PMLE Practice Exam 52 — 20 Questions

Question 1

A team is using Vertex AI Pipelines to automate their ML workflow. They want to ensure that pipeline runs are reproducible and that artifacts are tracked. Which feature should they use?

Accepted Answer

Vertex AI Experiments. Vertex AI Experiments is the correct feature because it captures parameters, metrics, and artifacts for each pipeline run, enabling reproducibility and lineage tracking. This directly supports the team's need to ensure runs are reproducible and artifacts are tracked, as Experiments automatically logs metadata for every execution.

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Vertex AI Feature Store

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Vertex AI Model Registry

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Vertex AI Endpoints

Question 2

An ML engineer needs to track the costs incurred by Vertex AI prediction endpoints. Which tool should they use to set budget alerts and monitor spending?

Accepted Answer

Google Cloud Budgets & Alerts. Google Cloud Budgets & Alerts allow setting budget thresholds and sending notifications. Billing reports provide cost breakdowns by service.

Answer

Vertex AI Model Monitoring

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Cloud Logging

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Cloud Monitoring with custom metrics

Question 3

A team has deployed a model on Vertex AI Prediction and wants to monitor for data drift. Which TWO metrics should they use to detect drift in numerical features?

Accepted Answer

Jensen-Shannon divergence (JSD). Jensen-Shannon divergence (JSD) is a symmetric, bounded (0 to 1) measure of the difference between two probability distributions, making it ideal for detecting drift in numerical features by comparing the training distribution to the serving distribution. It is a smoothed and normalized version of Kullback-Leibler divergence, and Vertex AI Prediction's Model Monitoring natively supports JSD for numerical feature drift detection.

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Pearson correlation coefficient

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Chi-squared statistic

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Population Stability Index (PSI)

Question 4

You have a TensorFlow model that you want to deploy on edge devices for real-time inference. The model was trained in Vertex AI. You need to convert it to a format suitable for on-device inference. Which approach should you use?

Accepted Answer

Convert the model to TensorFlow Lite using the TensorFlow Lite converter.. TensorFlow Lite is specifically designed for on-device inference on edge devices, offering optimized performance and reduced model size. The TensorFlow Lite converter transforms a TensorFlow model (e.g., from a SavedModel) into the FlatBuffer format (.tflite), which is lightweight and compatible with mobile and embedded platforms. This directly addresses the requirement for real-time inference on edge devices.

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Export the model as a serialized TFX pipeline.

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Export the model to a SavedModel and deploy it using Vertex AI Edge Manager.

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Use Vertex AI Model Optimization to compile the model for edge devices.

Question 5

A machine learning engineer wants to use a pre-built Google Cloud Pipeline Components (GCPC) to train a model using Vertex AI. Which component should they use?

Accepted Answer

VertexTrainJobOp. The Google Cloud Pipeline Components library includes pre-built components for various Vertex AI services. For training, the correct component is VertexTrainJobOp. VertexBatchPredictOp is for batch prediction, VertexEndpointCreateOp for deploying endpoints, and AutoMLTabularTrainingJobRunOp is specifically for AutoML tabular jobs.

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AutoMLTabularTrainingJobRunOp

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VertexEndpointCreateOp

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VertexBatchPredictOp

Question 6

A data scientist wants to use tf.Transform for preprocessing a large dataset stored in BigQuery before training a TensorFlow model. The preprocessing should be consistent during training and serving. What is the correct way to use tf.Transform in this scenario?

Accepted Answer

Use tf.Transform to define a preprocessing_fn and apply it to the dataset, then export the transform graph for serving. Tf.Transform is specifically designed to handle full-pass preprocessing (e.g., computing min/max, vocabularies) that requires seeing the entire dataset. By defining a `preprocessing_fn` and applying it via `tft.beam.analyze_and_transform`, the transform graph is exported as a SavedModel, which can be loaded during serving to ensure identical preprocessing logic. This guarantees consistency between training and inference, which is critical for production ML pipelines.

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Write preprocessing logic in Python and reuse the same code in training and serving

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Use BigQuery's built-in ML.TRANSFORM function for consistency

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Use a Lambda layer in Keras for preprocessing

Question 7

A machine learning engineer is deploying a TensorFlow model on an edge device with limited memory and compute. The model needs to perform inference with low latency. The engineer has a trained float32 model. Which model compression technique should be applied first to reduce the model size and improve inference speed without significant accuracy loss?

Accepted Answer

Post-training quantization to INT8. Post-training quantization to INT8 is the correct first step because it directly reduces the model size by approximately 4x (from 32-bit floats to 8-bit integers) and speeds up inference on edge devices by leveraging integer-optimized hardware (e.g., ARM NEON or Qualcomm Hexagon). This technique requires no retraining and typically yields minimal accuracy loss for most TensorFlow models, making it the fastest path to deploy on resource-constrained devices.

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Knowledge distillation

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Quantization-aware training

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Weight pruning

Question 8

Refer to the exhibit. A data engineer is defining a Vertex AI Pipeline step to train a model. The pipeline fails with an error: "Failed to create vertex ai custom job: Invalid resource name." What is the most likely cause of the error?

Accepted Answer

The training_data input should be a Vertex AI Dataset resource, not a simple string.. Vertex AI Pipeline steps that use a CustomJob to train a model require the training data input to be a Vertex AI Dataset resource (a Dataset object), not a plain string. When a string is passed instead of a Dataset resource, the pipeline attempts to create a custom job with an invalid resource name, as the backend expects a properly formatted Dataset resource name (e.g., projects/{project}/locations/{location}/datasets/{dataset_id}). This mismatch triggers the 'Invalid resource name' error.

Answer

The container image URI is incorrect; it should be from gcr.io/vertex-ai/training.

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The output artifact schema is missing the 'type' property.

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The machine type n1-standard-4 is not supported for Vertex AI training.

Question 9

A non-technical user wants to build a binary classification model using Vertex AI. Which UI should they use?

Accepted Answer

Vertex AI AutoML. Vertex AI AutoML is the correct choice because it provides a no-code graphical user interface specifically designed for non-technical users to build, train, and deploy machine learning models, including binary classification models, without writing any code. It automates the entire ML pipeline—feature engineering, model selection, hyperparameter tuning—allowing users to simply upload labeled data and get a production-ready model.

Answer

Vertex AI Workbench

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Vertex AI Pipelines

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Vertex AI Prediction

Question 10

A manufacturing company uses AutoML Tables to predict equipment failure. They want to improve model performance without increasing manual effort. Which three actions should they take? (Choose THREE.)

Accepted Answer

Perform feature engineering using Vertex AI Feature Store.. Vertex AI Feature Store enables feature engineering and reuse without manual effort, allowing the team to create, store, and serve features consistently for AutoML Tables, which can improve model performance by providing more relevant input data. This aligns with the goal of reducing manual work while enhancing model accuracy through automated feature management.

Answer

Use BigQuery to aggregate sensor data before training.

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Deploy the model on a larger machine type to speed up inference.

Question 11

An ML pipeline runs on Vertex AI and includes a component that uses a third-party library not available in the default Python environment. The team wants to avoid building a custom container image. Which approach should they use?

Accepted Answer

Use the packages_to_install parameter in @dsl.component. The `packages_to_install` parameter in the `@dsl.component` decorator allows you to specify a list of third-party Python packages (e.g., via pip) that will be installed at runtime in the component's execution environment, without needing to build a custom container image. This is the recommended approach in Vertex AI Pipelines when you need to use a library not present in the default Python environment, as it avoids the overhead of custom container creation while ensuring the dependency is available for that specific component.

Answer

Install the library using pip in the pipeline definition

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Use a container component with a pre-built image

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Add the library to the Vertex AI custom training image

Question 12

A team deploys a model on Vertex AI that uses a custom prediction routine (CPR) with a dependency on a native library. The container crashes with 'ImportError: libcudart.so.11.0: cannot open shared object file'. How should they resolve this?

Accepted Answer

Build a custom container image that includes the CUDA runtime library.. The error 'ImportError: libcudart.so.11.0: cannot open shared object file' indicates that the CUDA runtime library (version 11.0) is missing from the container environment. Since the custom prediction routine (CPR) depends on a native library that requires this CUDA runtime, the correct solution is to build a custom container image that includes the CUDA runtime library. This ensures the shared object is available at runtime, resolving the import error.

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Submit the model for batch prediction to avoid the error.

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Request a GPU machine type for the endpoint.

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Use a Vertex AI pre-built container for PyTorch instead.

Question 13

A company needs to extract text from scanned invoices and parse key fields like invoice number and total amount. Which Document AI processor should they use?

Accepted Answer

Invoice Parser. The Invoice Parser is specialised for parsing invoice documents. OCR Processor extracts text only, Form Parser extracts form fields, and Contract Parser is for legal contracts.

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OCR Processor

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Contract Parser

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Form Parser

Question 14

A company is deploying a computer vision model on edge devices using TensorFlow Lite. They want to reduce model size without significant accuracy loss. Which TWO model compression techniques are most suitable?

Accepted Answer

Weight pruning. Weight pruning (B) is suitable because it removes redundant connections (weights) from the neural network, reducing the model size and computational cost while often preserving accuracy if done gradually. Post-training float16 quantization (D) converts model weights from float32 to float16, halving the storage size with minimal accuracy loss, and is directly supported by TensorFlow Lite for edge deployment.

Answer

Quantization-aware training (QAT)

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Knowledge distillation

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Increasing model depth

Question 15

A company has deployed a machine learning model that uses a large input tensor. They notice that the prediction latency varies significantly between requests of the same size. Cloud Monitoring shows that the serving endpoint's CPU utilization is consistently below 50%, but memory utilization fluctuates between 70% and 95%. What is the most likely cause?

Accepted Answer

The model is performing garbage collection cycles. The described symptoms—low CPU utilization (below 50%) and high, fluctuating memory utilization (70%–95%) with variable latency—are classic indicators of garbage collection (GC) pauses in a managed runtime like Python or Java. When the model processes large input tensors, it allocates significant memory; as memory pressure builds, the garbage collector runs more frequently, causing stop-the-world pauses that increase latency unpredictably, even though CPU is not fully utilized.

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The model is using excessive memory due to a memory leak

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The prediction latency is being affected by CPU throttling

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The model is hitting a cold start due to autoscaling

Question 16

Which TWO practices help ensure reproducible ML experiments?

Accepted Answer

Use Vertex AI Experiments to track parameters and metrics. Vertex AI Experiments automatically logs parameters, metrics, and artifacts for each run, creating a complete lineage that enables exact reproduction of results. By tracking these details alongside the code version, you can recreate the exact environment and configuration that produced a given outcome, which is essential for reproducibility.

Answer

Store all artifacts in a temporary bucket

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Use a random seed for each run

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Use preemptible VMs

Question 17

You are using Vertex AI batch prediction and your model requires preprocessing that involves joining two BigQuery tables. The preprocessing logic is complex and must be done before inference. How should you design the pipeline?

Accepted Answer

Use Dataflow to read from both BigQuery tables, perform the join and preprocessing, write the results to GCS, then run Vertex AI batch prediction with GCS source.. Dataflow (Apache Beam) is designed for complex, stateful data processing like joining two BigQuery tables and performing custom preprocessing. It can read from BigQuery, execute the join logic, and write the preprocessed results to Cloud Storage (GCS). Vertex AI batch prediction then reads the preprocessed data from GCS, which is the recommended pattern for non-trivial transformations before inference, as it decouples preprocessing from prediction and avoids resource contention.

Answer

Write a Cloud Composer workflow that runs the preprocessing and then triggers the batch prediction job.

Answer

Use Vertex AI batch prediction with a custom container that includes logic to read and join tables on the fly.

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Use BigQuery to create a materialized view that joins the tables and directly use that as the batch prediction source.

Question 18

A data scientist uses Vertex AI Workbench to train a model and then deploys it to an endpoint. They want to automate the retraining and redeployment pipeline when new data arrives. Which service should they use?

Accepted Answer

Vertex AI Pipelines. (Vertex AI Pipelines) is correct because Vertex AI Pipelines is a serverless, managed pipeline orchestration service that can automate retraining and redeployment pipelines when new data arrives. Option A (Cloud Composer) is a workflow orchestration service but is more complex and not as integrated with Vertex AI. Option C (Cloud Scheduler) is for scheduled jobs, not event-driven retraining. Option D (Cloud Functions) is event-driven but lacks the pipeline capabilities needed for a full retraining and redeployment workflow.

Answer

Cloud Composer

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Cloud Scheduler

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Cloud Functions

Question 19

A company wants to track the cost of their Vertex AI prediction endpoint. They use a custom machine type with 1 n1-standard-4 (4 vCPU, 15 GB memory) and 1 NVIDIA T4 GPU. The endpoint is configured for automatic scaling with min=1, max=5 replicas. Which cost monitoring approach should they use?

Accepted Answer

Use Cloud Billing budget alerts and export cost data to BigQuery for analysis.. Vertex AI prediction costs are composed of per-hour per-replica compute and GPU charges plus per-request usage. The best approach is to use Cloud Billing export to BigQuery and query cost data by service and SKU, or use the Vertex AI cost tables in the console.

Answer

Calculate cost manually based on replica count and GPU hours from endpoint logs.

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Use Vertex AI Experiments to track cost.

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Monitor only the CPU utilisation metrics to infer cost.

Question 20

You are an ML engineer at a large e-commerce company. Your team has developed a product recommendation model using TensorFlow and deployed it on Vertex AI Endpoints for real-time inference. The model is retrained weekly using a Vertex AI Pipeline that reads new user interaction data from BigQuery, trains the model, evaluates it, and deploys the new version to the endpoint with a traffic split: 10% to the new model and 90% to the previous champion model. Recently, the team noticed that the new model's online prediction latency has increased significantly (from 50ms to 200ms) after deployment, causing timeouts for some requests. The training code has not changed, and the model size is similar. The pipeline uses a custom container with the same TensorFlow Serving image as before. The deployment step uses the same machine type (n1-standard-4) for the endpoint. What is the most likely cause of the latency increase?

Accepted Answer

The pipeline now includes a data validation step that modifies the SavedModel's serving signature, adding an extra preprocessing operation.. The pipeline now includes a data validation step that modifies the SavedModel's serving signature, adding an extra preprocessing operation. This additional operation runs during inference on Vertex AI Endpoints, increasing the per-request latency from 50ms to 200ms, even though the model architecture and size remain unchanged. The custom container and machine type are identical, so the latency increase must stem from a change in the serving graph itself.

Answer

The endpoint is using a machine type that is not optimized for the new model's computation.

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The new model has a significantly different architecture that requires more computation.

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The new model is experiencing data skew because the training data distribution has changed.