PMLE Practice Test 34 — 15 Questions

Question 1

You are training a model on Vertex AI using a custom training job. The training data is stored in a Cloud Storage bucket in the us-central1 region, and the training job runs in the us-central1 region. You notice that the training job takes significantly longer than expected due to data loading. You want to improve data loading performance without changing the model architecture. What should you do?

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

Accepted Answer

Convert the training data to TFRecord format and use the tf.data.TFRecordDataset with parallel interleave and prefetching.. Converting data to TFRecord and using tf.data with parallel interleave and prefetching optimizes the input pipeline by reducing the number of read operations and overlapping data loading with computation. This is a standard best practice for improving training performance on Vertex AI when data is in Cloud Storage.

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Copy the training data to the local SSD of the training VM before training begins, and read from there.

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Enable streaming reads from Cloud Storage by using the tf.data.experimental.make_csv_dataset function with a large buffer size.

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Use the Vertex AI Training reduction server to accelerate data loading.

Question 2

You are preparing a Vertex AI custom training job that uses a custom Docker image built on top of the PyTorch pre-built training container. The image must be able to read training data from a Cloud Storage bucket without embedding credentials in the image. You want the job to run on a single NVIDIA T4 GPU. Which approach should you take?

Accepted Answer

Specify a user-managed service account with the Storage Object Viewer role when submitting the custom job, and let the container use Application Default Credentials to read from Cloud Storage.. A user-managed service account attached to the custom job provides the identity that the training container uses. Vertex AI injects credentials via the metadata server, so Application Default Credentials in the container can authenticate to Cloud Storage. Granting the least-privilege Storage Object Viewer role satisfies the data access requirement without embedding keys. The job can also be configured with a single T4 GPU accelerator to meet the compute requirement.

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Mount the Cloud Storage bucket as a local file system using Cloud Storage FUSE inside the container and rely on the bucket's default IAM permissions for anonymous access.

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Hard-code a service account JSON key file into the Docker image and set GOOGLE_APPLICATION_CREDENTIALS in the Dockerfile.

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Grant the Vertex AI Custom Code Service Agent the Storage Object Viewer role and let the training container use the default credentials from the metadata server.

Question 3

A team has a prototype image classification model trained on a small dataset using TensorFlow Keras on a single GPU. They need to train on a larger dataset (1 million images) using a distributed strategy on Vertex AI with 8 GPUs. They implement a MirroredStrategy for data parallelism. During the first few epochs, the training speed does not improve significantly compared to a single GPU, and GPU utilization is low. The data is stored as JPEG files in Cloud Storage, and the input pipeline uses tf.data with map to decode images. What is the most likely cause?

Accepted Answer

The data loading from Cloud Storage is a bottleneck.. Reading and decoding JPEG images from Cloud Storage can be I/O-bound, causing low GPU utilization. Even though MirroredStrategy is used, if the input pipeline cannot supply data fast enough, GPUs will spend time waiting for data. Option A is incorrect because a large batch size per GPU typically increases memory usage and computational load, not decreases utilization; it might even improve utilization if the model fits. Option B is incorrect because MirroredStrategy is a straightforward configuration for synchronous data parallelism and does not require complex tuning; it is likely configured correctly. Option D is incorrect because the model size does not directly cause low GPU utilization; even a small model can saturate GPUs if the data pipeline is efficient. The primary bottleneck here is the data loading from Cloud Storage.

Answer

The batch size per GPU is too large.

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The MirroredStrategy is not properly configured.

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The model is too small for distributed training.

Question 4

An ML engineer is training a TensorFlow model on Vertex AI using a custom training job with a single worker and multiple GPUs. The training script uses tf.distribute.MirroredStrategy. After a few epochs, the job fails with a NCCL timeout error. The engineer confirms the GPUs are healthy and the batch size is reasonable. What should they do to resolve the error?

Accepted Answer

Set the environment variable NCCL_SOCKET_IFNAME to the correct network interface and ensure the container image includes a compatible NCCL version.. NCCL timeout errors in a single-worker, multi-GPU Vertex AI training job usually stem from NCCL being unable to select the correct network interface or from incompatible NCCL versions. Explicitly setting NCCL_SOCKET_IFNAME to the primary interface and using a container image with a validated NCCL build ensures that inter-GPU communication initializes correctly. MirroredStrategy does not require TF_CONFIG, so adding workers or TF_CONFIG would not help.

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Increase the number of workers in the worker pool configuration to match the number of GPUs.

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Set the environment variable NCCL_DEBUG=INFO and reduce the per-GPU batch size to lower memory pressure.

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Ensure the training job is configured with a single replica and that the container sets the TF_CONFIG environment variable appropriately for a single-worker, multi-GPU setup.

Question 5

You are deploying a scikit-learn model to Vertex AI for online predictions. The model requires a custom preprocessing step that transforms raw JSON input into a feature vector before calling predict. You want to minimize latency and avoid managing infrastructure. What should you do?

Accepted Answer

Create a custom container that includes the scikit-learn model and a Flask app that performs preprocessing and calls predict, then deploy it to a Vertex AI endpoint.. Vertex AI custom containers for prediction allow you to bundle the scikit-learn model, custom preprocessing code, and an HTTP server such as Flask. This keeps preprocessing and inference in the same process, reducing latency, and Vertex AI handles scaling and infrastructure management. Pre-built containers do not support arbitrary preprocessing, and batch prediction or model conversion would not meet the low-latency online requirement.

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Export the scikit-learn model to a TensorFlow SavedModel and deploy it with the TensorFlow pre-built container, using a preprocessing layer in the model.

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Deploy the scikit-learn model using the pre-built scikit-learn container and implement preprocessing in a Cloud Function that calls the endpoint.

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Use Vertex AI Batch Prediction with a pre-built scikit-learn container and a custom preprocessing script.

Question 6

A financial services company wants to detect fraudulent transactions in real-time. They have a trained XGBoost model that runs on a single Compute Engine instance. The current solution processes about 100 transactions per second, but they need to scale to 10,000 transactions per second. Which approach should they take?

Accepted Answer

Deploy the model to Vertex AI Prediction with autoscaling enabled. Vertex AI Prediction with autoscaling is the correct choice because it is purpose-built for serving ML models at scale, automatically adjusting the number of compute nodes based on incoming request traffic. This allows the company to seamlessly handle the increase from 100 to 10,000 transactions per second without manual intervention, while XGBoost is natively supported as a framework.

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Increase the VM to a machine type with more vCPUs and memory

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Use Dataflow to process transactions in micro-batches every second

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Rewrite the model as a Cloud Function triggered by Pub/Sub messages

Question 7

You have a prototype ML model that you want to scale to production on Vertex AI. The model is a Python function that performs simple data preprocessing and then calls a pre-trained scikit-learn model. You need to deploy this as a batch prediction job that runs weekly on a large dataset stored in BigQuery. What is the most efficient way to accomplish this?

Accepted Answer

Create a custom container that includes the preprocessing code and the scikit-learn model, push it to Artifact Registry, and use it to run a Vertex AI Batch Prediction job with BigQuery as the input source.. For batch prediction with custom preprocessing, a custom container is required because pre-built containers do not include your code. Vertex AI Batch Prediction natively supports BigQuery as both input and output, so you can avoid data movement. This method is efficient, scalable, and integrates with Vertex AI's managed infrastructure.

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Deploy the model to a Vertex AI Endpoint and write a script that sends all BigQuery rows as individual prediction requests, then store the results in BigQuery.

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Use a Vertex AI Pipelines job that runs a Dataflow transform to preprocess the BigQuery data, then calls a pre-built scikit-learn model for prediction and writes results back to BigQuery.

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Export the BigQuery data to Cloud Storage as CSV files, then run a Vertex AI Batch Prediction job using a pre-built scikit-learn container that reads from Cloud Storage.

Question 8

You are training a scikit-learn model on Vertex AI using a custom training job. The training dataset is a 2 TB CSV file stored in Cloud Storage, and the job must run on a single CPU-only VM. Loading the entire file into memory fails because the machine has only 32 GB of RAM. You need to train the model without increasing the VM size and without rewriting the training code to use a distributed framework. What should you do?

Accepted Answer

Use the scikit-learn partial_fit method on an estimator that supports incremental learning, reading the CSV in chunks with pandas.read_csv and feeding each chunk to partial_fit.. Incremental learning with partial_fit is the correct approach because it lets a scikit-learn estimator update its parameters one chunk at a time. Reading the CSV in chunks with pandas.read_csv bounds memory to the chunk size rather than the full 2 TB. This keeps the single-model semantics, avoids distributed training, and fits within the 32 GB VM, directly satisfying all the stated constraints.

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Use Vertex AI Pipelines to split the CSV into many smaller files, then run a separate training job for each file and average the model weights.

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Convert the CSV to TFRecord format and use tf.data to stream batches, then train the scikit-learn model on the streamed batches.

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Mount the Cloud Storage bucket as a local filesystem on the training VM and call pandas.read_csv on the mounted path, relying on the OS page cache to keep memory usage low.

Question 9

A team is training a TensorFlow model on Vertex AI using a custom container. The training script writes checkpoints to a local directory inside the container. The job runs for 14 hours, and when it completes, the team cannot find the checkpoints in Cloud Storage. They need the checkpoints to be persisted so they can resume training and deploy the best model. What should they do?

Accepted Answer

Configure the training job to write checkpoints to a Cloud Storage URI by passing a gs:// path to the checkpoint directory in the training script, and ensure the Vertex AI service account has storage.objectAdmin on the bucket.. Checkpoints must be written to a durable location, and in Vertex AI custom training the durable location is Cloud Storage. Passing a gs:// URI to the checkpoint directory makes TensorFlow persist each checkpoint through its GCS filesystem, and granting storage.objectAdmin to the training service account authorizes those writes. This allows both resuming training and retrieving the best model for deployment.

Answer

Enable Vertex AI TensorBoard integration and configure the training script to log checkpoints as TensorBoard artifacts, then retrieve them from the TensorBoard instance.

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Set the training job's base output directory to a local path and rely on Vertex AI to automatically upload everything under that path to the job's Cloud Storage output directory at the end of training.

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Increase the boot disk size of the training VM and re-run the job, then copy the checkpoints from the boot disk to Cloud Storage after training finishes.

Question 10

An ML engineer is using Vertex AI Training to fine-tune a large image classification model on a dataset stored in Cloud Storage. The training job uses a custom container and runs on a single NVIDIA V100 GPU. The engineer notices that GPU utilization is consistently low (around 20%) and training is slow. The data is stored as many small JPEG files. What should the engineer do to improve GPU utilization and training speed?

Accepted Answer

Convert the dataset to TFRecord format and use the tf.data API with parallel interleaving and prefetching.. Low GPU utilization during training often indicates that the GPU is starved for data. When data is stored as many small files, the I/O overhead dominates. Converting to TFRecord, a binary format optimized for TensorFlow, and using tf.data with parallel reads and prefetching can dramatically speed up data loading. This ensures the GPU receives data as fast as it can process it, improving utilization and reducing training time.

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Enable Vertex AI Vizier to automatically tune hyperparameters and improve training speed.

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Use a larger GPU instance with more memory, such as an NVIDIA A100.

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Increase the batch size to the maximum that fits in GPU memory.

Question 11

You are scaling a prototype ML model to production on Vertex AI. The model is trained with a custom training job and you want to ensure reproducibility and traceability of each training run. Which two practices should you implement? (Choose two.)

Accepted Answer

Store the training code in a Git repository and pass the commit hash as a hyperparameter or label to the Vertex AI training job.. To ensure reproducibility and traceability, you need to record the exact code version and the training parameters and metrics. Storing the Git commit hash with the training job links the model to the code, while Vertex AI Experiments logs parameters, metrics, and artifacts for each run. Together, these practices allow you to reproduce any model and trace its lineage. Model monitoring, hardware consistency, and timestamped folders do not provide the necessary metadata linkage.

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Store the trained model artifacts in a Cloud Storage bucket with a unique timestamped folder for each run.

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Use the same machine type and accelerator for every training run to ensure identical hardware.

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Enable Vertex AI Model Monitoring on the endpoint to detect training-serving skew.

Question 12

Drag and drop the steps to set up a batch prediction job using Vertex AI in the correct order.

Accepted Answer

Prepare input data, then register model, then create batch prediction job, then submit job, then retrieve results.. The correct sequence for setting up a batch prediction job using Vertex AI is to first prepare your input data, then register your model, create the batch prediction job, submit the job, and finally retrieve the results. This order ensures that all necessary resources are available before job creation and submission, and that results are only requested after job completion.

Answer

Register model, then prepare input data, then create batch prediction job, then submit job, then retrieve results.

Answer

Create batch prediction job, then prepare input data, then register model, then submit job, then retrieve results.

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Prepare input data, then register model, then create batch prediction job, then retrieve results, then submit job.

Question 13

You are a machine learning engineer at a retail company. Your team uses Vertex AI Pipelines to train a model that predicts customer churn. The pipeline reads training data from a BigQuery table that is updated daily by an external marketing analytics team. You need to ensure that every pipeline run uses a consistent snapshot of the data and that you can reproduce any past run for auditing. What should you do?

Accepted Answer

Before each pipeline run, create a BigQuery table snapshot of the source table and configure the pipeline to read from that snapshot.. Using BigQuery table snapshots ensures that each pipeline run reads an immutable, point-in-time copy of the source data. This guarantees consistency across runs and enables exact reproducibility for audits, because the snapshot preserves the data as it existed when created. Other methods either do not freeze the data or introduce complexity without the same guarantees.

Answer

Configure the pipeline's training component to query the BigQuery table using a parameter that specifies the current date, and log that date in the pipeline run metadata.

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Set up a Cloud Scheduler job that copies the BigQuery table to a Cloud Storage bucket as CSV files, and have the pipeline read from that bucket.

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Use BigQuery's streaming inserts to push data into a new table that the pipeline reads, ensuring that the data is frozen at the time of insertion.

Question 14

A data science team is using Vertex AI Pipelines to orchestrate their ML workflows. They want to ensure that each pipeline run is reproducible and that artifacts are versioned. Which Vertex AI feature should they use to track and manage pipeline artifacts?

Accepted Answer

Vertex ML Metadata. Vertex ML Metadata is the dedicated service for capturing and managing metadata and artifacts from Vertex AI Pipelines. It records executions, artifacts, and their relationships, enabling lineage tracking and reproducibility. The other options serve different purposes: Experiments for run comparison, Model Registry for models, and Feature Store for feature serving.

Answer

Vertex AI Model Registry

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Vertex AI Experiments

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Vertex AI Feature Store

Question 15

An ML team is using Vertex AI Online Prediction and wants to receive alerts when the 99th percentile latency exceeds 500ms for more than 5 minutes. What is the best practice to set up this alert in Cloud Monitoring?

Accepted Answer

Use the 'aiplatform.googleapis.com/prediction/online_prediction_latencies' metric with a metric threshold condition set to 500ms and a percentile aligner of 99.. Cloud Monitoring provides a pre-built metric, `aiplatform.googleapis.com/prediction/online_prediction_latencies`, which directly captures prediction latency. By applying a percentile aligner of 99 and a metric threshold condition of 500ms, you can alert when the 99th percentile latency exceeds 500ms for the specified duration, without needing custom instrumentation or external processing.

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Create a custom metric from the prediction container that emits latency percentiles, then set an alert on that metric.

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Use a log-based metric to parse latency from Cloud Logging and alert when the average exceeds 500ms.

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Export prediction latency logs to BigQuery and run a scheduled query to check the 99th percentile, then trigger a Cloud Function to send an alert.