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Newly Released Google Professional-Machine-Learning-Engineer Exam PDF

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Question 60

You work for a rapidly growing social media company. Your team builds TensorFlow recommender models in an on-premises CPU cluster. The data contains billions of historical user events and 100 000 categorical features. You notice that as the data increases the model training time increases. You plan to move the models to Google Cloud You want to use the most scalable approach that also minimizes training time. What should you do?

Options:

A.

Deploy the training jobs by using TPU VMs with TPUv3 Pod slices, and use the TPUEmbedding API.

B.

Deploy the training jobs in an autoscaling Google Kubernetes Engine cluster with CPUs

C.

Deploy a matrix factorization model training job by using BigQuery ML.

D.

Deploy the training jobs by using Compute Engine instances with A100 GPUs and use the

t f. nn. embedding_lookup API.

Question 61

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?

Options:

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 62

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?

Options:

A.

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

B.

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

C.

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

D.

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

Question 63

You work for a company that manages a ticketing platform for a large chain of cinemas. Customers use a mobile app to search for movies they’re interested in and purchase tickets in the app. Ticket purchase requests are sent to Pub/Sub and are processed with a Dataflow streaming pipeline configured to conduct the following steps:

1. Check for availability of the movie tickets at the selected cinema.

2. Assign the ticket price and accept payment.

3. Reserve the tickets at the selected cinema.

4. Send successful purchases to your database.

Each step in this process has low latency requirements (less than 50 milliseconds). You have developed a logistic regression model with BigQuery ML that predicts whether offering a promo code for free popcorn increases the chance of a ticket purchase, and this prediction should be added to the ticket purchase process. You want to identify the simplest way to deploy this model to production while adding minimal latency. What should you do?

Options:

A.

Run batch inference with BigQuery ML every five minutes on each new set of tickets issued.

B.

Export your model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline.

C.

Export your model in TensorFlow format, deploy it on Vertex AI, and query the prediction endpoint from your streaming pipeline.

D.

Convert your model with TensorFlow Lite (TFLite), and add it to the mobile app so that the promo code and the incoming request arrive together in Pub/Sub.

Page: 15 / 20
Exam Name: Google Professional Machine Learning Engineer
Last Update: Nov 1, 2024
Questions: 270
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