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Exam Professional Machine Learning Engineer All Questions

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Exam Professional Machine Learning Engineer topic 1 question 246 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 246
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?

  • A. Retrain the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint,
  • B. Retrain the model by using Vertex Al Deploy the model from Vertex AI Model. Registry to a Vertex AI endpoint.
  • C. Alter the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint.
  • D. Export the model from BigQuery ML to Cloud Storage. Import the model into Vertex AI Model Registry. Deploy the model to a Vertex AI endpoint.
Show Suggested Answer Hide Answer
Suggested Answer: C 🗳️

Comments

Chosen Answer:
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pikachu007
Highly Voted 1 year, 2 months ago
Selected Answer: D
I think it's D, as model retraining should not be required unless it's specified there's new data.
upvoted 7 times
shadz10
1 year, 2 months ago
I agree with pikachu007
upvoted 2 times
vaibavi
1 year, 2 months ago
I think it's C Exported models for model types AUTOML_REGRESSOR and AUTOML_CLASSIFIER do not support AI Platform deployment for online prediction.
upvoted 5 times
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daidai75
1 year, 2 months ago
Agree with Pikachu007, the option D is good.
upvoted 1 times
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Dagogi96
1 year, 2 months ago
Friend is the C, and with Alter MODEL you can register the model in Vertex AI, I work in a company and I myself have registered models like this.
upvoted 7 times
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cruise93
Highly Voted 11 months, 2 weeks ago
Selected Answer: C
https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-alter-model#alter_model_statement
upvoted 6 times
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phani49
Most Recent 3 months, 1 week ago
Selected Answer: C
You can use the ALTER MODEL statement to register your existing BigQuery ML model with Vertex AI Model Registry https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-alter-model
upvoted 2 times
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Omi_04040
3 months, 3 weeks ago
Selected Answer: C
No need to export the model to Cloud Storage
upvoted 1 times
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lunalongo
4 months, 1 week ago
Selected Answer: C
C is the best option because: 1) Retraining the model (A/B) is not necessary (see positive feedbacks) 2) Exporting to Cloud Storage (D) is not necessary, since you can use the ALTER MODEL statement to register it on Vertex AI Model Registry and deploy it to the Vertex AI endpoint from there 3) Using BigQuery ML without exporting the model is the quickiest option
upvoted 2 times
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lunalongo
4 months, 1 week ago
D is the best option because: 1) BigQuery ML is excellent for training, not much for online prediction 2) Vertex AI provides a more robust and scalable infrastructure. 3) Exporting model from BigQuery ML to a format compatible with Vertex AI (typically Cloud Storage) is required ***A, B, and C attempt to directly deploy from BigQuery ML, which isn't a supported workflow.
upvoted 1 times
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Land3r
4 months, 2 weeks ago
Selected Answer: C
https://cloud.google.com/bigquery/docs/managing-models-vertex#register-new-bqml-model-version
upvoted 1 times
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hybridpro
6 months, 1 week ago
Selected Answer: C
It's C
upvoted 1 times
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d6e1ae4
7 months, 2 weeks ago
Selected Answer: D
The model has already been trained and received positive feedback, so there's no need to retrain the model.
upvoted 1 times
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AzureDP900
9 months ago
C is correct Here's why: 1) You trained an AutoML regression model using BigQuery ML. 2)To deploy the model for online prediction, you need to export the model in a format that is compatible with Vertex AI. 3)Altering the model by using BigQuery ML and specifying Vertex AI as the model registry allows you to export the model in the correct format. Once exported, you can deploy the model from Vertex AI Model Registry to a Vertex AI endpoint, which enables online prediction
upvoted 1 times
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AzureDP900
9 months ago
C is correct Here's why: 1) You trained an AutoML regression model using BigQuery ML. 2)To deploy the model for online prediction, you need to export the model in a format that is compatible with Vertex AI. 3)Altering the model by using BigQuery ML and specifying Vertex AI as the model registry allows you to export the model in the correct format. Once exported, you can deploy the model from Vertex AI Model Registry to a Vertex AI endpoint, which enables online prediction
upvoted 1 times
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gscharly
11 months, 2 weeks ago
Selected Answer: C
https://cloud.google.com/vertex-ai/docs/model-registry/model-registry-bqml https://cloud.google.com/bigquery/docs/update_vertex
upvoted 3 times
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fitri001
11 months, 3 weeks ago
Selected Answer: D
You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do? A. Retrain the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint, B. Retrain the model by using Vertex Al Deploy the model from Vertex AI Model. Registry to a Vertex AI endpoint. C. Alter the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint. D. Export the model from BigQuery ML to Cloud Storage. Import the model into Vertex AI Model Registry. Deploy the model to a Vertex AI endpoint.
upvoted 2 times
fitri001
11 months, 3 weeks ago
No Retraining: You've already trained a successful model in BigQuery ML. Retraining (Options A, B, and C) is unnecessary and adds time. Direct Deployment: Option D leverages existing tools for streamlined deployment. You export the model directly from BigQuery ML and import it into Vertex AI Model Registry for centralized management. Finally, you deploy the model to a Vertex AI endpoint for online predictions. Cloud Storage: Cloud Storage provides a readily accessible location to store your exported model before deployment.
upvoted 1 times
pinimichele01
11 months, 2 weeks ago
alter the model doesn't mean retrain...
upvoted 3 times
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omermahgoub
11 months, 3 weeks ago
Selected Answer: D
D. Export the model from BigQuery ML to Cloud Storage. Import the model into Vertex AI Model Registry. Deploy the model to a Vertex AI endpoint.
upvoted 1 times
pinimichele01
11 months, 3 weeks ago
why not C? it is not necessary to export in GCS
upvoted 1 times
omermahgoub
11 months, 2 weeks ago
I changed my answer to C. GCS is not necessary
upvoted 2 times
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playerXL7
11 months, 3 weeks ago
Selected Answer: C
https://cloud.google.com/vertex-ai/docs/model-registry/model-registry-bqml
upvoted 1 times
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alfieroy16
1 year ago
Selected Answer: C
Alter the model is correct,no need to export the model : "You can register BigQuery ML models with the Model Registry, in order to manage them alongside your other ML models without needing to export them" https://cloud.google.com/bigquery/docs/managing-models-vertex a simple update is sufficient : https://cloud.google.com/bigquery/docs/update_vertex
upvoted 1 times
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vaibavi
1 year, 1 month ago
Selected Answer: B
I think the answer here is B , because even if we alter or export automl regressor model trained in BQML is not supported in vertex ai for online prediction so we need to retrain using vertex ai
upvoted 1 times
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C (25%)
B (20%)
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