Exam Professional Machine Learning Engineer topic 1 question 255 discussion

The DataflowPythonJobOp operator lets you create a Vertex AI Pipelines component that prepares data by submitting a Python-based Apache Beam job to Dataflow for execution. https://cloud.google.com/vertex-ai/docs/pipelines/dataflow-component#dataflowpythonjobop Using we can specify an output location for Vertex AI to store predictions results https://cloud.google.com/vertex-ai/docs/pipelines/batchprediction-component A - is incorrect since we dont need an endpoint for batch predictions B - creating a new Dataflow pipeline is redundant

C is the best option because it uses: 1) Vertex AI Pipelines for orchestrating the flow (managed and scalable). 2) DataflowPythonJobOp for prep and ModelBatchPredictOp for batch predictions on Vertex AI. *A deploys the model to a Vertex AI endpoint, inefficient for batch jobs! *B uses a single Dataflow pipeline, which needs custom Vertex AI and BQ integration. *D uses BigQuery, a datawarehouse, for model deployment and prediction.

Uploading predictions directly to BigQuery from the Dataflow pipeline integrates seamlessly with your data storage.

B is right because 1)You've already trained a classification model using TensorFlow, so you need to productionize it by deploying it to a Vertex AI endpoint. 2)To automate the prediction process on a weekly schedule, you can create a Dataflow pipeline that reuses your existing data processing logic. This pipeline will send requests to the deployed model for inference and then upload the predicted results to BigQuery.

Only option B talks about loading the data to BigQuery

B Vertex AI Deployment: Vertex AI provides a managed environment for deploying machine learning models. It simplifies the process and ensures scalability. Dataflow Pipeline Reuse: Reusing the existing Dataflow pipeline for data processing leverages your existing code and avoids redundant logic. Model Endpoint Predictions: Sending requests to the deployed model endpoint allows for efficient prediction generation. BigQuery Upload: Uploading predictions directly to BigQuery from the Dataflow pipeline integrates seamlessly with your data storage.

No need to deploy to endpoint as we need batch predictions. ModelBatchPredictOp can upload data to BQ. Dataflow pipeline logic can be implemented in DataflowPythonJobOp

TFRecords is a specific file format designed by TensorFlow for storing data in a way that's efficient for the machine learning framework. Here are some key points about TFRecords:

Option A: Vertex AI Pipelines' ModelBatchPredictOp is designed for batch prediction within pipelines, not for serving models through an endpoint. Option C: Importing the model directly into BigQuery is not feasible for TensorFlow models. Option D: Vertex AI Pipelines' BigqueryPredictModelJobOp assumes the model is already trained and hosted in BigQuery ML, which isn't the case here.

ModelBatchPredictOp -> upload automatically on BQ No need for endpoint --> C

agree with BlehMaks

Answer is C. No need for an endpoint here : Simply specify the BigQuery table URI in the ModelBatchPredictOp parameter and you're done automatically uploading to BigQuery

My Answer: B The most complete answer, and reuse a created pipeline. Don’t make sense to use DataflowPythonJobOp when you have already created a dataflow pipeline that does the same.

Not A, C as they does not explicitly mention how the predictions will be uploaded to BigQuery.

The answer is B, optional A and B doesn't mention how to import prediction result to BigQuery.

Option A: Vertex AI Pipelines are excellent for orchestrating ML workflows but might not be as efficient as Dataflow for large-scale data processing, especially with existing Dataflow logic. Option C: While Vertex AI Pipelines can handle model loading and prediction, Dataflow is better suited for large-scale data processing and BigQuery integration. Option D: BigQuery ML is primarily for in-database model training and prediction, not ideal for external models or large-scale data processing.