PMLE Practice Test 46 — 15 Questions

Question 1

Which TWO are benefits of using Vertex AI Pipelines for ML workflow orchestration over deploying custom Airflow DAGs in Cloud Composer? (Choose TWO.)

Accepted Answer

Automatic artifact lineage tracking. Vertex AI Pipelines automatically captures and tracks artifact lineage (inputs, outputs, and their relationships) as part of the ML metadata store. This built-in lineage tracking is a key differentiator from custom Airflow DAGs, where you must manually implement artifact tracking using external tools or custom code.

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Managed infrastructure without manual configuration

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Built-in scheduling capabilities

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Support for arbitrary Python code in steps

Question 2

A company uses Vertex AI Model Monitoring to detect training-serving skew. They have a categorical feature 'product_category' with high cardinality. The monitoring job alerts for skew, but the data scientists believe the model performance is still acceptable. Which THREE actions should the team take to investigate and resolve the alert?

Accepted Answer

Examine which categories have the largest distribution changes to understand the nature of the shift.. Examining which categories have the largest distribution changes allows the team to pinpoint the root cause of the training-serving skew. In Vertex AI Model Monitoring, the skew alert is based on statistical distance metrics (e.g., Jensen-Shannon divergence) between training and serving distributions. By drilling down into the specific categories driving the divergence, the team can assess whether the shift is benign (e.g., seasonal) or problematic, rather than relying on aggregate model performance alone.

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Remove the drifted categories from the feature set to eliminate the alert.

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Ignore the alert because the model is performing well; monitoring alerts are often false positives.

Question 3

You are deploying a PyTorch model on Vertex AI using a custom container with NVIDIA Triton Inference Server. The model is a large transformer that requires GPU. You want to optimize GPU utilization and reduce memory footprint. Which technique should you apply?

Accepted Answer

Apply model quantization using TensorRT.. Model quantization using TensorRT reduces the precision of model weights (e.g., from FP32 to FP16 or INT8), which directly decreases GPU memory usage and can improve throughput by enabling faster arithmetic operations on compatible NVIDIA GPUs. This technique is specifically designed to optimize GPU utilization and memory footprint for large transformer models deployed with Triton Inference Server.

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Enable dynamic batching in Triton.

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Use CPU-only instances to avoid GPU memory issues.

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Increase the number of GPU replicas.

Question 4

A company is using AutoML Tables to build a fraud detection model. The dataset has 10 million rows with 100 features, heavily imbalanced (fraud cases 0.1%). They used AutoML Tables with default settings and achieved high precision but very low recall. They need to deploy the model for real-time scoring on a Vertex AI Endpoint. The model will be used by a transaction processing system that requires low latency (<100 ms per prediction) and high throughput. The team is concerned about cost as the endpoint will receive up to 5,000 predictions per second. After deploying the model, they notice that the endpoint's latency occasionally spikes to over 1 second during peak hours. The team wants to optimize both model performance (recall) and serving performance. Which course of action should they take?

Accepted Answer

Retrain the model with adjusted class weights in AutoML Tables to increase recall, then deploy using Vertex AI Prediction with autoscaling enabled.. AutoML Tables allows adjusting class weights to handle imbalanced datasets, which directly addresses the low recall issue by penalizing misclassifications of the minority class more heavily. Deploying on Vertex AI Prediction with autoscaling ensures the endpoint can handle up to 5,000 predictions per second while maintaining low latency, as autoscaling dynamically adjusts resources based on traffic, preventing spikes during peak hours.

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Use BigQuery ML to create a logistic regression model with class weights, then deploy it on Cloud Run with maximum concurrency.

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Export the AutoML Tables model as a TensorFlow SavedModel and deploy it on Vertex AI Prediction with a larger machine type and increased min replicas.

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Use Vertex AI Workbench to manually tune a deep neural network with class imbalance techniques, then deploy as a custom container on App Engine.

Question 5

An ML team is using Vertex AI Pipelines to automate model training and deployment. They want to reuse components across multiple pipelines. What is the best practice for managing component code?

Accepted Answer

Store components as container images in Artifact Registry and reference them from pipelines. Vertex AI Pipelines natively supports reusable components by packaging them as container images stored in Artifact Registry. This allows teams to version, share, and reference components across multiple pipelines without duplicating code, ensuring consistency and reducing maintenance overhead. Container images encapsulate the component's runtime environment and logic, making them portable and independently deployable.

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Define components inline in the pipeline definition

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Embed component code in Cloud Composer DAGs

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Copy the component definitions into each pipeline's YAML file

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Use Cloud Functions to define components

Question 6

A machine learning pipeline includes a conditional branch: if model accuracy exceeds 0.95, deploy to production; otherwise, send a notification. Which KFP SDK feature allows implementing this logic within the pipeline definition?

Accepted Answer

dsl.If. C is correct because KFP SDK's `dsl.If` is the native construct for implementing conditional branching within a pipeline definition, allowing you to conditionally execute a task (like deploying to production) based on the output of a previous component (e.g., model accuracy > 0.95). It works by creating a `Condition` object that wraps the downstream tasks, ensuring the pipeline DAG is correctly compiled and executed only when the condition evaluates to true.

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dsl.ParallelFor

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Using a Python if statement inside the component

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dsl.Condition

Question 7

A team has a pipeline that trains a model and then evaluates it. They want to conditionally deploy the model to a staging endpoint only if evaluation metrics exceed a threshold. Which KFP feature should they use?

Accepted Answer

Use dsl.Condition (deprecated) or dsl.If to check metrics and conditionally run deployment.. KFP provides `dsl.Condition` (deprecated) and `dsl.If` as first-class pipeline constructs to conditionally execute pipeline components based on runtime metrics or other pipeline outputs. By wrapping the deployment step inside a `dsl.If` block that checks whether evaluation metrics exceed a threshold, the pipeline can deploy the model to a staging endpoint only when the condition is met, avoiding unnecessary deployments for underperforming models.

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Use dsl.ParallelFor to evaluate and deploy in parallel.

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Use an exit handler to deploy regardless of metrics.

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Split the pipeline into two separate pipelines and run the second only if metrics are good.

Question 8

A company uses Vertex AI for training. They have a large dataset stored in Cloud Storage and need to train a custom model using TensorFlow. The training job is failing with an out-of-memory error. What is the best first step?

Accepted Answer

Enable data sharding and reduce input pipeline parallelism.. Enabling data sharding and reducing input pipeline parallelism can lower memory usage from data loading, which is a common cause of out-of-memory errors in TensorFlow training on Vertex AI. Option A is wrong because reducing model size may negatively impact accuracy and is not the best first step; it should be considered after addressing data loading issues. Option C is wrong because using a larger machine type increases cost and does not address the root cause of memory inefficiency in data pipeline. Option D is wrong because increasing batch size would increase memory usage, exacerbating the OOM error.

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Reduce model size.

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Use a larger machine type.

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Increase the batch size.

Question 9

An ML engineer is monitoring a model on Vertex AI Endpoint and sees that feature 'age' has a training distribution of (mean=45, std=10) but the serving distribution over the last hour shows (mean=30, std=15). JS divergence is 0.12, but the alert threshold is 0.1. The engineer suspects this is due to a temporary campaign targeting younger users. What should they do first?

Accepted Answer

Use Vertex AI Explainable AI to understand the importance of the 'age' feature for the model.. Before taking action like retraining, the engineer should investigate the root cause. Using Vertex AI Explainable AI to check feature importance can confirm whether 'age' is a critical feature. If it is not important, the drift may be harmless.

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Immediately trigger retraining to avoid model degradation.

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Increase the sampling rate to get more data before making a decision.

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Adjust the alert threshold to 0.2 to avoid false positives.

Question 10

An e-commerce company uses a Vertex AI endpoint for product recommendations. Recently, the click-through rate (CTR) dropped significantly. Model monitoring shows no significant data drift or skew. Logs show increased latency but no errors. Which technique should the engineer use to diagnose the issue?

Accepted Answer

Analyze the prediction output distribution using Vertex AI Model Monitoring for prediction drift and compare to a baseline.. The drop in CTR despite no data drift or skew suggests that the model's predictions have shifted in distribution (prediction drift), even if the input features remain stable. Vertex AI Model Monitoring can compare the current prediction output distribution against a baseline to detect such drift, which directly explains the CTR decline. The increased latency is a symptom, not the root cause, and fixing latency alone would not restore CTR.

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Increase the endpoint's request timeout value to accommodate the higher latency.

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Enable autoscaling on the endpoint to reduce latency by adding more nodes.

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Retrain the model with the most recent user interaction data.

Question 11

What is the primary benefit of using a centralised model registry in MLOps?

Accepted Answer

Governance and version control of models. A centralised model registry provides governance, versioning, and lineage tracking, enabling collaboration and auditability.

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Better hyperparameter tuning

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Faster model training

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Automatic model deployment

Question 12

A data scientist wants to quickly train a binary classification model on a tabular dataset stored in BigQuery without writing any code. They have limited ML experience. Which Google Cloud service should they use?

Accepted Answer

BigQuery ML with CREATE MODEL statement using SQL.. BigQuery ML allows a data scientist to train a binary classification model directly in BigQuery using a `CREATE MODEL` SQL statement, without writing any code or moving data. This is the fastest low-code approach for users with limited ML experience, as it leverages familiar SQL syntax and runs entirely within BigQuery's serverless infrastructure.

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Vertex AI Workbench with a built-in scikit-learn notebook.

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Dataflow with a TensorFlow pipeline.

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AutoML Tables with a direct BigQuery connection.

Question 13

A data scientist deployed a TensorFlow model for sentiment analysis to Vertex AI Prediction. The model expects input key 'text' but the client sends requests with key 'review_text'. Which step should the data scientist take to resolve the error without retraining the model?

Accepted Answer

Modify the client code to send requests with input key 'text'. The most straightforward and reliable solution is to modify the client code to send the request with the expected input key 'text'. This avoids any additional infrastructure, latency, or complexity, and does not require retraining the model or altering the deployed endpoint. Vertex AI Prediction serves the model as-is, so aligning the client's request format with the model's expected input is the simplest and most maintainable fix.

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Use a Cloud Function to strip the 'review_text' key and replace it with 'text'

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Retrain the model with input key 'review_text'

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Create a new Vertex AI Endpoint with an alias mapping 'review_text' to 'text'

Question 14

A financial services company deploys a model on Vertex AI Endpoints with GPU acceleration. They notice that the p99 latency for predictions has increased from 200ms to 1.2s over the past week. CPU utilisation is low, but GPU utilisation is high. Which action should they take to reduce latency?

Accepted Answer

Increase the number of replicas in the endpoint deployment.. High GPU utilisation with low CPU utilisation suggests the model is compute-bound on GPU. Scaling out by adding more replicas distributes the load, reducing queuing and latency.

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Increase the CPU machine type for the endpoint.

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Switch to a more powerful GPU type (e.g., from T4 to A100).

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Reduce the sampling rate for monitoring to free up resources.

Question 15

A retailer wants to implement a recommendation engine that suggests products based on a user's current cart. They have limited ML expertise and want a quick deployment. Which Recommendations AI model type should they use?

Accepted Answer

Frequently bought together. The 'Frequently bought together' model type is the correct choice because it directly leverages collaborative filtering based on co-purchase patterns in historical transaction data, enabling the retailer to recommend items commonly purchased alongside the current cart contents. This model requires minimal ML expertise and can be quickly deployed using pre-built Recommendations AI templates, as it does not require user-level personalization or real-time session data.

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