PMLE Practice Test 44 — 15 Questions

Question 1

A retailer uses BigQuery ML to build a linear regression model for sales forecasting. The model's evaluation shows high RMSE. Which step should they take first?

Accepted Answer

Examine the data for outliers and missing values. High RMSE in a linear regression model often indicates issues with data quality, such as outliers or missing values, which can disproportionately skew the model's predictions. BigQuery ML's linear regression is sensitive to such anomalies, so examining and cleaning the data is the most appropriate first step before considering model complexity or feature engineering.

Answer

Use a more complex model like XGBoost

Answer

Increase the number of features

Answer

Set a larger training budget

Question 2

An MLOps team wants to set up alerts for GPU memory utilization on Vertex AI Training jobs. Which approach is most efficient?

Accepted Answer

Use the 'compute.googleapis.com/accelerator/memory_utilization' metric with a metric threshold condition.. Vertex AI training jobs automatically export the 'compute.googleapis.com/accelerator/memory_utilization' metric to Cloud Monitoring. This metric is natively collected by the Google Cloud agent on the training VM, so you can directly create a metric threshold alert without any custom instrumentation or log parsing. It is the most efficient approach as it requires zero additional code or configuration.

Answer

Enable Cloud Audit Logs for the training job and parse the logs for GPU memory events.

Answer

Create a log-based metric from the training job's GPU logs.

Answer

Add a container sidecar that emits a custom metric for GPU memory usage via OpenCensus.

Question 3

A company runs batch predictions on Vertex AI every hour using a custom container. They want to reduce costs by minimizing idle time while ensuring the batch job completes within 10 minutes. Which endpoint configuration should they use?

Accepted Answer

Use Vertex AI batch prediction job with a custom service account and set machine_type to 'n1-standard-4' and batch_size to optimize throughput.. Vertex AI batch prediction jobs are designed for high-throughput, asynchronous processing of large datasets without maintaining persistent infrastructure. By tuning `machine_type` and `batch_size`, you can minimize idle time and ensure the job completes within the 10-minute window, as the job only runs while actively processing and scales resources as needed.

Answer

Use Vertex AI online prediction with a load balancer in front to distribute requests.

Answer

Create a Dataflow pipeline to read from BigQuery and write predictions to GCS, using the trained model as a side input.

Answer

Deploy the model to an endpoint with min_replicas=0 and max_replicas=10, then send batch requests to the endpoint.

Question 4

A data scientist wants to perform feature engineering on a large dataset stored in BigQuery before training a model. Which feature engineering tool is most appropriate?

Accepted Answer

Use BigQuery ML TRANSFORM clause. The most appropriate tool is the BigQuery ML TRANSFORM clause (Option D). This allows feature engineering directly within BigQuery using SQL, without moving data. Option A (Vertex AI Feature Store) is for storing and serving features, not for computing them. Option B (Cloud Dataproc) is for Hadoop/Spark workloads, which would require exporting data from BigQuery. Option C (Dataflow) is a pipeline service for batch and stream processing, but the TRANSFORM clause is simpler and more integrated for this use case.

Answer

Use Vertex AI Feature Store to store engineered features

Answer

Export data to Cloud Dataproc for feature engineering

Answer

Create a Dataflow pipeline to compute features

Question 5

Your company uses a custom container for model serving on Vertex AI. After a recent update, the model returns predictions but they are clearly wrong (e.g., negative probabilities for a classification model). The logs show no errors. What is the most likely cause?

Accepted Answer

The preprocessing code in the container was updated but the model was not retrained on the new preprocessing. The most likely cause of a model returning predictions without errors, but with clearly wrong outputs like negative probabilities, is a mismatch between the preprocessing logic used during training and inference. If the preprocessing code in the container was updated (e.g., scaling, normalization, or feature engineering steps changed) but the model was not retrained on data processed with that new logic, the model receives inputs that are out of distribution, leading to nonsensical outputs. Vertex AI containers run inference with the deployed code, so any change in preprocessing directly affects the input tensor values without raising runtime errors.

Answer

The model file is corrupted

Answer

The model file was accidentally replaced with a different model

Answer

The container is using an incompatible version of the serving framework

Question 6

A team has successfully trained a deep learning model on Vertex AI using a custom container and distributed training with TensorFlow. They want to serve this model for online predictions with low latency. They deploy the model to Vertex AI Endpoint with a single n1-standard-4 machine. During load testing, they observe that the median latency is 200ms, but the 99th percentile latency spikes to 2 seconds. The model is a complex neural network that takes variable-length text as input. Which approach will best reduce tail latency while maintaining throughput?

Accepted Answer

Implement request batching to process multiple inputs per request.. Request batching (Option B) is the best approach to reduce tail latency for variable-length text inputs because it amortizes the fixed overhead of processing multiple predictions together, reducing per-request latency variability. Option A (autoscaling) helps handle increased traffic but does not reduce per-request latency spikes. Option C (GPU) can improve throughput but may not reduce tail latency caused by variability in input lengths. Option D (more memory) addresses memory constraints but not compute-bound variability.

Answer

Use autoscaling with a target CPU utilization of 70%.

Answer

Use a GPU machine type like n1-standard-4 with an attached GPU.

Answer

Increase the machine type to n1-highmem-8 to allocate more memory.

Question 7

Which TWO of the following are low-code machine learning solutions on Google Cloud?

Accepted Answer

BigQuery ML. BigQuery ML (D) is a low-code ML solution because it allows users to create, train, and deploy machine learning models using standard SQL queries directly within BigQuery, eliminating the need for custom coding in Python or other programming languages. Vertex AI AutoML (E) is also low-code as it provides a graphical interface and automated pipeline to train high-quality models with minimal manual intervention, handling feature engineering, model selection, and hyperparameter tuning automatically.

Answer

TensorFlow

Answer

scikit-learn

Answer

PyTorch

Question 8

A data science team uses Vertex AI Pipelines to orchestrate ML training. They notice that some pipeline runs are failing because of inconsistent data schemas. They want to enforce schema validation as a gate before the training step executes. Which approach should they implement?

Accepted Answer

Add a pipeline component that runs schema validation using the TensorFlow Data Validation library.. The TensorFlow Data Validation (TFDV) library is specifically designed for ML pipeline schema validation. By adding a custom pipeline component that uses TFDV, the team can validate incoming data schemas against a predefined schema directly within the Vertex AI Pipelines orchestration, acting as a gate before the training step executes. This approach integrates seamlessly with the pipeline's component-based architecture and provides detailed anomaly reports.

Answer

Use Cloud Dataflow to validate schema during data ingestion before the pipeline starts.

Answer

Use BigQuery schema enforcement when importing data.

Answer

Use TFX ExampleGen with schema_gen to automatically generate and enforce schemas.

Question 9

Refer to the exhibit. A team member complains they cannot deploy a model to Vertex AI Endpoints. What is the most likely reason?

Accepted Answer

The policy lacks `roles/aiplatform.deployer`. Deploying a model to Vertex AI Endpoints requires the `roles/aiplatform.deployer` role on the service account. This role grants the necessary permissions to create and manage endpoint deployments. Without it, the deployment operation will fail with an access denied error, even if other roles are present.

Answer

The policy is missing a condition

Answer

The policy lacks `roles/aiplatform.specialist`

Answer

The service account needs `roles/aiplatform.user`

Question 10

A machine learning team is collaborating on a project using Vertex AI Experiments to track model training runs. They want to ensure that all team members can reproduce any experiment by using the same code, data, and environment. Which THREE actions should the team take?

Accepted Answer

Store the training code in a Cloud Source Repository and tag commits with the experiment ID.. Storing training code in a Cloud Source Repository with tags linked to experiment IDs ensures that every team member can retrieve the exact code version used for a given experiment. This is a core reproducibility practice in Vertex AI Experiments, where the code snapshot is a key component of the experiment lineage.

Answer

Share a service account key with all team members so they can access the same resources.

Answer

Use Vertex AI's hyperparameter tuning job to automatically find the best parameters.

Question 11

A large e-commerce company uses Vertex AI to train a recommendation model daily. The training pipeline is built with Vertex AI Pipelines and involves three steps: data preprocessing, training, and model evaluation. The pipeline is triggered by a Cloud Scheduler job every morning at 8 AM. Recently, the pipeline has been failing intermittently during the data preprocessing step, with an error message indicating 'ResourceExhausted: Quota limits exceeded for read api requests.' The team has checked and confirmed that the quota for BigQuery read requests is not exceeded at the project level. The preprocessing step reads data from a BigQuery table with billions of rows. The team has also noticed that the pipeline runs on a custom machine type (n1-standard-4) with a persistent disk. What is the most likely cause of this error?

Accepted Answer

The preprocessing component is using a BigQuery client library that does not use exponential backoff for retries.. The error 'ResourceExhausted: Quota limits exceeded for read api requests' indicates that the BigQuery API is throttling requests from the client, even though the project-level quota is not exceeded. The preprocessing component likely uses a BigQuery client library that lacks exponential backoff retry logic, causing rapid, repeated requests that exhaust the per-client or per-connection quota. Implementing exponential backoff would allow the client to back off and retry, preventing quota exhaustion.

Answer

The BigQuery table is partitioned on a date column, and the pipeline is querying a specific partition that exceeds the quota.

Answer

The Cloud Scheduler job is triggering multiple pipeline runs that overlap, causing concurrent quota usage.

Answer

The pipeline is using a shared VPC that has traffic shaping limits.

Question 12

A machine learning engineer is using Vertex AI Pipelines and wants to run a custom Python function as a component. They need to pass a dataset artifact from a previous component and output a model artifact. Which decorator should they use to define the component in the Kubeflow Pipelines SDK v2?

Accepted Answer

@dsl.component. The correct decorator is @dsl.component because in Kubeflow Pipelines SDK v2, this decorator is used to define a custom Python function as a reusable pipeline component. It automatically handles input and output artifact serialization, such as passing a dataset artifact from a previous component and outputting a model artifact, by leveraging the component's type annotations and the KFP artifact system.

Answer

@dsl.task

Answer

@dsl.pipeline

Answer

@dsl.container

Question 13

A company wants to implement a CI/CD pipeline for their ML models using Vertex AI. They need to automatically retrain the model when new data arrives, but only if the model performance on a validation set has degraded by more than 5% compared to the current production model. Which three services or components should they incorporate into the automated pipeline? (Choose three.)

Accepted Answer

Vertex AI Evaluation component to compute model performance metrics on the validation set. Vertex AI Evaluation component can be used within a pipeline to compute model performance metrics (e.g., accuracy, precision, recall) on a validation set. This allows the pipeline to compare the newly trained model's performance against the current production model's performance, enabling the conditional logic to check if degradation exceeds 5%.

Answer

Dataflow pipeline to clean the new data before training

Answer

Cloud Scheduler to run the pipeline on a fixed schedule

Question 14

A marketing agency uses Vertex AI AutoML Vision to classify social media images into brand logos and generic content. They have 5,000 images per class. The model achieves 95% accuracy on validation set, but in production it misclassifies many images that contain logos in unusual angles or lighting. They have limited ML expertise and want to improve robustness. Which action should they take?

Accepted Answer

Augment the training set with images that have varied angles and lighting.. The core issue is a domain shift between the training data (likely clean, canonical logo images) and production data (logos at unusual angles and lighting). Augmenting the training set with those specific variations directly addresses the lack of robustness by exposing the model to the missing edge cases during training, which is the most effective and simplest fix for a team with limited ML expertise using AutoML Vision.

Answer

Switch to a custom CNN model trained with data augmentation.

Answer

Deploy the model with a lower confidence threshold.

Answer

Use Vertex AI Matching Engine for similarity search instead.

Question 15

A data science team is deploying a PyTorch model for real-time inference using Vertex AI Endpoints. The model requires a custom container with specific CUDA drivers and Python packages. They have created a Docker image and pushed it to Artifact Registry. The pipeline should automatically retrain the model every week and deploy the new version if it passes validation. However, the deployment step fails intermittently with the error 'The container image is not compatible with the machine type.' What is the most likely cause?

Accepted Answer

The container image requires GPU support but the machine type specified in the endpoint is a CPU-only machine.. The error 'The container image is not compatible with the machine type' indicates a mismatch between the container's hardware requirements and the machine type selected for the Vertex AI Endpoint. Since the custom container requires specific CUDA drivers, it is built for GPU acceleration. If the endpoint is configured with a CPU-only machine type (e.g., n1-standard-4), the container will fail to run because the GPU drivers cannot initialize, triggering this incompatibility error.

Answer

The service account does not have permission to pull the container from Artifact Registry.

Answer

The container's health check endpoint is not responding correctly.

Answer

The model artifact size exceeds the maximum allowed for the machine type.