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

Question 1

You are designing a distributed training job for a very large neural network that does not fit on a single machine. You need to split the model across multiple devices. Which TWO techniques can you use?

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

Accepted Answer

Pipeline parallelism. Pipeline parallelism splits layers across devices; model parallelism (operator-level) splits individual operations. Data parallelism replicates the model. ParameterServer is a form of distributed training, not model splitting.

Answer

ParameterServerStrategy

Answer

Data parallelism with MirroredStrategy

Answer

MultiWorkerMirroredStrategy

Question 2

You are fine-tuning a large language model using Vertex AI Training with spot VMs to reduce cost. Your training job keeps getting preempted, causing delays. Which THREE strategies can help mitigate the impact of preemption?

Accepted Answer

Enable checkpointing and restore from the latest checkpoint after preemption. Checkpointing saves progress; using a smaller model reduces training time; increasing number of workers allows faster training and recovery. Using dedicated VMs increases cost. Reducing batch size doesn't help with preemption frequency.

Answer

Reduce batch size to lower memory usage

Answer

Switch to dedicated (non-preemptible) VMs

Question 3

You are building a machine learning pipeline on Google Cloud. You need to perform feature engineering on large datasets stored in BigQuery and store the resulting features in Vertex AI Feature Store for both online and offline use. Which TWO Google Cloud services should you use?

Accepted Answer

Dataflow. Dataflow can read from BigQuery, compute features via Apache Beam, and write to Feature Store. Alternatively, Dataproc can also do this but Dataflow is more serverless. Cloud Functions is not suitable for large-scale. Cloud Build is for CI/CD. BigQuery ML is for in-database ML.

Answer

Cloud Functions

Answer

BigQuery ML

Answer

Cloud Build

Question 4

A data scientist wants to train a TensorFlow model on Vertex AI using a pre-built container. Which of the following pre-built containers is NOT available for custom training in Vertex AI?

Accepted Answer

Apache Spark. Vertex AI provides pre-built containers for TensorFlow, PyTorch, scikit-learn, and XGBoost. There is no pre-built container for Apache Spark in the Vertex AI training service; Spark jobs are typically run on Dataproc.

Answer

TensorFlow

Answer

PyTorch

Answer

scikit-learn

Question 5

You need to run a distributed training job on Vertex AI using TensorFlow with MirroredStrategy on a single machine with 4 GPUs. Which training configuration should you use?

Accepted Answer

Use MirroredStrategy with a single workerPoolSpec containing a machine_type with 4 GPUs. For single-machine multi-worker training with multiple GPUs, TensorFlow's MirroredStrategy is appropriate. The workerPoolSpec should have a single worker pool with a machine type that has multiple GPUs.

Answer

Use MultiWorkerMirroredStrategy with multiple workerPools

Answer

Use MirroredStrategy with two workerPoolSpecs, each with 2 GPUs

Answer

Use ParameterServerStrategy with a chief and a parameter server

Question 6

You have a very large language model that does not fit on a single GPU. You need to train it efficiently across multiple GPUs on a single machine. Which approach should you use?

Accepted Answer

Model parallelism using pipeline parallelism. When a model is too large for a single GPU, model parallelism (specifically pipeline parallelism) splits the model across GPUs. Data parallelism would require each GPU to hold the full model, which is not possible.

Answer

Data parallelism with MirroredStrategy

Answer

Data parallelism with MultiWorkerMirroredStrategy

Answer

Use TPU training as TPUs have more memory

Question 7

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 8

You are performing hyperparameter tuning on Vertex AI with Vizier. You want to maximize the accuracy of your model, and you have a budget of 50 trials. Which algorithm should you choose to best explore the search space?

Accepted Answer

Bayesian optimization. Bayesian optimization is the default and recommended algorithm for most use cases as it balances exploration and exploitation efficiently. For a budget of 50 trials, Bayesian optimization is suitable.

Answer

No algorithm; use default Vertex AI tuning

Answer

Grid search

Answer

Random search

Question 9

You need to preprocess a large dataset (terabytes) for training a TensorFlow model. The preprocessing includes scaling and bucketizing features, and the same transformations must be applied during serving. Which tool should you use?

Accepted Answer

Dataflow with Apache Beam and tf.Transform. TensorFlow Transform (tf.Transform) allows you to define preprocessing pipelines that are applied consistently during training and serving. It computes statistics over the full dataset and creates a TensorFlow graph for serving.

Answer

Dataproc with Spark ML

Answer

Vertex AI Feature Store

Answer

BigQuery ML

Question 10

You are fine-tuning a pre-trained BERT model from Hugging Face for a sentiment analysis task using Vertex AI training. The dataset has 100k examples. To avoid catastrophic forgetting, which layer freezing strategy should you apply?

Accepted Answer

Unfreeze all layers and fine-tune the entire model. For fine-tuning with a sufficiently large dataset, it is common to unfreeze all layers to adapt the model to the new task. Freezing many layers is typical for very small datasets. With 100k examples, full fine-tuning is appropriate.

Answer

Freeze the first 6 layers, fine-tune the last 6 layers

Answer

Freeze all layers except the classification head

Answer

Use transfer learning only on the embeddings layer

Question 11

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 12

You are designing a distributed training job for a very large neural network that does not fit on a single machine. You need to split the model across multiple devices. Which TWO techniques can you use?

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

Accepted Answer

16. In PyTorch DDP on Vertex AI, each GPU typically runs one process. With 4 nodes each with 4 GPUs, the world size is 16 (total GPUs). Vertex AI distributed training uses TF_CONFIG to set up the cluster; for PyTorch, you need to set the number of workers to the total number of processes, which is 16.

Answer

4

Answer

8

Answer

1

Question 13

You have an edge device with limited compute resources. You need to deploy a deep learning model for real-time inference. Which model compression technique should you apply to reduce the model size and latency with minimal accuracy loss?

Accepted Answer

Post-training quantization to INT8. Post-training quantization (e.g., INT8) is the easiest and most effective method for reducing model size and latency on edge devices. Quantization-aware training can yield better accuracy but is more complex. Pruning and distillation also help, but quantization often gives the best trade-off.

Answer

Pruning only

Answer

Knowledge distillation only

Answer

Use full precision FP32 to maintain accuracy

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

Answer

Grid search

Question 15

You are using tf.Transform to preprocess data at scale. Which TWO services are required to run tf.Transform on Google Cloud? (Choose 2)

Accepted Answer

Dataflow. tf.Transform requires Apache Beam for execution, which on GCP is typically run on Dataflow. The processed data and transform artifacts are stored in Cloud Storage.

Answer

Cloud Functions

Answer

Vertex AI Training

Answer

BigQuery