PMLE Scaling Prototypes into ML Models Practice Test 2 — 15 Questions

Question 1

A company is deploying a deep learning model on edge devices with limited storage and computational resources. They need to reduce the model size by 80% while maintaining acceptable accuracy. Which two techniques should they combine?

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

Accepted Answer

Quantization and pruning. Quantization reduces model size by converting weights from FP32 to INT8, achieving up to 75% size reduction. Pruning removes redundant weights (e.g., 50% pruning) to further shrink the model. Together, these two techniques can reduce model size by over 80% (e.g., 50% pruning + INT8 quantization yields ~87.5% reduction) while preserving acceptable accuracy on edge devices. Knowledge distillation also reduces size but typically requires a pre-trained teacher and may not achieve the extreme compression needed when combined with quantization alone.

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Pruning and distillation

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Quantization and distillation

Question 2

A company is fine-tuning a large language model (Gemma 7B) using Vertex AI JumpStart. They want to reduce the model's memory footprint for deployment on edge devices. Which THREE model compression techniques should they consider?

Accepted Answer

Post-training quantization to INT8. Post-training quantization to INT8 (A) is correct because it converts the model's FP32/FP16 weights to 8-bit integers after training, cutting memory usage roughly 2-4x with minimal accuracy loss, which is ideal for edge deployment. Quantization-aware training (C) is correct because it simulates quantization during fine-tuning so the Gemma 7B model learns to compensate for reduced precision, yielding better accuracy than post-training quantization alone at the same low-bit footprint. Pruning of weights with small magnitude (E) is correct because removing near-zero weights reduces parameter count and model size, directly lowering the memory footprint on edge devices. Using a larger batch size during inference (B) is incorrect because batch size affects throughput and activation memory, not the model's stored weight footprint, and larger batches actually increase memory use. Increasing the number of layers (D) is incorrect because adding layers enlarges the model and increases memory requirements, the opposite of compression.

Answer

Using a larger batch size during inference

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Increasing the number of layers

Question 3

Your team is deploying a large language model (LLM) on Vertex AI for online prediction. The model exceeds the maximum request size for Vertex AI Prediction. Which approach should you take to serve this model?

Accepted Answer

Deploy the model on a Compute Engine VM with a custom container and a load balancer. Vertex AI online prediction enforces a maximum request size (1.5 MB for HTTP, 10 MB for gRPC) that cannot be raised by changing machine type or protocol. When the model's input payload exceeds this limit, the correct workaround is to move off the managed prediction service and host the model yourself on a Compute Engine VM with a custom container behind a load balancer, where you control request size limits.

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Use Vertex AI Endpoint with a larger machine type and gRPC

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Use Vertex AI Batch Prediction

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Split the model into smaller parts and deploy multiple endpoints

Question 4

An ML engineer wants to use Vertex AI Model Garden to deploy a pre-trained foundation model for text summarisation. What is the quickest way to achieve this?

Accepted Answer

Use Vertex AI JumpStart to deploy the model with one click. Vertex AI JumpStart provides a streamlined way to deploy pre-trained foundation models from Model Garden with just a few clicks, often with one-click deployment options. It abstracts away the complexity of containerization, endpoint configuration, and scaling, making it the quickest method for deploying a pre-trained model for text summarization.

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Use Vertex AI AutoML for text summarisation

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Use Vertex AI Pipelines to build a custom training pipeline

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Export the model from Model Garden and deploy using a custom container

Question 5

You are performing post-training quantisation of a trained TensorFlow model to INT8 for deployment on edge devices. Which technique should you use to minimise accuracy loss?

Accepted Answer

Quantisation-aware training. Quantisation-aware training (QAT) simulates the effects of quantisation during training by inserting fake quantisation nodes, allowing the model to learn weights that are robust to the reduced precision. This minimises accuracy loss compared to post-training quantisation methods.

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Float16 quantisation

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Post-training integer quantisation with calibration

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Post-training dynamic range quantisation

Question 6

You are fine-tuning a pre-trained model using transfer learning. The new dataset is small and very similar to the original training data. To avoid overfitting, which layer freezing strategy should you adopt?

Accepted Answer

Freeze all layers and train only the classifier head. When fine-tuning a pre-trained model on a small dataset that is very similar to the original training data, the safest strategy to avoid overfitting is to freeze all layers and train only the classifier head. This preserves the rich, general-purpose feature representations learned from the original large dataset, while allowing the final classification layer to adapt to the new task. Training the entire model or unfreezing many layers on a small dataset would risk overfitting because the model would have too many parameters to update relative to the limited new samples.

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Unfreeze the last few layers and freeze the rest

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Randomly reinitialise all layers and train from scratch

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Unfreeze all layers and train the entire model

Question 7

You are using Vertex AI hyperparameter tuning with a custom container. The training job reports the objective metric but Vizier is not converging. Which configuration change could improve convergence?

Accepted Answer

Increase the number of parallel trials. Increasing the number of parallel trials can help explore more of the hyperparameter space, but the specified number of max trials must be high enough. Reducing parallel trials may slow exploration. Bayesian optimisation benefits from more trials.

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Use a larger machine type for each trial

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Reduce the number of parallel trials

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Switch from Bayesian to grid search

Question 8

You are designing a distributed training job for a PyTorch model on Vertex AI using multiple machines with GPUs. Which TWO configurations are required to enable data parallelism with PyTorch DDP? (Choose 2.)

Accepted Answer

Use the command 'torch.distributed.launch' to start each worker.. PyTorch DDP requires the master address and port (MASTER_ADDR, MASTER_PORT) for the communication group, and WORLD_SIZE and RANK. Vertex AI sets TF_CONFIG for TensorFlow, not PyTorch. NCCL is the backend.

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Enable Vertex Explainable AI during training.

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Set environment variable TF_CONFIG for each replica.

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Specify a custom service account with access to Cloud TPU.

Question 9

You are using Vertex AI to train a model with a custom container. You need to pass command-line arguments for hyperparameters. Which TWO methods can you use? (Choose 2.)

Accepted Answer

Use the 'command' field to override the entrypoint and include arguments.. Options D and E are correct because the 'command' field allows you to override the container's entrypoint and include command-line arguments, and the 'args' field appends arguments to the entrypoint or command. Option B is incorrect because environment variables are not command-line arguments; they are a separate mechanism for passing configuration. Options A and C are also incorrect.

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Store arguments in a Cloud Storage file and download at runtime.

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Set environment variables using the 'env' field and reference them in the container.

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Set hyperparameter values in the 'hyperparameters' field of the worker pool spec.

Question 10

An ML engineer is using Vertex AI Vizier to tune hyperparameters for a custom training job. The training job takes 2 hours per trial. To speed up the process, the engineer wants to run 10 trials in parallel. What is the correct way to configure parallel trial execution?

Accepted Answer

Set the 'parallelTrialCount' parameter in the study configuration to 10. In Vertex AI Vizier, parallel trial execution is configured by setting the 'parallelTrialCount' field in the study configuration. The maxParallelTrials is not a direct field; instead, the StudySpec contains a StudyJobConfig with parallelTrialCount. Setting parallelTrialCount to 10 allows up to 10 trials to run concurrently.

Answer

Use the '--parallel-trials' flag in the gcloud ai hp-tuning-jobs create command

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Set the 'maxParallelTrials' attribute in the HyperparameterSpec

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Create a CustomJob with 'numTrials' set to 10 and 'parallel' flag

Question 11

An ML team is fine-tuning a large language model using a custom container on Vertex AI. They want to reduce costs by using preemptible (spot) VMs for training. The training job is long-running and uses checkpointing. Which statement is correct regarding spot VM usage?

Accepted Answer

You must enable checkpointing in the training code and use spot VMs by setting the 'spot' field in the machine spec. Vertex AI custom training jobs support spot VMs, but you must explicitly enable checkpointing in your training code and set the 'spot' field in the machine spec to true. This ensures that when a preemptible VM is terminated, the training can resume from the latest checkpoint, preventing loss of progress and reducing costs.

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Spot VMs are not available for custom training jobs on Vertex AI

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Training will automatically resume from the latest checkpoint without any configuration

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Spot VMs cannot be used with GPU accelerators

Question 12

An organization wants to deploy a pre-trained BERT model for sentiment analysis on Vertex AI. They want to fine-tune it on their domain-specific data. Which feature in Vertex AI allows them to find and fine-tune a suitable foundation model with minimal effort?

Accepted Answer

Vertex AI JumpStart. Vertex AI JumpStart provides one-click deployment and fine-tuning of foundation models, including BERT and other NLP models. Model Garden is for model discovery, but fine-tuning is typically done via JumpStart. AutoML is for training custom models, not fine-tuning existing ones. Custom training requires more manual effort.

Answer

Vertex AI Model Garden

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Vertex AI AutoML

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Vertex AI Custom Training

Question 13

A data scientist wants to use tf.Transform for preprocessing a large dataset stored in BigQuery before training a TensorFlow model. The preprocessing should be consistent during training and serving. What is the correct way to use tf.Transform in this scenario?

Accepted Answer

Use tf.Transform to define a preprocessing_fn and apply it to the dataset, then export the transform graph for serving. Tf.Transform is specifically designed to handle full-pass preprocessing (e.g., computing min/max, vocabularies) that requires seeing the entire dataset. By defining a `preprocessing_fn` and applying it via `tft.beam.analyze_and_transform`, the transform graph is exported as a SavedModel, which can be loaded during serving to ensure identical preprocessing logic. This guarantees consistency between training and inference, which is critical for production ML pipelines.

Answer

Write preprocessing logic in Python and reuse the same code in training and serving

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Use BigQuery's built-in ML.TRANSFORM function for consistency

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Use a Lambda layer in Keras for preprocessing

Question 14

A team is training a large image classification model using transfer learning from a pre-trained ResNet50. The model will be deployed on mobile devices. They want to fine-tune only the last few layers while keeping the earlier layers frozen. Which approach should they use?

Accepted Answer

Load ResNet50, freeze all layers, add new classification layers, and train only the new layers. Transfer learning typically involves loading a pre-trained model (e.g., ResNet50 from Keras Applications) without the top classification layer, freezing all layers, adding new trainable layers on top, and then training. The base model's layers are frozen (trainable=False). After initial training, one can optionally unfreeze some top layers.

Answer

Load ResNet50, set trainable=False for all layers, and replace the final dense layer only

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Load ResNet50 from Keras Applications, set trainable=True for all layers, add new layers, and train the entire model

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Use AutoML Vision to transfer learn without coding

Question 15

A company is deploying a TensorFlow model on Vertex AI Prediction. The model is memory-intensive and requires GPU acceleration. The team wants to minimize latency and cost. Which TWO configurations should they select? (Select 2)

Accepted Answer

Use NVIDIA T4 GPUs. For GPU-accelerated prediction, the T4 GPU is cost-effective and provides good performance for inference. Autoscaling with a minimum of 1 instance ensures availability while allowing the service to scale down when not in use. A100 is more expensive; batch prediction is for asynchronous large-scale jobs; manual scaling may lead to over-provisioning.

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Use NVIDIA A100 GPUs for faster inference

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Set manual machine count to 10 for consistent performance

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Use batch prediction to reduce cost