PMLE Scaling Prototypes into ML Models Practice Test 5 — 15 Questions

Question 1

Your Vertex AI custom training job is failing with an out-of-memory error on a single GPU. You need to reduce memory usage without changing the model architecture. Which approach should you try first?

Accepted Answer

Decrease the batch size. Decreasing the batch size directly reduces the memory required for activations and gradients, making it the simplest and most immediate way to resolve out-of-memory errors without altering the model architecture. It is a standard first step because it requires no code changes beyond a hyperparameter and often resolves OOM with minimal impact on convergence if adjusted with learning rate.

Answer

Implement model parallelism across GPUs

Answer

Use gradient accumulation

Answer

Enable mixed precision training (FP16)

Question 2

You need to perform a large-scale feature computation on streaming data from Pub/Sub, transforming raw events into features, and writing results to Vertex AI Feature Store for online serving. Which Google Cloud architecture is most appropriate?

Accepted Answer

Use Dataflow streaming pipeline with Apache Beam to read from Pub/Sub, compute features, and write to Feature Store. Dataflow with Apache Beam is the purpose-built managed service for streaming ETL on Google Cloud, natively integrating with Pub/Sub as a source and Vertex AI Feature Store as a sink. Beam's windowing, watermarks, and exactly-once semantics handle the large-scale, continuous feature computation required for online serving. It provides autoscaling and managed infrastructure, which is exactly what a production streaming feature pipeline needs.

Answer

Use Dataproc with Spark Streaming to read from Pub/Sub and write to Feature Store

Answer

Use Cloud Functions triggered by Pub/Sub to compute features and update Feature Store

Answer

Use Cloud Run to consume Pub/Sub messages and update Feature Store via a service

Question 3

You want to use Vertex AI JumpStart to quickly deploy a pre-built foundation model for text summarization. Which action is required?

Accepted Answer

Select the model from Model Garden and deploy it to a Vertex AI endpoint. Vertex AI JumpStart provides pre-built foundation models in Model Garden that can be deployed directly to a Vertex AI endpoint with minimal configuration. Selecting the model from Model Garden and deploying it to an endpoint is the standard JumpStart workflow for getting a foundation model into production for tasks like text summarization. No training, containerization, or batch export is required because JumpStart handles the deployment plumbing.

Answer

Train the model from scratch using Vertex AI Training

Answer

Export the model to a Cloud Storage bucket and use batch prediction

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Build a custom Docker container with the model and deploy to Vertex AI

Question 4

Your PyTorch training script uses DistributedDataParallel (DDP) across 4 vertices each with 4 GPUs (16 GPUs total). You submit a Vertex AI custom training job. How should you configure the worker pool spec?

Accepted Answer

Create one worker pool with 4 replicas, each with machine type having 4 GPUs. For DDP across multiple machines, use MultiWorkerMirroredStrategy equivalent in PyTorch: set replicas to 4, each with machine type having 4 GPUs. The TF_CONFIG env var is not needed; Vertex AI sets necessary environment variables for distributed training.

Answer

Create a chief worker pool with 1 replica (4 GPUs) and a parameter server pool with 4 replicas (no GPUs)

Answer

Create 4 separate jobs, each with 1 replica and 4 GPUs

Answer

Create one worker pool with 16 replicas, each with 1 GPU

Question 5

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's Trainer API exposes mixed precision through the TrainingArguments parameter fp16=True (or bf16=True for bfloat16). Setting this flag enables automatic mixed precision (AMP) via PyTorch's torch.cuda.amp, which casts eligible operations to FP16 while keeping master weights in FP32 for numerical stability. This is the standard, supported way to accelerate fine-tuning on GPU without rewriting the model.

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 6

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?

Accepted Answer

Pipeline parallelism. Pipeline parallelism (B) is correct because it splits the model's layers into sequential stages placed on different devices, so a model too large for one machine can be distributed across devices while micro-batches flow through the pipeline. Operator-level model parallelism (C) is also correct because it partitions individual operations (e.g., splitting a large matrix multiplication or a layer's weights) across devices, which directly addresses a model that does not fit on a single machine. ParameterServerStrategy (A) is a data-parallel approach that replicates the full model on each worker and only shards the parameter updates, so it does not solve the memory problem of a model too large for one device. Data parallelism with MirroredStrategy (D) likewise replicates the entire model on every device, which is infeasible when the model does not fit on one machine. MultiWorkerMirroredStrategy (E) is also a data-parallel strategy that requires the full model to fit on each worker, so it does not satisfy the requirement.

Answer

ParameterServerStrategy

Answer

Data parallelism with MirroredStrategy

Answer

MultiWorkerMirroredStrategy

Question 7

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. MirroredStrategy is TensorFlow's single-machine, multi-GPU strategy that replicates the model across all GPUs on one host using all-reduce for gradient synchronization. On Vertex AI, this maps to a single workerPoolSpec whose machine type has 4 GPUs attached. No additional workers are needed because MirroredStrategy operates within one machine.

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 8

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 does not fit on a single GPU, model parallelism is required because it partitions the model itself across devices. Pipeline parallelism is a specific model parallelism technique that splits the model into stages across GPUs and pipelines micro-batches to maintain utilization, making it the appropriate approach for training a very large model on multiple GPUs in one machine.

Answer

Data parallelism with MirroredStrategy

Answer

Data parallelism with MultiWorkerMirroredStrategy

Answer

Use TPU training as TPUs have more memory

Question 9

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 (the successor to preemptible VMs) by setting the 'spot' field to true in the machine spec of the worker pool. Spot VMs offer up to 60-91% discounts and are suitable for fault-tolerant jobs with checkpointing, since they can be preempted with 30 seconds' notice. This is the documented, supported configuration for cost-optimized training.

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 10

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. tf.Transform is a library built on Apache Beam that allows you to define preprocessing functions (like scaling and bucketizing) once and then apply them consistently in both training and serving. Dataflow provides a scalable, fully managed runner for Apache Beam pipelines, making it ideal for processing terabyte-scale datasets. This combination ensures that the exact same transformations are used during training and serving, avoiding training-serving skew.

Answer

Dataproc with Spark ML

Answer

Vertex AI Feature Store

Answer

BigQuery ML

Question 11

You are designing a distributed training job on Vertex AI for a PyTorch model using DataDistributedParallel (DDP). You have 4 nodes, each with 4 GPUs. What is the total number of workers that should be configured in the TF_CONFIG equivalent for PyTorch?

Accepted Answer

16. In PyTorch DDP on Vertex AI, the number of workers equals the total number of processes across all nodes, which is nodes × GPUs per node. With 4 nodes and 4 GPUs each, the total is 16 workers, so the TF_CONFIG-equivalent worker count should be 16. Each GPU runs one DDP process, and all 16 processes participate in the all-reduce synchronization.

Answer

4

Answer

8

Answer

1

Question 12

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 to INT8 reduces model weights and activations from 32-bit floats to 8-bit integers, cutting model size by roughly 4x and speeding up inference on edge hardware with minimal accuracy loss. It requires no retraining and works with most trained models. This makes it the best fit for limited-compute edge deployment.

Answer

Pruning only

Answer

Knowledge distillation only

Answer

Use full precision FP32 to maintain accuracy

Question 13

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 is the default and best-suited algorithm in Vertex AI Vizier for mixed parameter spaces containing both categorical and continuous parameters. It builds a probabilistic surrogate model of the objective function and uses an acquisition function to intelligently select the next hyperparameter configuration, handling categorical and continuous dimensions natively. This makes it far more sample-efficient than random or grid search when the search space is mixed and potentially high-dimensional.

Answer

Evolutionary algorithm

Answer

Random search

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Grid search

Question 14

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

Question 15

A data scientist needs to train a large PyTorch model on a custom dataset using Vertex AI. The training script expects data from Cloud Storage and uses GPU acceleration. Which option correctly configures a custom training job with a pre-built container for PyTorch and attaches a single NVIDIA V100 GPU?

Accepted Answer

Use the pre-built container 'us-docker.pkg.dev/vertex-ai/training/pytorch-gpu.1-12:latest' and in worker_pool_specs set machine_type='n1-standard-4', accelerator_type='NVIDIA_TESLA_V100', accelerator_count=1. Vertex AI custom training jobs use worker_pool_specs to define the machine type, container image, and accelerator configuration. The correct configuration uses the pre-built PyTorch GPU container and specifies machine_type='n1-standard-4', accelerator_type='NVIDIA_TESLA_V100', and accelerator_count=1 in the worker pool spec. This is the documented Vertex AI pattern for attaching a single V100 GPU.

Answer

Use a custom container built from PyTorch base image and specify accelerator_count=1 in the machine spec

Answer

Use the AI Platform Training service with gcloud ai-platform jobs submit training and --scale-tier BASIC_GPU

Answer

Create a training pipeline with AutoML and select GPU runtime