support@Free-Braindumps.com
Register Login
Cisco
CompTIA
ISC
EC-Council
EXIN
Fortinet
ITIL
Juniper
Microsoft
VMware
Avaya
SAP
Google
NVIDIA
Salesforce
Amazon
Palo Alto Networks
ISC2
CrowdStrike
Anthropic
All Vendors
  1. Home
  2. Exams
  3. Google
  4. PROFESSIONAL MACHINE LEARNING ENGINEER

Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 26–30 (page: 7)

Updated On: 25-Oct-2026
Page 7 of 82
PDF with 402 Questions

You work at an organization that maintains a cloud-based communication platform that integrates conventional chat, voice, and video conferencing into one platform. The audio recordings are stored in Cloud Storage. All recordings have an 8 kHz sample rate and are more than one minute long. You need to implement a new feature in the platform that will automatically transcribe voice call recordings into a text for future applications, such as call summarization and sentiment analysis. How should you implement the voice call transcription feature following Google-recommended best practices?

  1. Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with synchronous recognition.
  2. Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.
  3. Upsample the audio recordings to 16 kHz, and transcribe the audio by using the Speech-to-Text API with synchronous recognition.
  4. Upsample the audio recordings to 16 kHz, and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.

Answer(s): B



You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI.
What should you do?

  1. Train local surrogate models to explain individual predictions.
  2. Configure sampled Shapley explanations on Vertex Explainable AI.
  3. Configure integrated gradients explanations on Vertex Explainable AI.
  4. Measure the effect of each feature as the weight of the feature multiplied by the feature value.

Answer(s): B



You are developing an ML model in a Vertex AI Workbench notebook. You want to automate the tracking of artifacts and compare models during experimentation by using different approaches and frameworks. You want to create a repeatable process, and be able to rapidly and easily transition to production as you iterate on your model implementation.
What should you do?

  1. 1. Initialize the Vertex SDK with the name of your experiment. Log parameters and metrics for each experiment, and attach dataset and model artifacts as inputs and outputs to each execution.
    2. After a successful experiment create a Vertex AI pipeline.
  2. 1. Initialize the Vertex SDK with the name of your experiment. Log parameters and metrics for each experiment, save your dataset to a Cloud Storage bucket, and upload the models to Vertex AI Model Registry.
    2. After a successful experiment, create a Vertex AI pipeline.
  3. 1. Create a Vertex AI pipeline with parameters you want to track as arguments to your PipelineJob. Use the Metrics, Model, and Dataset artifact types from the Kubeflow Pipelines DSL as the inputs and outputs of the components in your pipeline.
    2. Associate the pipeline with your experiment when you submit the job.
  4. 1. Create a Vertex AI pipeline. Use the Dataset and Model artifact types from the Kubeflow Pipelines DSL as the inputs and outputs of the components in your pipeline.
    2. In your training component, use the Vertex AI SDK to create an experiment run. Configure the log_params and log_metrics functions to track parameters and metrics of your experiment.

Answer(s): A



You have created a prototype of a real-time customer support virtual agent using Gemini Pro that has achieved great results. During load testing, you discover that the cost per query and the end-user latency are higher than expected. You need to reduce both cost and latency while maintaining high-quality responses.
What should you do?

  1. Fine-tune a Gemini Flash model using high-quality responses collected from the Gemini Pro prototype, and route all queries to this model.
  2. Batch incoming user queries into groups of 15 before processing them with Gemini Pro.
  3. Purchase Provisioned Throughput for the Gemini Pro model to reserve dedicated compute resources.
  4. Re-engineer the virtual agent to use Gemini Flash to classify the user's intent. Answer simple queries using Gemini Flash, and route more complex queries to Gemini Pro.

Answer(s): D

Explanation:

Using Gemini Flash as an initial classifier and response model for simpler requests reduces both per-query cost and latency because Flash is optimized for fast, cost-efficient processing. More complex requests can still be routed to Gemini Pro when stronger reasoning capability is needed, preserving response quality where it matters while avoiding the expense and latency of using Pro for every query.


Reference:

https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/deploy/consumption-options?hl=en



Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input data. How should you address the input differences in production?

  1. Create alerts to monitor for skew using Vertex AI Model Monitoring, and retrain the model.
  2. Perform feature selection on the model, and retrain the model with fewer features.
  3. Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service.
  4. Perform feature selection on the model, and retrain the model on a monthly basis with fewer features.

Answer(s): A



  PDF
Viewing page 7 of 82
Viewing questions 31 - 35 out of 402 questions


Post your Comments and Discuss Google PROFESSIONAL MACHINE LEARNING ENGINEER exam prep with other Community members:

AI Tutor AI Tutor 👋 I’m here to help!