Free Google Professional-Machine-Learning-Engineer Exam Dumps Questions & Answers

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

• A.Build 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.
• B.Create a pipeline by 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.
• C.Use the Vertex AI SDK to preprocess the new data in the bucket prior to each model retraining. Store the processed features in BigQuery.
• D.Create 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.

You are developing a mode! to detect fraudulent credit card transactions. You need to prioritize detection because missing even one fraudulent transaction could severely impact the credit card holder. You used AutoML to tram a model on users ' profile information and credit card transaction data. After training the initial model, you notice that the model is failing to detect many fraudulent transactions. How should you adjust the training parameters in AutoML to improve model performance?
Choose 2 answers

• A.Increase the score threshold.
• B.Add more negative examples to the training set.
• C.Decrease the score threshold.
• D.Add more positive examples to the training set.
• E.Reduce the maximum number of node hours for training.

Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

• A.Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring
• B.Cloud Composer, Vertex AI Training with custom containers, and App Engine
• C.Cloud Composer, BigQuery ML, and Vertex AI Prediction
• D.Vertex AI Pipelines and App Engine

You work on the data science team for a multinational beverage company. You need to develop an ML model to predict the company's profitability for a new line of naturally flavored bottled waters in different locations.
You are provided with historical data that includes product types, product sales volumes, expenses, and profits for all regions. What should you use as the input and output for your model?

• A.Use product type and the feature cross of latitude with longitude, followed by binning, as features. Use profit as model output.
• B.Use latitude, longitude, and product type as features. Use profit as model output.
• C.Use product type and the feature cross of latitude with longitude, followed by binning, as features. Use revenue and expenses as model outputs.
• D.Use latitude, longitude, and product type as features. Use revenue and expenses as model outputs.

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?

• A.Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with synchronous recognition.
• B.Upsample the audio recordings to 16 kHz. and transcribe the audio by using the Speech-to-Text API with synchronous recognition.
• C.Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.
• D.Upsample the audio recordings to 16 kHz. and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.