientist is using SageMaker Autopilot to automatically build a model. Which TWO aspects does Autopilot handle? (Choose TWO.)

Accepted Answer

Feature engineering. Options A and B are correct because Autopilot performs automated feature engineering and hyperparameter tuning. Option C is wrong because Autopilot does not deploy the model; that is a separate step. D is wrong because data labeling is not part of Autopilot. E is wrong because Autopilot does not handle data ingestion beyond using provided dataset.

Answer

Data ingestion

Answer

Model deployment

Answer

Data labeling

Question 13

A company is training a deep learning model using SageMaker's built-in PyTorch framework. They want to optimize training performance. Which THREE actions should they take? (Choose THREE.)

Accepted Answer

Use Pipe mode to stream data from S3. Options B, C, and D are correct. Using SageMaker Debugger for profiling (B) helps identify bottlenecks. Pipe mode (C) streams data from S3 efficiently. Increasing DataLoader workers (D) parallelizes data loading. Option A is wrong because checkpoint storage does not directly improve performance. Option E is wrong because spot instances reduce cost but not performance.

Answer

Use a spot instance for training

Answer

Use a SageMaker ML Storage volume for checkpointing

Question 14

A team is using SageMaker Pipelines to automate a training workflow. They need to ensure that if a step fails, the pipeline can resume from the failed step without reprocessing prior steps. Which TWO configurations are necessary? (Choose TWO.)

Accepted Answer

Set a retry policy on the step. Options B and D are correct. Enabling caching on each step (B) allows outputs to be reused from previous runs. Setting a retry policy (D) allows the pipeline to retry the failed step. Option A is wrong because parallelism does not affect resumption. Option C is wrong because Lambda steps are for custom processing, not resumption. Option E is wrong because storing artifacts is common but not sufficient for resumption without caching.

Answer

Set the Pipeline's parallel flag to True

Answer

Use a Lambda step for retry logic

Answer

Store intermediate artifacts in S3

Question 15

A company is building a recommendation system and has trained a matrix factorization model using SageMaker. They want to evaluate the model's performance using precision at k (P@k) and recall at k (R@k). They have a test set of user-item interactions. The data scientist implements a custom evaluation script that computes these metrics, but the precision values are consistently zero. What is the most likely cause?

Accepted Answer

The test set contains only positive interactions.. Option C is correct because if the test set contains only positive interactions (items the user interacted with), then there are no negative examples. In precision at k, if the recommended items do not exactly match the test set items (which is likely), precision will be zero. Options A and B are incorrect because ranking or k value would not cause consistent zero unless no overlap. Option D is incorrect because overfitting would cause high training accuracy, not zero precision.

Answer

The model outputs are not being ranked correctly.

Answer

The model is overfitting.

Answer

The k value is too large.