[PMLE CERTIFICATE - EXAMTOPIC] DUMPS Q5-Q8

EXAMTOPIC DUMPS Q5-Q8 ; Managed Service for Training jobs with various ML frameworks, Retraining configuration of Image Classification model, No code sevice for EDA/Feature selection /Modeling/Training/Hyperparameters Tuning/Serving, End-to-end architecture configuration tool for repeatitive job

Q5.

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-learn, and custom libraries. What should you do?

Managed Service for Training jobs with various ML frameworks

• ⭕ A. Use the AI Platform custom containers feature to receive training jobs using any framework.
  → AI Platform/Vertex AIManaged service & allows for custom containers & supports frameworks(keras,pytorch, theano, Scikit-learn,...)
• ❌ B. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TF Job.
  → Kubeflow is not managed service in GCP
• ❌ C. Create a library of VM images on Compute Engine, and publish these images on a centralized repository.
• ❌ D. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.

Containers on AI Platform Training

• *a feature that allows you to run your application within a Docker image *
• Build your own custom container to run jobs on AI Platform Training , using ML frameworks and versions as well as non-ML dependencies, libraries and binaries that are not otherwise supported on AI Platform Training.
• Hyperparameter tuning with custom containers
• Using GPUs with custom containers

Advantages of custom containers
Custom containers to specify and pre-install all the dependencies needed for your application.
(1) Faster start-up time. If you use a custom container with your dependencies pre-installed, you can save the time that your training application would otherwise take to install dependencies when starting up.
(2) 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. (e.g, train with PyTorch)
(3) Extended support for distributed training using any ML framework.
(4) Use the newest version. You can also use the latest build or minor version of an ML framework. (e.g, you can build a custom container to train with tf-nightly).

Q6.

You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use AI Platform's continuous evaluation service to ensure that the models have high accuracy on your test dataset. What should you do?

Retraining configuration of Image Classification model

• ❌ A. Keep the original test dataset unchanged even if newer products are incorporated into retraining.
• ⭕ B. Extend your test dataset with images of the newer products when they are introduced to retraining.
• ❌ C. Replace your test dataset with images of the newer products when they are introduced to retraining.
• ❌ D. Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.

Q7.

You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?

No code sevice for EDA/Feature selection /Modeling/Training/Hyperparameters Tuning/Serving

• ⭕ A. Configure AutoML Tables to perform the classification task.
  → doesn't require code
  _→ Automatically build and deploy state-of-the-art machine learning models on structured data _
• ❌ B. Run a BigQuery ML task to perform logistic regression for the classification.
  → need to write code
• ❌ C. Use AI Platform Notebooks to run the classification model with pandas library.
  → need to write code
• ❌ D. Use AI Platform to run the classification model job configured for hyperparameter tuning.
  → need to write code

• AutoML Tables
• Fully managed easy to deploy model training and delployment engine. however, AutoML tables has limitation in terms of feature engineering.

Q8.

You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?

End-to-end architecture configuration tool for repeatitive job

• ⭕ A. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
  → Kubeflow : End-to-end orchestration of machine learning pipelines
• ❌ B. Use a model trained and deployed on BigQuery ML, and trigger retraining with the scheduled query feature in BigQuery.
  → NOT provide END-TO-END ARCHITECTURE
  • PROCESS OF BQML MODEL SERVING
• ❌ C. Write a Cloud Functions script that launches a training and deploying job on AI Platform that is triggered by Cloud Scheduler.
  → NOT provide END-TO-END ARCHITECTURE
• ❌ D. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model.
  → NOT GOOGLE-RECOMMENDED BEST PRACTICE
  →Cloud Scheduler can trigger the first step in a pipeline, but then some orchestrator is needed to continue the remaining steps. Besides, having Cloud Scheduler alone can't ensure failure handling during pipeline execution.

• GOOGLE-RECOMMENDED BEST PRACTICE FOR ML WORKFLOW ORCHESTRATION
• KUBEFLOW OVERVIEW