Online Access Free Google.Professional-Machine-Learning-Engineer.v2023-01-24.q55 Practice Test (Page 8)

Professional-Machine-Learning-Engineer Exam Question 31

You need to design an architecture that serves asynchronous predictions to determine whether a particular mission-critical machine part will fail. Your system collects data from multiple sensors from the machine. You want to build a model that will predict a failure in the next N minutes, given the average of each sensor's data from the past 12 hours. How should you design the architecture?

A.1. Export the data to Cloud Storage using the BigQuery command-line tool
2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.
3. Export the batch prediction job outputs from Cloud Storage and import them into BigQuery.

B.1. Export your data to Cloud Storage using Dataflow.
2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.
3. Export the batch prediction job outputs from Cloud Storage and import them into Cloud SQL.

C.1. Events are sent by the sensors to Pub/Sub, consumed in real time, and processed by a Dataflow stream processing pipeline.
2. The pipeline invokes the model for prediction and sends the predictions to another Pub/Sub topic.
3. Pub/Sub messages containing predictions are then consumed by a downstream system for monitoring.

D.1. HTTP requests are sent by the sensors to your ML model, which is deployed as a microservice and exposes a REST API for prediction
2. Your application queries a Vertex AI endpoint where you deployed your model.
3. Responses are received by the caller application as soon as the model produces the prediction.

Professional-Machine-Learning-Engineer Exam Question 32

You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?

A.Create a tf.data.Dataset.prefetch transformation

B.Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).

C.Convert the images to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().

D.Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training

Professional-Machine-Learning-Engineer Exam Question 33

You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?

A.* Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online prediction

B.* Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue

C.* Stream incoming prediction request data into Cloud Spanner
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue.

D.* Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue

Professional-Machine-Learning-Engineer Exam Question 34

You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?

A.Use Data Catalog to search the BigQuery datasets by using keywords in the table description.

B.Tag each of your model and version resources on AI Platform with the name of the BigQuery table that was used for training.

C.Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.

D.Execute a query in BigQuery to retrieve all the existing table names in your project using the

Professional-Machine-Learning-Engineer Exam Question 35

You have a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. You need to quickly build, test, and deploy a service that will automatically classify future written requests into one of the categories. How should you configure the pipeline?

A.Create a TensorFlow model using Google's BERT pre-trained model. Build and test a classifier, and deploy the model using Vertex AI.

B.Use the Cloud Natural Language API to obtain metadata to classify the incoming cases.

C.Use AutoML Natural Language to build and test a classifier. Deploy the model as a REST API.

D.Use BigQuery ML to build and test a logistic regression model to classify incoming requests. Use BigQuery ML to perform inference.

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Professional-Machine-Learning-Engineer Exam Question 31

Professional-Machine-Learning-Engineer Exam Question 32

Professional-Machine-Learning-Engineer Exam Question 33

Professional-Machine-Learning-Engineer Exam Question 34

Professional-Machine-Learning-Engineer Exam Question 35

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