Free Google Professional Machine Learning Engineer Exam Professional-Machine-Learning-Engineer Exam Practice Test

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?

• Vertex AI Pipelines and App Engine
• Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring
• Cloud Composer, BigQuery ML, and Vertex AI Prediction
• Cloud Composer, Vertex AI Training with custom containers, and App Engine

You are developing an ML model intended to classify whether X-Ray images indicate bone fracture risk. You have trained on Api Resnet architecture on Vertex AI using a TPU as an accelerator, howeveryou are unsatisfied with the trainning time and use memory usage. You want to quickly iterate your training code but make minimal changes to the code. You also want to minimize impact on the models accuracy. What should you do?

• Configure your model to use bfloat16 instead float32
• Reduce the global batch size from 1024 to 256
• Reduce the number of layers in the model architecture
• Reduce the dimensions of the images used un the model

Your company stores a large number of audio files of phone calls made to your customer call center in an on-premises database. Each audio file is in wav format and is approximately 5 minutes long. You need to analyze these audio files for customer sentiment. You plan to use the Speech-to-Text API. You want to use the most efficient approach. What should you do?

• 1 Upload the audio files to Cloud Storage
• Call the speech: Iongrunningrecognize API endpoint to generate transcriptions
• Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions
• 1 Upload the audio files to Cloud Storage
• Call the speech: Iongrunningrecognize API endpoint to generate transcriptions.
• Create a Cloud Function that calls the Natural Language API by using the analyzesentiment method
• 1 Iterate over your local Tiles in Python
• Use the Speech-to-Text Python library to create a speech.RecognitionAudio object and set the content to the audio file data
• Call the speech: recognize API endpoint to generate transcriptions
• Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions
• 1 Iterate over your local files in Python
• Use the Speech-to-Text Python Library to create a speech.RecognitionAudio object, and set the content to the audio file data3. Call the speech: lengrunningrecognize API endpoint to generate transcriptions
• Call the Natural Language API by using the analyzesenriment method

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?

• Split incoming traffic among separate Cloud Run instances of deployed models. Monitor the performance of each version using Cloud Monitoring.
• Split 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.
• Split 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.
• Split incoming traffic to distribute prediction requests among the versions. Monitor the performance of each version using Vertex AI’s built-in monitoring tools.

You are developing a model to identify traffic signs in images extracted from videos taken from the dashboard of a vehicle. You have a dataset of 100 000 images that were cropped to show one out of ten different traffic signs. The images have been labeled accordingly for model training and are stored in a Cloud Storage bucket You need to be able to tune the model during each training run.How should you train the model?

• Train a model for object detection by using Vertex Al AutoML.
• Train a model for image classification by using Vertex Al AutoML.
• Develop the model training code for object detection and tram a model by using Vertex Al custom training.
• Develop the model training code for image classification and train a model by using Vertex Al custom training.

You recently used XGBoost to train a model in Python that will be used for online serving Your model prediction service will be called by a backend service implemented in Golang running on a Google Kubemetes Engine (GKE) cluster Your model requires pre and postprocessing steps You need to implement the processing steps so that they run at serving time You want to minimize code changes and infrastructure maintenance and deploy your model into production as quickly as possible. What should you do?

• Use FastAPI to implement an HTTP server Create a Docker image that runs your HTTP server and deploy it on your organization's GKE cluster.
• Use FastAPI to implement an HTTP server Create a Docker image that runs your HTTP server Upload the image to Vertex Al Model Registry and deploy it to a Vertex Al endpoint.
• Use the Predictor interface to implement a custom prediction routine Build the custom contain upload the container to Vertex Al Model Registry, and deploy it to a Vertex Al endpoint.
• Use the XGBoost prebuilt serving container when importing the trained model into Vertex Al Deploy the model to a Vertex Al endpoint Work with the backend engineers to implement the pre- and postprocessing steps in the Golang backend service.

You are an ML engineer in the contact center of a large enterprise. You need to build a sentiment analysis tool that predicts customer sentiment from recorded phone conversations. You need to identify the best approach to building a model while ensuring that the gender, age, and cultural differences of the customers who called the contact center do not impact any stage of the model development pipeline and results. What should you do?

• Extract sentiment directly from the voice recordings
• Convert the speech to text and build a model based on the words
• Convert the speech to text and extract sentiments based on the sentences
• Convert the speech to text and extract sentiment using syntactical analysis

You are building an ML model to predict trends in the stock market based on a wide range of factors. While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don’t overfit the model. What should you do?

• Standardize the data by transforming it with a logarithmic function.
• Apply a principal component analysis (PCA) to minimize the effect of any particular feature.
• Use a binning strategy to replace the magnitude of each feature with the appropriate bin number.
• Normalize the data by scaling it to have values between 0 and 1.

You have recently created a proof-of-concept (POC) deep learning model. You are satisfied with the overall architecture, but you need to determine the value for a couple of hyperparameters. You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate. You configure the following settings:For the embedding dimension, you set the type to INTEGER with a minValue of 16 and maxValue of 64.For the learning rate, you set the type to DOUBLE with a minValue of 10e-05 and maxValue of 10e- 02.You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy. Training time is not a concern. How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?

• Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a large number of parallel trials.
• Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials.
• Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials.
• Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a small number of parallel trials.

You work for a company that sells corporate electronic products to thousands of businesses worldwide. Your company stores historical customer data in BigQuery. You need to build a model that predicts customer lifetime value over the next three years. You want to use the simplest approach to build the model. What should you do?

• Access BigQuery Studio in the Google Cloud console. Run the create model statement in the SQL editor to create an ARIMA model.
• Create a Vertex Al Workbench notebook. Use IPython magic to run the create model statement tocreate an ARIMA model.
• Access BigQuery Studio in the Google Cloud console. Run the create model statement in the SQL editor to create an AutoML regression model.
• Create a Vertex Al Workbench notebook. Use IPython magic to run the create model statement to create an AutoML regression model.