PMLE Practice Test 21 — 15 Questions

Question 1

A machine learning team is deploying a PyTorch model on Vertex AI Prediction for real-time inference. The model was trained with preprocessing that includes tokenization and normalization. They want to embed the preprocessing logic in the model to reduce prediction latency and avoid additional service calls. Which approach should they take?

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

Accepted Answer

Use TorchScript to trace the preprocessing steps and export the entire pipeline as a single scripted model. TorchScript allows exporting the entire model (including preprocessing) into a serialized format that can be run without Python dependencies. This reduces latency as all operations are within the exported graph. Wrapping in a Flask app or using Cloud Functions would introduce overhead. Training with tf.Transform is not applicable for PyTorch.

Answer

Deploy the preprocessing logic as a Cloud Function and invoke it before calling the prediction endpoint

Answer

Wrap the preprocessing logic in a Flask application and deploy it as a separate microservice in front of the prediction endpoint

Answer

Use TensorFlow Transform to convert preprocessing into a SavedModel and call it from the PyTorch model

Question 2

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.

Answer

The GPUs are not properly configured in TF_CONFIG.

Answer

The learning rate is too high, causing instability.

Answer

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

Question 3

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.

Answer

Vertex AI Model Garden

Answer

Vertex AI Endpoints

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Vertex AI AutoML

Question 4

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 Prediction has a request size limit (1.5 MB). Using a custom container with a ModelServer (e.g., TensorFlow Serving) behind an HTTP load balancer bypasses this limit and allows large payloads.

Answer

Use Vertex AI Endpoint with a larger machine type and gRPC

Answer

Use Vertex AI Batch Prediction

Answer

Split the model into smaller parts and deploy multiple endpoints

Question 5

You are fine-tuning a Gemma model using Vertex AI JumpStart. You want to combine the fine-tuned model with a custom output layer for a unique task. Which TWO components are required to deploy the combined model? (Choose 2)

Accepted Answer

Vertex AI Model Registry. To customize the output layer, you need a custom container that loads both the fine-tuned base model and your custom head. The model must be registered in Vertex AI Model Registry for deployment.

Answer

Vertex AI Feature Store

Answer

Cloud Run

Answer

Pre-built container for Gemma

Question 6

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.

Answer

Knowledge distillation to train a smaller student model.

Answer

Pruning the model weights to zero out unimportant connections.

Answer

Use a larger VM for training.

Question 7

A team is training a large TensorFlow model that requires more memory than a single GPU provides. They have access to multiple GPUs on a single machine. Which distributed training strategy should they use to split the model layers across GPUs?

Accepted Answer

Manual device placement using tf.device to assign layers to specific GPUs. Model parallelism splits the model itself across devices, as opposed to data parallelism which replicates the model. For TensorFlow, this is typically achieved using device placement or tf.distribute.experimental.ParameterServerStrategy with manual partitioning, but for splitting layers, tf.distribute.experimental.DeviceAssignment or manual tf.device is used.

Answer

tf.distribute.experimental.MultiWorkerMirroredStrategy

Answer

tf.distribute.experimental.ParameterServerStrategy

Answer

tf.distribute.MirroredStrategy

Question 8

A data scientist has a TensorFlow 2.x model trained on a single GPU. They want to scale training to multiple GPUs on a single Vertex AI machine without code changes. Which strategy should they use?

Accepted Answer

MirroredStrategy. MirroredStrategy distributes training across GPUs on a single machine with minimal code changes — just wrap model creation and compile inside the strategy scope. MultiWorkerMirroredStrategy is for multi-machine, not single-machine multi-GPU.

Answer

MultiWorkerMirroredStrategy

Answer

TPUStrategy

Answer

CentralStorageStrategy

Question 9

Which Vertex AI service allows you to discover, fine-tune, and deploy foundation models with a few clicks, including models like Llama and Gemma?

Accepted Answer

Vertex AI JumpStart. Vertex AI JumpStart (now part of Model Garden) provides one-click deployment and fine-tuning of foundation models. Model Garden is the broader model discovery hub, but JumpStart is the feature for quick start.

Answer

Vertex AI Prediction

Answer

Vertex AI Vizier

Answer

Vertex AI Model Garden

Question 10

Your Vertex AI endpoint receives many identical prediction requests (same input features). You want to cache responses to reduce latency and cost. Which Google Cloud service should you use?

Accepted Answer

Cloud Memorystore for Redis. Cloud Memorystore (Redis) is ideal for caching prediction results. By hashing the request input, you can store and retrieve cached responses, avoiding redundant inference.

Answer

Cloud CDN

Answer

Bigtable

Answer

Cloud Storage with object versioning

Question 11

You are fine-tuning a pre-trained BERT model from Hugging Face on a custom text classification dataset using Vertex AI Training. You want to speed up training by using mixed precision. What should you do?

Accepted Answer

Set fp16=True in the TrainingArguments. Hugging Face Trainer supports mixed precision via the fp16 argument. Set it to True in the TrainingArguments. No need to modify the model architecture or use a custom container.

Answer

Modify the model to use half-precision layers

Answer

Use a custom container with TensorFlow instead of PyTorch

Answer

Enable mixed precision via Vertex AI hyperparameter tuning

Question 12

You want to reduce training costs by using preemptible VMs on Vertex AI for a fault-tolerant distributed training job that uses checkpointing. Which machine type should you choose in the worker pool configuration?

Accepted Answer

Use spot VMs by setting 'spot' to true in the machine spec. Vertex AI supports spot VMs (formerly preemptible) for training. By setting 'spot' to true, you use preemptible instances at a discount. The training must handle preemption via checkpointing.

Answer

Use custom machine types with preemptible flag

Answer

Use standard VMs and rely on Vertex AI auto-restart

Answer

Use TPU VMs because they are cheaper

Question 13

A developer creates a Cloud Build trigger that runs a training pipeline whenever code is pushed to the main branch of the repository. The trigger is configured to use a source archive stored in Cloud Storage. After pushing code to main, the build fails with the error shown. What is the most likely cause of this failure?

Accepted Answer

The source archive is not being updated when code is pushed, so the trigger tries to fetch an old or nonexistent object.. The trigger is configured to use a source archive stored in Cloud Storage. When code is pushed to the main branch, the trigger attempts to fetch the archive from the specified Cloud Storage location. If the archive is not updated (e.g., via a separate upload or a Cloud Function that rebuilds the archive on push), the trigger will either fetch an old version or fail if the object does not exist. The error indicates that the build cannot proceed because the source archive is stale or missing, not because of a missing config file or permission issue.

Answer

The build configuration file is missing from the source archive.

Answer

The included files filter 'train/**' excludes all files outside the train directory, causing the build to have no source.

Answer

The service account does not have storage.objectViewer permission on the bucket.

Question 14

You want to use Vertex AI Vizier for hyperparameter tuning. You have 2 categorical parameters and 3 continuous parameters. Which algorithm is best suited for this mixed parameter space?

Accepted Answer

Bayesian optimization. Bayesian optimization can handle mixed parameter types. Grid search cannot handle continuous parameters efficiently; random search can, but Bayesian is more sample-efficient.

Answer

Evolutionary algorithm

Answer

Random search

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Grid search

Question 15

An ML team is building a feature pipeline with Dataflow that reads from BigQuery, computes features, and writes to Vertex AI Feature Store. They need to ensure that features are available for both training and serving with low latency. Which Feature Store option should they use?

Accepted Answer

Create a featurestore with online serving enabled. Vertex AI Feature Store supports online serving (low latency) and offline serving (batch). For low latency, they must enable online serving. The online store uses Bigtable for low-latency lookups.

Answer

Create a featurestore with only offline serving

Answer

Store features directly in BigQuery

Answer

Use Cloud SQL as a feature store