Google Professional Machine Learning Engineer - Professional-Machine-Learning-Engineer FREE EXAM DUMPS QUESTIONS & ANSWERS]

Question 1

You are developing a model to help your company create more targeted online advertising campaigns. You need to create a dataset that you will use to train the model. You want to avoid creating or reinforcing unfair bias in the model. What should you do? (Choose two.)

A. Collect a stratified sample of production traffic to build the training dataset B. Conduct fairness tests across sensitive categories and demographics on the trained model C. Include a comprehensive set of demographic features D. Collect a random sample of production traffic to build the training dataset E. Include only the demographic groups that most frequently interact with advertisements

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Correct Answer: A Vote an answer

Question 2

You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using AI Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take? (Choose two.)

A. Change the search algorithm from Bayesian search to random search. B. Set the early stopping parameter to TRUE. C. Decrease the number of parallel trials. D. Decrease the range of floating-point values. E. Decrease the maximum number of trials during subsequent training phases.

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Correct Answer: B,E Vote an answer

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Question 3

You deployed a conversational application that uses a large language model (LLM). The application has 1,000 users. You collect user feedback about the verbosity and accuracy of the model 's responses. The user feedback indicates that the responses are factually correct but users want different levels of verbosity depending on the type of question. You want the model to return responses that are more consistent with users' expectations, and you want to use a scalable solution. What should you do?

A. Experiment with other proprietary and open-source LLMs. Perform A/B testing by setting each model as your application's default model. Choose a model based on the results. B. Ask users to provide examples of responses with the appropriate verbosity as a list of question and answer pairs. Use this dataset to perform supervised fine tuning of the foundational model.
Re-evaluate the verbosity of responses with the tuned model. C. Ask users to indicate all scenarios where they expect concise responses versus verbose responses. Modify the application 's prompt to include these scenarios and their respective verbosity levels. Re-evaluate the verbosity of responses with updated prompts. D. Implement a keyword-based routing layer. If the user's input contains the words "detailed" or
"description," return a verbose response. If the user's input contains the word "fact." re-prompt the language model to summarize the response and return a concise response.

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Correct Answer: C Vote an answer

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Question 4

You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?

A. Create a Vertex AI Workbench managed notebook to browse and query the tables directly from the JupyterLab interface. B. Create a Vertex AI Workbench user-managed notebook using the default VM instance, and use the %%bigquerv magic commands in Jupyter to query the tables. C. Create a Vertex AI Workbench user-managed notebook on a Dataproc Hub, and use the %%bigquery magic commands in Jupyter to query the tables. D. Create a Vertex AI Workbench managed notebook on a Dataproc cluster, and use the spark- bigquery-connector to access the tables.

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Correct Answer: A Vote an answer

Question 5

While monitoring your model training's GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the execution time of your input pipeline. What should you do?

A. Add caching to the pipeline B. Increase the CPU load C. Increase the network bandwidth D. Add parallel interleave to the pipeline

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Correct Answer: D Vote an answer

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Question 6

You developed a custom model by using Vertex AI to predict your application's user churn rate.
You are using Vertex AI Model Monitoring for skew detection. The training data stored in BigQuery contains two sets of features - demographic and behavioral. You later discover that two separate models trained on each set perform better than the original model. You need to configure a new model monitoring pipeline that splits traffic among the two models. You want to use the same prediction-sampling-rate and monitoring-frequency for each model. You also want to minimize management effort. What should you do?

A. Keep the training dataset as is. Deploy the models to two separate endpoints, and submit two Vertex AI Model Monitoring jobs with appropriately selected feature-thresholds parameters. B. Separate the training dataset into two tables based on demographic and behavioral features.
Deploy the models to two separate endpoints, and submit two Vertex AI Model Monitoring jobs. C. Separate the training dataset into two tables based on demographic and behavioral features.
Deploy both models to the same endpoint, and submit a Vertex AI Model Monitoring job with a monitoring-config-from-file parameter that accounts for the model IDs and training datasets. D. Keep the training dataset as is. Deploy both models to the same endpoint and submit a Vertex AI Model Monitoring job with a monitoring-config-from-file parameter that accounts for the model IDs and feature selections.

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Correct Answer: C Vote an answer

Question 7

You are an ML engineer at a retail company. You need to automate a recurring retraining workflow for a demand forecasting model. The workflow involves dependencies across hybrid environments and must execute the following sequential steps:
1. Trigger a shell script on an on-premises server to extract legacy
sales data and send it to a Cloud Storage bucket.
2. Trigger a pipeline in Agent Platform Pipelines to train and validate the new model.
3. Push the validation metrics to a third-party API.
You need a fully managed orchestration service that provides native connectors to handle these cross-platform dependencies. You want to use the most stable, robust solution. What should you do?

A. Use Agent Platform Pipelines to develop a custom container component for each workflow step.
Use the Kubeflow Pipelines (KFP) SDK to define the execution order. B. Use Managed Service for Apache Airflow to define a directed acyclic graph (DAG) in Python. Use Apache Airflow operators and sensors to orchestrate the workflow steps. C. Use Cloud Scheduler to trigger a Cloud Run function that executes the on-premises script using SSH, publishes a Pub/Sub message upon completion, and triggers the subsequent workflow steps. D. Use Workflows to define the orchestration logic in YAML. Use the http.post connector to trigger the on-premises shell script and the Agent Platform API for the training job.

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Correct Answer: B Vote an answer

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Question 8

You are using Vertex AI to manage your ML models and datasets. You recently updated one of your models. You want to track and compare the new version with the previous one and incorporate dataset versioning. What should you do?

A. Use Vertex AI TensorBoard to visualize the training metrics of the new model version, and use Data Catalog to manage dataset versioning. B. Use Vertex AI Experiments to track and compare model artifacts and versions, and use Vertex AI managed datasets to manage dataset versioning. C. Use Vertex AI Experiments to track and compare model artifacts and versions, and use Vertex ML Metadata to manage dataset versioning. D. Use Vertex AI Model Monitoring to monitor the performance of the new model version, and use Vertex AI Training to manage dataset versioning.

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Correct Answer: C Vote an answer

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Question 9

You work at a mobile gaming startup that creates online multiplayer games. Recently, your company observed an increase in players cheating in the games, leading to a loss of revenue and a poor user experience You built a binary classification model to determine whether a player cheated after a completed game session, and then send a message to other downstream systems to ban the player that cheated. Your model has performed well during testing, and you now need to deploy the model to production. You want your serving solution to provide immediate classifications after a completed game session to avoid further loss of revenue. What should you do?

A. Save the model files in a VM. Load the model files each time there is a prediction request, and run an inference job on the VM B. Import the model into Vertex AI Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs. C. Save the model files in a Cloud Storage bucket. Create a Cloud Function to read the model files and make online inference requests on the Cloud Function. D. Import the model into Vertex AI Model Registry. Create a Vertex AI endpoint that hosts the model, and make online inference requests.

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Correct Answer: D Vote an answer