PMLE Practice Test 3 — 10 Questions

Question 1

An organization uses Cloud Composer to orchestrate ML workflows. A DAG that triggers Vertex AI training jobs fails because the training job exceeds the 7-day maximum runtime. What is the best way to handle long-running training jobs in Cloud Composer?

Accepted Answer

Use Vertex AI Pipeline to manage the training job asynchronously. Option B is correct because Vertex AI Pipelines natively supports asynchronous execution, allowing Cloud Composer to trigger a pipeline and monitor its status without blocking the Airflow worker for the entire duration of the training job. This decouples the DAG execution timeout from the training runtime, enabling workflows that exceed the 7-day Airflow task timeout limit.

Answer

Increase the DAG execution timeout to 14 days in the Airflow configuration

Answer

Refactor the training job to run on Dataflow, which supports longer runtimes

Answer

Set max_active_runs=1 in the DAG to prevent overlapping runs

Question 2

A company deploys a TensorFlow model on Vertex AI Prediction with a single node. During peak hours, inference latency increases. What should they do first to reduce latency?

Accepted Answer

Enable autoscaling for the deployment. Enabling autoscaling for the deployment is the correct first step because it allows Vertex AI Prediction to dynamically adjust the number of replicas based on incoming traffic. During peak hours, autoscaling can add more nodes to distribute the inference load, directly reducing latency without requiring manual intervention or over-provisioning.

Answer

Increase the machine type of the node

Answer

Decrease the min replicas to 0

Answer

Enable automatic batching of requests

Question 3

A company uses Vertex AI Prediction with a custom container for a TensorFlow model. They notice that after deploying a new model version, requests still go to the old version. What is the most likely cause?

Accepted Answer

Traffic is not split to the new model version. In Vertex AI Prediction, when you deploy a new model version to an existing endpoint, you must explicitly allocate traffic to it. By default, the new version receives 0% traffic, so all requests continue to be served by the old version. The correct fix is to update the endpoint's traffic split, for example via the console or the `gcloud ai endpoints update` command with the `--traffic-split` flag.

Answer

The custom container is not compatible with Vertex AI

Answer

The model is cached and needs cache invalidation

Answer

The new model version was not deployed to the same endpoint

Question 4

A company needs to serve a model with strict latency requirements (<100ms). They are using Vertex AI Prediction with CPU. During testing, latency is 150ms. What should they do?

Accepted Answer

Switch to a GPU machine type. The model's latency of 150ms exceeds the 100ms requirement. Switching to a GPU machine type (Option D) is correct because GPUs are optimized for parallel computation, significantly reducing inference latency for many ML models, especially deep learning models, compared to CPUs. Vertex AI Prediction supports GPU machine types, and this change directly addresses the latency bottleneck without altering the model or its serving configuration.

Answer

Enable batching to improve throughput

Answer

Use a smaller machine type with more replicas

Answer

Export the model to TensorFlow Lite

Question 5

You are monitoring a classification model that predicts loan default. The model was trained on data from 2020-2022. In 2023, the economic conditions changed, and the model's accuracy dropped significantly. Which monitoring approach would best help you detect this issue early?

Accepted Answer

Monitor the prediction distribution for significant shift from training distribution. Option C is correct because monitoring the prediction distribution for a significant shift from the training distribution directly detects changes in the model's output behavior, which is the earliest indicator of concept drift or data drift caused by economic changes. Unlike accuracy monitoring, this approach does not require labeled data, enabling real-time detection of performance degradation before ground truth labels become available.

Answer

Monitor the accuracy of the model on the latest batch of labeled data

Answer

Monitor feature distribution drift using KS test

Answer

Monitor the freshness of the training data

Question 6

A retail company wants to forecast weekly sales for each of its 500 stores. The data includes historical sales, promotions, holidays, and local weather. The company needs to update forecasts every week with new data. Which ML approach should they use?

Accepted Answer

Use Vertex AI Forecasting to train a time-series model with holiday and weather features. Vertex AI Forecasting is purpose-built for time-series forecasting with support for exogenous features like holidays and weather, making it the ideal choice for weekly sales predictions across 500 stores. It handles multiple time series automatically and integrates with the required weekly retraining cycle, unlike generic regression models that lack temporal awareness.

Answer

Use BigQuery ML to create a linear regression model on historical data

Answer

Export data to AutoML Tables and train a regression model

Answer

Build a custom LSTM model using TensorFlow on Vertex AI Workbench

Question 7

A retail company wants to build a product recommendation system using BigQuery ML for their e-commerce platform. The data includes customer purchase history, product metadata, and clickstream logs. The ML engineer needs to minimize manual feature engineering and leverage pre-built solutions. Which approach should the engineer take?

Accepted Answer

Use BigQuery ML's matrix factorization model (CREATE MODEL with model_type='matrix_factorization') to train directly on historical interaction data.. Option D is correct because BigQuery ML's matrix factorization model (model_type='matrix_factorization') is purpose-built for recommendation systems using implicit or explicit feedback data. It trains directly on historical interaction data (e.g., user-item purchases) without requiring manual feature engineering, aligning with the goal of minimizing low-code ML effort. This approach leverages BigQuery's native SQL interface and scales automatically, making it ideal for the described e-commerce scenario.

Answer

Use a pre-built recommendation model from Vertex AI Model Garden and deploy it to an endpoint.

Answer

Write a custom TensorFlow model using the Vertex AI Training service and deploy it via Vertex AI Prediction.

Answer

Export the data to CSV and use AutoML Tables to train a recommendation model.

Question 8

A company uses Vertex AI Pipelines to orchestrate their ML training workflow. The pipeline includes a BigQuery ML training step, a model evaluation step, and a deployment step to Vertex AI Endpoints. The engineer notices that the pipeline fails intermittently due to a quota exceeded error on Vertex AI Endpoints during model deployment. What is the best long-term solution to prevent this failure?

Accepted Answer

Add retry logic with exponential backoff to the deployment step in the pipeline.. Option D is correct because implementing retry logic with exponential backoff is a resilient pattern for transient quota errors. Option A is wrong because increasing quota requires a support ticket and may not be granted immediately. Option B is wrong because using a custom container does not address quota limits. Option C is wrong because sequential execution does not prevent quota errors.

Answer

Run the pipeline steps sequentially with longer wait times.

Answer

Switch to deploying models using a custom container on Compute Engine.

Answer

Request a permanent quota increase for Vertex AI Endpoints.

Question 9

A marketing team wants to use a pre-built natural language processing (NLP) model from Vertex AI Model Garden to analyze customer feedback. They need to extract sentiment from text data stored in Cloud Storage. The team has no experience with model serving infrastructure. Which deployment option minimizes operational overhead?

Accepted Answer

Deploy the model to a Vertex AI Endpoint directly from Model Garden.. Option D is correct because deploying directly to a Vertex AI Endpoint from Model Garden eliminates all infrastructure management. Vertex AI handles model serving, scaling, and monitoring automatically, which is ideal for a team with no experience in model serving infrastructure. This is a fully managed, serverless deployment that requires no containerization or server configuration.

Answer

Deploy the model as a Cloud Function invoked by Cloud Storage events.

Answer

Deploy the model as a Cloud Run service using a custom Docker container.

Answer

Deploy the model on App Engine flexible environment.

Question 10

A team wants to share a trained model with other teams within the organization. They need to provide access to the model artifact in Vertex AI Model Registry and ensure that only authorized teams can deploy the model. What should they do?

Accepted Answer

Use IAM to grant the 'aiplatform.models.deploy' role to the other teams on the model resource. Option D is correct because Vertex AI Model Registry uses IAM to control access to model resources. By granting the 'aiplatform.models.deploy' role on the specific model resource, you ensure that only authorized teams can deploy the model, while other operations (like viewing or updating) remain restricted. This follows the principle of least privilege and avoids exposing the model artifact broadly.

Answer

Grant the other teams access to the Cloud Storage bucket where the model is stored

Answer

Set the model to public in Vertex AI Model Registry

Answer

Use Cloud Key Management Service to encrypt the model and share the decryption key