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NEW QUESTION 28
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. 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.B. Add a ContainerOp to your pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage.C. Remove the data transformation step from your pipeline.D. Containerize the PySpark transformation step, and add it to your pipeline.

Answer: A

 

NEW QUESTION 29
You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

A. Export the model to BigQuery ML.B. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.C. Deploy and version the model on Al Platform.D. Use Dataflow with the SavedModel to read the data from BigQuery

Answer: A

 

NEW QUESTION 30
You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano. Scikit-team, and custom libraries. What should you do?

A. Create a library of VM images on Compute Engine; and publish these images on a centralized repositoryB. Use the Al Platform custom containers feature to receive training jobs using any frameworkC. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJobD. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.

Answer: B

Explanation:
because AI platform supported all the frameworks mentioned. And Kubeflow is not managed service in GCP. https://cloud.google.com/ai-platform/training/docs/getting-started-pytorch
https://cloud.google.com/ai-platform/training/docs/containers-overview#advantages_of_custom_containers Use the ML framework of your choice. If you can't find an AI Platform Training runtime version that supports the ML framework you want to use, then you can build a custom container that installs your chosen framework and use it to run jobs on AI Platform Training.

 

NEW QUESTION 31
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