Google Professional-Machine-Learning-Engineer Exam Practice Test Instant Access

Question 1

You are implementing a batch inference ML pipeline in Google Cloud. The model was developed by using TensorFlow and is stored in SavedModel format in Cloud Storage. You need to apply the model to a historical dataset that is stored in a BigQuery table. You want to perform inference with minimal effort. What should you do?

AImport the TensorFlow model by using the create model statement in BigQuery ML. Apply the historical data to the TensorFlow model.

BExport the historical data to Cloud Storage in Avro format. Configure a Vertex Al batch prediction job to generate predictions for the exported data.

CExport the historical data to Cloud Storage in CSV format. Configure a Vertex Al batch prediction job to generate predictions for the exported data.

DConfigure and deploy a Vertex Al endpoint. Use the endpoint to get predictions from the historical data inBigQuery.

Answer : B

Vertex AI batch prediction is the most appropriate and efficient way to apply a pre-trained model like TensorFlow's SavedModel to a large dataset, especially for batch processing.

The Vertex AI batch prediction job works by exporting your dataset (in this case, historical data from BigQuery) to a suitable format (like Avro or CSV) and then processing it in Cloud Storage where the model is stored.

Avro format is recommended for large datasets as it is highly efficient for data storage and is optimized for read/write operations in Google Cloud, which is why option B is correct.

Option A suggests using BigQuery ML for inference, but it does not support running arbitrary TensorFlow models directly within BigQuery ML. Hence, BigQuery ML is not a valid option for this particular task.

Option C (exporting to CSV) is a valid alternative but is less efficient compared to Avro in terms of performance.

Question 2

Your company needs to generate product summaries for vendors. You evaluated a foundation model from Model Garden for text summarization but found that the summaries do not align with your company's brand voice. How should you improve this LLM-based summarization model to better meet your business objectives?

AIncrease the model's temperature parameter.

BFine-tune the model using a company-specific dataset.

CTune the token output limit in the response.

DReplace the pre-trained model with another model in Model Garden.

Answer : B

Fine-tuning the model with a company-specific dataset aligns the model outputs with the brand voice, making it better suited for the company's objectives. Adjusting the temperature (Option A) affects randomness rather than content style, and changing token limits (Option C) does not impact tone. Replacing the model (Option D) is inefficient without guarantees of better alignment.

Question 3

You work as an ML researcher at an investment bank and are experimenting with the Gemini large language model (LLM). You plan to deploy the model for an internal use case and need full control of the model's underlying infrastructure while minimizing inference time. Which serving configuration should you use for this task?

ADeploy the model on a Vertex AI endpoint using one-click deployment in Model Garden.

BDeploy the model on a Google Kubernetes Engine (GKE) cluster manually by creating a custom YAML manifest.

CDeploy the model on a Vertex AI endpoint manually by creating a custom inference container.

DDeploy the model on a Google Kubernetes Engine (GKE) cluster using the deployment options in Model Garden.

Answer : B

Deploying the model on GKE with a custom YAML manifest allows maximum control over infrastructure and latency, aligning with the need for low inference time and internal model use. Vertex AI's one-click deployment (Option A) limits control, and deploying on Vertex AI (Option C) doesn't allow for as much customization as a GKE setup.

Question 4

You are the lead ML engineer on a mission-critical project that involves analyzing massive datasets using Apache Spark. You need to establish a robust environment that allows your team to rapidly prototype Spark models using Jupyter notebooks. What is the fastest way to achieve this?

AConfigure a Compute Engine instance with Spark and use Jupyter notebooks.

BSet up a Dataproc cluster with Spark and use Jupyter notebooks.

CSet up a Vertex AI Workbench instance with a Spark kernel.

DUse Colab Enterprise with a Spark kernel.

Answer : B

Dataproc provides a managed Spark environment and integrates with Jupyter notebooks, ideal for large datasets and rapid prototyping. It reduces setup time compared to manual Spark configurations on Compute Engine or Vertex AI. Colab Enterprise is more suitable for small-scale prototyping rather than extensive Spark-based analysis.

Question 5

You have created multiple versions of an ML model and have imported them to Vertex AI Model Registry. You want to perform A/B testing to identify the best-performing model using the simplest approach. What should you do?

ASplit incoming traffic among separate Cloud Run instances of deployed models. Monitor the performance of each version using Cloud Monitoring.

BSplit incoming traffic to distribute prediction requests among the versions. Monitor the performance of each version using Looker Studio dashboards that compare logged data for each version.

CSplit incoming traffic among Google Kubernetes Engine (GKE) clusters and use Traffic Director to distribute prediction requests to different versions. Monitor the performance of each version using Cloud Monitoring.

DSplit incoming traffic to distribute prediction requests among the versions. Monitor the performance of each version using Vertex AI's built-in monitoring tools.

Answer : D

Vertex AI Model Registry supports traffic splitting and built-in monitoring, making A/B testing seamless. This approach eliminates the need for additional monitoring tools and infrastructure overhead. Cloud Run and GKE solutions (Options A and C) add unnecessary complexity, while Looker Studio (Option B) requires additional configuration for monitoring.

Question 6

You have developed a fraud detection model for a large financial institution using Vertex AI. The model achieves high accuracy, but stakeholders are concerned about potential bias based on customer demographics. You have been asked to provide insights into the model's decision-making process and identify any fairness issues. What should you do?

AEnable Vertex AI Model Monitoring to detect training-serving skew. Configure an alert to send an email when the skew or drift for a model's feature exceeds a predefined threshold. Retrain the model by appending new data to existing training data.

BCompile a dataset of unfair predictions. Use Vertex AI Vector Search to identify similar data points in the model's predictions. Report these data points to the stakeholders.

CUse feature attribution in Vertex AI to analyze model predictions and the impact of each feature on the model's predictions.

DCreate feature groups using Vertex AI Feature Store to segregate customer demographic features and non-demographic features. Retrain the model using only non-demographic features.

Answer : C

Feature attribution helps to determine how each feature influences predictions, essential for identifying bias. Vertex AI's built-in explainability tools provide insights without altering the model's feature space. Model monitoring (Option A) detects distributional drift rather than feature influence. Options B and D do not directly address the request to explain model decisions or provide fairness insights.

Question 7

You trained a model on data stored in a Cloud Storage bucket. The model needs to be retrained frequently in Vertex AI Training using the latest data in the bucket. Data preprocessing is required prior to retraining. You want to build a simple and efficient near-real-time ML pipeline in Vertex AI that will preprocess the data when new data arrives in the bucket. What should you do?

ACreate a pipeline using the Vertex AI SDK. Schedule the pipeline with Cloud Scheduler to preprocess the new data in the bucket. Store the processed features in Vertex AI Feature Store.

BCreate a Cloud Run function that is triggered when new data arrives in the bucket. The function initiates a Vertex AI Pipeline to preprocess the new data and store the processed features in Vertex AI Feature Store.

CBuild a Dataflow pipeline to preprocess the new data in the bucket and store the processed features in BigQuery. Configure a cron job to trigger the pipeline execution.

DUse the Vertex AI SDK to preprocess the new data in the bucket prior to each model retraining. Store the processed features in BigQuery.

Answer : B

Cloud Run can be triggered on new data arrivals, which makes it ideal for near-real-time processing. The function then initiates the Vertex AI Pipeline for preprocessing and storing features in Vertex AI Feature Store, aligning with the retraining needs. Cloud Scheduler (Option A) is suitable for scheduled jobs, not event-driven triggers. Dataflow (Option C) is better suited for batch processing or ETL rather than ML preprocessing pipelines.

Google Professional Machine Learning Engineer Exam Practice Test