PMLE Scaling Prototypes into ML Models Practice Test 6 — 15 Questions

Question 1

A team is scaling a prototype ML model to production on Vertex AI. The model was developed using scikit-learn and requires custom preprocessing. They want to minimize operational overhead and ensure consistency between training and serving. Which approach should they use?

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

Accepted Answer

Use a pre-built Vertex AI container for scikit-learn and provide a custom training Python package with preprocessing code included.. Using a pre-built Vertex AI container for scikit-learn with a custom training Python package ensures that the same preprocessing code runs during both training and serving, minimizing operational overhead. This approach leverages Vertex AI's managed infrastructure to handle scaling, monitoring, and consistency without requiring custom container maintenance.

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Train on a local machine and upload the model artifacts to Cloud Storage, then create an endpoint with a pre-built container.

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Deploy the model as a custom prediction routine on Vertex AI Endpoints with a custom container.

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Export the model as a .pkl file and use Vertex AI's 'Import Model' with a default container for inference.

Question 2

A data scientist is fine-tuning a large language model from Hugging Face using Vertex AI Training with a GPU. The model has 7 billion parameters and does not fit on a single GPU. They need to split the model across multiple GPUs and train with data parallelism. Which strategy should they use?

Accepted Answer

Use pipeline parallelism via a custom container with DeepSpeed and data parallelism across workers using PyTorch DDP, configured with Vertex AI distributed training.. It combines pipeline parallelism (via DeepSpeed) to split the 7B-parameter model across multiple GPUs, with data parallelism (via PyTorch DDP) to replicate the model across workers for training on larger batches. Vertex AI distributed training coordinates the multi-worker setup, making this the only viable strategy for a model that exceeds single-GPU memory while requiring data parallelism.

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Use Vertex AI's AutoML to automatically distribute the model.

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Use Vertex AI's hyperparameter tuning with multiple trials.

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Configure a multi-worker mirrored strategy with TensorFlow, setting TF_CONFIG to use all GPUs on each worker.

Question 3

A company wants to use Vertex AI Vizier to tune hyperparameters for a PyTorch model. They have a limited budget of 50 training jobs. The objective metric is validation accuracy, and they want to find the best configuration efficiently. Which algorithm should they choose?

Accepted Answer

Bayesian optimization using Vertex AI Vizier.. Bayesian optimization is the most efficient algorithm for hyperparameter tuning when the number of trials is limited. It builds a probabilistic model of the objective function and selects promising configurations.

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Random search with 50 random configurations.

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Use a custom algorithm implemented in the training code.

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Grid search with 50 evenly spaced points.

Question 4

A data engineer wants to compute feature aggregates over a large dataset stored in BigQuery and write the results to Vertex AI Feature Store. The pipeline must handle both batch and streaming data. Which Google Cloud service should they use?

Accepted Answer

Cloud Dataflow with Apache Beam. Dataflow (Apache Beam) is the recommended service for both batch and streaming data processing at scale. It can read from BigQuery and write to Feature Store.

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BigQuery scheduled queries

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Cloud Functions triggered by Pub/Sub

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Cloud Dataproc with Spark

Question 5

An ML team is using Vertex AI to train a deep learning model on a large dataset. To reduce costs, they want to use preemptible VMs for training jobs. However, training must complete within a bounded time. Which strategy should they use?

Accepted Answer

Use Vertex AI Training with spot VMs and ensure the training code saves checkpoints periodically to Cloud Storage.. Vertex AI Training supports spot VMs (preemptible instances) for cost savings, and periodic checkpointing to Cloud Storage ensures that training can resume from the last saved state if a VM is preempted, allowing the job to complete within a bounded time despite interruptions.

Answer

Use Cloud TPU instead of GPU; TPUs are not preemptible.

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Use Vertex AI Training without spot VMs, because preemptible VMs are not supported for training.

Answer

Use a single powerful non-preemptible VM to avoid interruptions.

Question 6

A developer wants to quickly deploy a pre-trained foundation model for text generation without writing any code. Which Vertex AI feature should they use?

Accepted Answer

Vertex AI JumpStart. Vertex AI JumpStart provides one-click deployment of foundation models and ML solutions. It allows deploying pre-trained models without coding.

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Vertex AI Model Garden

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Vertex AI Endpoints

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Vertex AI AutoML

Question 7

A company has a TensorFlow model for image classification that must run on edge devices with limited memory. They need to reduce the model size without significant accuracy loss. Which technique should they use?

Accepted Answer

Post-training quantization using TensorFlow Lite.. Post-training quantization (e.g., INT8) reduces model size and speeds up inference with minimal accuracy loss. It is the simplest method for deployment on edge devices.

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Knowledge distillation to train a smaller student model.

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Pruning the model weights to zero out unimportant connections.

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Use a larger VM for training.

Question 8

An ML engineer is using Vertex AI distributed training for a TensorFlow model that uses the MirroredStrategy. They notice that the training throughput drops significantly when moving from a single GPU to multiple GPUs on the same machine. What is the most likely cause?

Accepted Answer

The batch size is too small, causing each GPU to complete its forward pass quickly, but the sync wait dominates.. MirroredStrategy synchronously updates gradients across GPUs. The overhead of gradient synchronization (all-reduce) can become a bottleneck if the model is small or the network between GPUs is slow. This is a common issue.

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The GPUs are not properly configured in TF_CONFIG.

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The learning rate is too high, causing instability.

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The model uses TensorFlow 1.x instead of 2.x.

Question 9

A data scientist wants to use a pre-trained ResNet model from Keras Applications and fine-tune it on a small custom dataset. Which approach should they take to avoid overfitting?

Accepted Answer

Freeze the first few layers and train the rest.. Freezing the earlier layers (which capture general features) and only training the later layers is a common transfer learning approach for small datasets, reducing overfitting.

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Add more convolutional layers to the model.

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Use a larger learning rate to speed up training.

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Train the entire model from scratch on the custom dataset.

Question 10

A team is using TensorFlow Transform (tf.Transform) to create preprocessing functions that will be used both in training and serving. They want to ensure consistency. Which artifact should they save after analyzing the training data?

Accepted Answer

A transform_fn SavedModel.. The correct artifact to save after analyzing training data with tf.Transform is the `transform_fn` SavedModel. This SavedModel encapsulates the exact preprocessing logic (e.g., scaling, normalization, vocabulary mapping) computed from the training dataset, ensuring that the same transformations are applied consistently during both training and serving. Without this artifact, the serving pipeline would need to recompute or duplicate the transformation logic, risking skew between training and inference.

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A trained model checkpoint.

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A analyzed_dataset directory with statistics.

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A flattened schema file (schema.pbtxt).

Question 11

An organization wants to use Vertex AI JumpStart to fine-tune a foundation model for a custom classification task. They have a labeled dataset stored in BigQuery. Which steps should they take?

Accepted Answer

Use the Vertex AI console to select a model from JumpStart, specify the BigQuery table as the source, and launch fine-tuning.. JumpStart fine-tuning typically involves selecting a model, connecting to your dataset (often via BigQuery), configuring training, and launching. The process is largely GUI-based.

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Export the data from BigQuery to CSV files in Cloud Storage, then upload to JumpStart.

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Write a custom training script using PyTorch and submit to Vertex AI Training.

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Use Vertex AI Model Garden to deploy the model directly without fine-tuning.

Question 12

An ML engineer is training a very large PyTorch model on Vertex AI using a TPU v3 pod. The training is slower than expected, and the TPU utilization is low. What is the most likely cause?

Accepted Answer

The data pipeline is a bottleneck; the TPU is waiting for data.. The most likely cause of low TPU utilization is a data pipeline bottleneck, where the TPU spends a significant amount of time idle waiting for the next batch of data to be loaded and preprocessed. TPU v3 pods are designed for high-throughput matrix operations and can process data far faster than a typical CPU-based data loader can supply it, especially if the data pipeline uses inefficient I/O, lacks prefetching, or has insufficient workers. This mismatch starves the TPU, leading to low utilization and slower training.

Answer

The learning rate schedule is too aggressive.

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The model is using a single TensorFlow operation not supported by TPU.

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The batch size is too large for the TPU memory.

Question 13

An ML team is optimizing an inference model for deployment on edge devices. They need to reduce the model size and improve latency while maintaining accuracy as much as possible. Which two techniques should they use? (Choose TWO.)

Accepted Answer

Post-training quantization to INT8.. Post-training quantization to INT8 reduces model size by converting 32-bit floating-point weights and activations to 8-bit integers, which also speeds up inference on edge devices with integer-optimized hardware. This technique typically maintains accuracy within 1-2% of the original model while significantly lowering memory footprint and latency.

Answer

Use a larger pre-trained model as a starting point.

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Use half-precision (FP16) instead of INT8.

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Increase the number of layers in the model.

Question 14

A company wants to use Vertex AI for hyperparameter tuning. Which three components are required to configure a hyperparameter tuning job? (Choose THREE.)

Accepted Answer

Algorithm (e.g., Bayesian, grid, random). Vertex AI hyperparameter tuning requires specifying the search algorithm (Bayesian, grid, or random) to determine how the hyperparameter space is explored. Bayesian optimization is the default and most efficient for continuous spaces, while grid search is exhaustive and random search is simple. Without an algorithm, Vertex AI cannot decide how to sample trials.

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Machine type for each trial

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Training container image

Question 15

An engineer is designing a distributed training job on Vertex AI for a TensorFlow model that uses the MultiWorkerMirroredStrategy. They need to ensure proper communication between workers. Which environment variable must be set correctly for each worker?

Accepted Answer

TF_CONFIG. In TensorFlow distributed training with MultiWorkerMirroredStrategy, the only required environment variable is `TF_CONFIG`. It provides the cluster topology and task identity, enabling gRPC communication between workers. The distribution strategy is defined in code, not via an environment variable. `TF_DISTRIBUTED_STRATEGY` is not a standard TensorFlow environment variable.

Answer

CLUSTER_SPEC

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TF_CPP_MIN_LOG_LEVEL

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TF_CONFIG_JSON

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TF_DISTRIBUTED_STRATEGY