Free Access Google.Professional-Machine-Learning-Engineer.v2024-09-13.q242 Practice Test (Page 20)

Question 91

You work for a startup that has multiple data science workloads. Your compute infrastructure is currently on-premises. and the data science workloads are native to PySpark Your team plans to migrate their data science workloads to Google Cloud You need to build a proof of concept to migrate one data science job to Google Cloud You want to propose a migration process that requires minimal cost and effort. What should you do first?

A.Create a n2-standard-4 VM instance and install Java, Scala and Apache Spark dependencies on it.

B.Create a Google Kubemetes Engine cluster with a basic node pool configuration install Java Scala, and Apache Spark dependencies on it.

C.Create a Standard (1 master. 3 workers) Dataproc cluster, and run a Vertex Al Workbench notebook instance on it.

D.Create a Vertex Al Workbench notebook with instance type n2-standard-4.

Question 92

You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

A.Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate transformations Choose an automatic data split across the training, validation, and testing sets

B.Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately Choose an automatic data split across the training, validation, and testing sets

C.Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set

D.Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets

Question 93

You are designing an architecture with a serveress ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.
The proposed architecture has the following flow:

Which endpoints should the Enrichment Cloud Functions call?

A.1 = Al Platform, 2 = Al Platform, 3 = AutoML Natural Language

B.1 = cloud Natural Language API, 2 = Al Platform, 3 = Cloud Vision API

C.1 = Al Platform, 2 = Al Platform, 3 = Cloud Natural Language API

D.1 = Al Platform, 2 = Al Platform, 3 = AutoML Vision

Question 94

You have built a model that is trained on data stored in Parquet files. You access the data through a Hive table hosted on Google Cloud. You preprocessed these data with PySpark and exported it as a CSV file into Cloud Storage. After preprocessing, you execute additional steps to train and evaluate your model. You want to parametrize this model training in Kubeflow Pipelines. What should you do?

A.Remove the data transformation step from your pipeline.

B.Containerize the PySpark transformation step, and add it to your pipeline.

C.Deploy Apache Spark at a separate node pool in a Google Kubernetes Engine cluster. Add a ContainerOp to your pipeline that invokes a corresponding transformation job for this Spark instance.

D.Add a ContainerOp to your pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage.

Question 95

You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained You will use Vertex Al Pipelines to run the pipeline You need to decide which Google Cloud pipeline components to use What components should you choose?