PMLE Practice Test 40 — 15 Questions

Question 1

A company uses BigQuery ML with a remote model calling Vertex AI's pre-trained image classification model. They need to classify images stored in Cloud Storage buckets. What is the correct approach?

Accepted Answer

Create a remote model with model_type='VERTEX_AI' and use ML.PREDICT with image URIs.. BigQuery ML remote models allow you to invoke Vertex AI pre-trained models via the `model_type='VERTEX_AI'` setting. You can then use `ML.PREDICT` directly on Cloud Storage image URIs without needing to export or transform the image data, as BigQuery ML handles the URI resolution and passes the image to Vertex AI for classification.

Answer

Train a custom model in BigQuery ML with image data.

Answer

Export images to BigQuery as base64 and then use ML.PREDICT.

Answer

Use ML.PREDICT with IMAGE data type directly.

Question 2

You are deploying a PyTorch model for online predictions on Vertex AI. The model expects input tensors and performs GPU-accelerated inference. You want to minimize prediction latency and maximize throughput. Which approach should you use?

Accepted Answer

Deploy using a prebuilt PyTorch serving container with NVIDIA Triton Inference Server.. NVIDIA Triton Inference Server provides advanced features like dynamic batching, concurrent model execution, and GPU scheduling that maximize throughput and minimize latency for GPU-accelerated inference. Vertex AI's prebuilt PyTorch serving container with Triton is specifically designed to handle online prediction workloads efficiently, outperforming a plain custom container without an inference server.

Answer

Package the model in a custom container without any inference server.

Answer

Use Vertex AI Model Optimization to quantize the model to FP16 and deploy using the optimized model.

Answer

Use batch prediction instead of online prediction to reduce latency.

Question 3

You are using tf.Transform to preprocess data for a TensorFlow model. You want to ensure that the same transformations applied during training are also applied during serving. Which THREE components are necessary to achieve this?

Accepted Answer

Use the tf.Transform analyze_and_transform function on the training data. `tf.Transform.analyze_and_transform` computes the full-pass statistics (e.g., mean, variance, vocabulary) needed for consistent preprocessing and applies the transformation to the training data. This function produces a `tf.Transform` graph that captures the exact operations, ensuring the same transformation logic is available for both training and serving.

Answer

Store raw data in BigQuery for serving

Answer

Duplicate the preprocessing code in the serving application

Question 4

A model deployed on Vertex AI Endpoints shows increasing prediction latency. What is the most scalable way to reduce latency?

Accepted Answer

Enable autoscaling with min nodes increased. Increasing the minimum number of nodes in autoscaling ensures that a baseline of compute capacity is always ready to handle requests, reducing cold-start latency. This is the most scalable approach because it allows the endpoint to dynamically scale up during traffic spikes while maintaining a floor of pre-warmed instances, directly addressing prediction latency without over-provisioning.

Answer

Switch to a larger machine type

Answer

Use batch prediction instead

Answer

Deploy multiple model versions

Question 5

Refer to the exhibit. A data scientist trained a BigQuery ML classification model to detect fraudulent transactions. The dataset has 95% non-fraud (class 0) and 5% fraud (class 1). The evaluation metrics show high accuracy (0.91) but low recall (0.60) for fraud detection. Which low-code approach should the data scientist take to improve recall without significantly sacrificing precision?

Accepted Answer

Use the ML.PREDICT function with a lower classification threshold (e.g., 0.3 instead of 0.5) to capture more positive cases.. Lowering the classification threshold in ML.PREDICT (e.g., from 0.5 to 0.3) causes the model to classify more transactions as fraud, directly increasing recall. This is a low-code adjustment that does not require retraining or complex feature engineering, and it allows the data scientist to trade off precision for recall as needed.

Answer

Apply feature selection to reduce the number of features and focus on the most predictive ones.

Answer

Increase the number of training iterations by setting the MAX_ITERATIONS option to a higher value.

Answer

Re-train the model using AutoML Tables with class weights to penalize false negatives more heavily.

Question 6

Refer to the exhibit. A team runs this Vertex AI Pipeline definition but the deploy component never executes, even though the evaluate step outputs a metric of 0.9. What is the most likely cause?

Accepted Answer

The condition should reference the evaluate component's output directly instead of using an input variable.. In Vertex AI Pipelines, the `condition` block evaluates a boolean expression at pipeline compile time, not runtime. If the condition references an input variable rather than the actual output of the `evaluate` component, the pipeline will use the default or placeholder value (often `False`), causing the deploy step to be skipped even when the runtime metric is 0.9. The condition must directly reference the `evaluate` component's output (e.g., `evaluate.outputs['metric']`) to be evaluated correctly at runtime.

Answer

The deploy component depends on the gate component, but the gate is not producing an output.

Answer

The deploy container image does not exist.

Answer

The evaluate component must be run before train, but the pipeline order is incorrect.

Answer

The pipeline should use a custom component for the condition instead of the built-in type.

Question 7

You are responsible for monitoring a batch prediction pipeline that runs daily. Recently, the pipeline started failing intermittently with out-of-memory errors. The input data volume has not changed. What is the most likely cause?

Accepted Answer

A recent code change that loads the entire dataset into memory before processing. A code change that loads the entire dataset into memory before processing would directly cause out-of-memory (OOM) errors, even if the input data volume remains unchanged. In batch prediction pipelines, data is typically streamed or processed in chunks to manage memory efficiently. A change that bypasses this pattern and loads all data at once can exceed the available heap or container memory, leading to intermittent failures depending on data characteristics or concurrent loads.

Answer

Increase in model size due to retraining

Answer

Decrease in the number of worker machines

Answer

Increase in input data size

Question 8

A data science team has trained a TensorFlow model and wants to serve it online with minimal latency. Which Vertex AI deployment option should they use to ensure the model can handle traffic spikes without manual scaling?

Accepted Answer

Deploy the model to a Vertex AI Endpoint with automatic scaling.. Vertex AI Endpoints with automatic scaling (option B) are designed for online serving with minimal latency and can automatically adjust the number of replicas based on traffic load, handling spikes without manual intervention. This is the correct choice for a TensorFlow model requiring real-time inference and elastic scaling.

Answer

Use Vertex AI Model Garden.

Answer

Use Vertex AI Batch Prediction for offline inference.

Answer

Deploy the model to a Compute Engine VM with a load balancer.

Question 9

A company uses Vertex AI Pipelines with prebuilt components for data processing, training, and deployment. They need to integrate a custom validation step written in Python. What is the correct way to include this as a component?

Accepted Answer

Create a custom component using the Vertex AI Pipelines SDK @component decorator. The Vertex AI Pipelines SDK provides a `@component` decorator that allows you to define a custom Python function as a pipeline component. This decorator automatically handles packaging the Python code into a container image, generating the component specification, and integrating it seamlessly with the pipeline orchestration engine. It is the idiomatic and recommended way to add custom validation logic without manually managing Docker or infrastructure.

Answer

Package the code in a Docker container and reference it as a custom job

Answer

Define the step in the YAML pipeline definition using arbitrary Python commands

Answer

Use a Cloud Function as a pipeline step

Answer

Write a standalone Python script and call it using a Cloud Shell step

Question 10

A team uses Cloud Composer to orchestrate a complex ML pipeline with many tasks. They notice that the DAG parsing time is very high, causing delays in task scheduling. Which action would most effectively reduce DAG parsing time?

Accepted Answer

Optimize DAG files to avoid heavy top-level imports and database queries. Heavy top-level imports and database queries in DAG files are executed every time the scheduler parses the DAG, which happens frequently (default every 30 seconds). By moving imports inside Python callables or using lazy loading, the parsing time is drastically reduced, allowing the scheduler to process DAGs faster and trigger tasks without delay.

Answer

Remove all DAG files that are not currently needed from the bucket

Answer

Increase the parallelism of the Airflow scheduler

Answer

Combine all DAGs into a single file

Question 11

An organization uses Cloud Dataflow to preprocess training data. Dataflow jobs are often failing because of insufficient quota for certain resources. The team has requested a quota increase, but the jobs still fail with 'quota exceeded' errors for a different resource. They want to proactively monitor and manage quotas to avoid failures. What is the best approach?

Accepted Answer

Set up Cloud Monitoring alerts for quota usage and automate quota increase requests.. Setting up Cloud Monitoring alerts for quota usage and automating quota increase requests helps catch issues before failures occur. Option B might reduce resource consumption but does not address the root cause of quota limits. Option C is not feasible. Option D could worsen the problem by requiring more resources.

Answer

Configure Dataflow to use a different pipeline type that avoids the quota.

Answer

Use Dataflow's autoscaling feature to reduce resource usage.

Answer

Increase the maximum number of workers in the Dataflow job.

Question 12

Which of the following is a benefit of using Vertex AI Endpoints with autoscaling and scale-to-zero?

Accepted Answer

It reduces costs by scaling down to zero replicas when no requests are received.. Vertex AI Endpoints with autoscaling and scale-to-zero allow the number of serving replicas to dynamically adjust based on incoming traffic. When no requests are received, the endpoint can scale down to zero replicas, meaning you are not charged for idle compute resources. This directly reduces operational costs compared to maintaining a minimum number of always-on instances.

Answer

It eliminates the need for a load balancer.

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It reduces model training time.

Answer

It automatically upgrades the model version.

Question 13

A large financial company uses a complex ML pipeline to detect fraudulent transactions. The pipeline consists of multiple steps: data ingestion from Pub/Sub, feature engineering using Dataflow, model training with Vertex AI, and deployment to an endpoint. They currently use Cloud Composer to orchestrate the pipeline with separate DAGs for each step. Recently, they have been experiencing failures in the Dataflow job due to schema changes in the incoming transactions, causing the pipeline to stall. The team manually fixes the schema and re-runs the pipeline, which is time-consuming. They want to improve the robustness of the pipeline. The pipeline is run on a schedule but also triggered by the arrival of new data. The team is considering moving to Vertex AI Pipelines to unify the workflow. They also want to automatically detect schema changes and handle them without manual intervention. Which approach should they take?

Accepted Answer

Migrate to Vertex AI Pipelines and add a pre-processing step that validates incoming data schema against a schema registry; if schema change is detected, the pipeline sends an alert and uses a default schema to continue processing. It directly addresses the need for automated schema change detection and handling within a unified orchestration framework. By migrating to Vertex AI Pipelines, the team gains a managed, end-to-end ML workflow service that can include a pre-processing step to validate incoming data against a schema registry. When a schema change is detected, the pipeline can automatically apply a default schema and continue, eliminating manual intervention and reducing downtime.

Answer

Keep using Cloud Composer but add retries with exponential backoff to the Dataflow task, and set up a Cloud Monitoring alert to notify the team if the task fails repeatedly

Answer

Use Cloud Scheduler to trigger the pipeline more frequently to reduce the impact of failures

Answer

Create a separate Dataflow pipeline to handle schema detection and run it before the main pipeline; if schema changes, send an email to the team

Question 14

A data scientist wants to use Vertex AI Pipelines to automate a low-code ML workflow. Which two statements are correct regarding best practices? (Choose TWO.)

Accepted Answer

Use pre-built components from Google's curated component library to avoid custom code.. Vertex AI Pipelines offers a curated library of pre-built components that encapsulate common ML tasks (e.g., data preprocessing, training, evaluation). Using these components reduces the need for custom code, aligning with the low-code ML workflow requirement. This approach accelerates development while maintaining reliability through Google-tested implementations.

Answer

Avoid using pre-built components because they are not customizable.

Answer

Use the Vertex AI Experiments to track and compare pipeline runs.

Answer

Use the Kubeflow Pipelines SDK to define the pipeline, which requires extensive coding.

Question 15

A team wants to share feature definitions across multiple projects in their organization using Vertex AI Feature Store. What is the recommended approach?

Accepted Answer

Use a centralized feature store in a shared project and grant access to other projects via IAM. Vertex AI Feature Store supports cross-project sharing by registering the feature store at the organization level, allowing features to be accessed from different projects.

Answer

Export features to BigQuery datasets in each project

Answer

Use Vertex AI Feature Store's feature view for cross-project access

Answer

Create separate feature stores in each project and synchronize them with Dataflow