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| Section | Objectives |
|---|---|
| ML model development | - Model training and tuning
|
| Designing ML solutions | - ML architecture design
|
| Deployment and operations | - Model deployment
|
| Data preparation and processing | - Data ingestion and pipelines
|
| ML pipeline automation and orchestration | - Pipeline design
|
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NEW QUESTION # 240
You are developing a process for training and running your custom model in production. You need to be able to show lineage for your model and predictions. What should you do?
Answer: B
NEW QUESTION # 241
You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?
Answer: B
Explanation:
Cost-effectiveness: User-managed notebooks in Vertex AI Workbench allow you to leverage pre-configured virtual machines with reasonable resource allocation, keeping costs lower compared to options involving managed notebooks or Dataproc clusters.
Development flexibility: User-managed notebooks offer full control over the environment, allowing you to install additional libraries or dependencies needed for your specific EDA, preprocessing, and model training tasks. This flexibility is crucial while experimenting with different algorithms.
BigQuery integration: The %%bigquery magic commands provide seamless integration with BigQuery within the Jupyter Notebook environment. This enables efficient querying and exploration of customer transaction data stored in BigQuery directly from the notebook, streamlining the workflow.
Other options and why they are not the best fit:
B . Managed notebook: While managed notebooks offer an easier setup, they might have limited customization options, potentially hindering your ability to install specific libraries or tools.
C . Dataproc Hub: Dataproc Hub focuses on running large-scale distributed workloads, and it might be overkill for your scenario involving exploratory analysis and experimentation with different algorithms. Additionally, it could incur higher costs compared to a user-managed notebook.
D . Dataproc cluster with spark-bigquery-connector: Similar to option C, using a Dataproc cluster with the spark-bigquery-connector would be more complex and potentially more expensive than using %%bigquery magic commands within a user-managed notebook for accessing BigQuery data.
Reference:
https://cloud.google.com/vertex-ai/docs/workbench/instances/bigquery
https://cloud.google.com/vertex-ai-notebooks
NEW QUESTION # 242
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?
Answer: C
Explanation:
The end-to-end architecture of the predictive model for estimating delay times for multiple transportation routes should be configured using Kubeflow Pipelines. Kubeflow Pipelines is a platform for building and deploying scalable, portable, and reusable machine learning pipelines on Kubernetes. Kubeflow Pipelines allows you to orchestrate your multi-step workflow from data preparation, model training, model evaluation, model deployment, and model serving. Kubeflow Pipelines also provides a user interface for managing and tracking your pipeline runs, experiments, and artifacts1 Using Kubeflow Pipelines has several advantages for this use case:
* Full automation: You can define your pipeline as a Python script that specifies the steps and dependencies of your workflow, and use the Kubeflow Pipelines SDK to compile and uploadyour pipeline to the Kubeflow Pipelines service. You can also use the Kubeflow Pipelines UI to create, run, and monitor your pipeline2
* Scalability: You can leverage the power of Kubernetes to scale your pipeline components horizontally and vertically, and use distributed training frameworks such as TensorFlow or PyTorch to train your model on multiple nodes or GPUs3
* Portability: You can package your pipeline components as Docker containers that can run on any Kubernetes cluster, and use the Kubeflow Pipelines SDK to export and import your pipeline packages across different environments4
* Reusability: You can reuse your pipeline components across different pipelines, and share your components with other users through the Kubeflow Pipelines Component Store. You can also use pre-built components from the Kubeflow Pipelines library or other sources5
* Schedulability: You can use the Kubeflow Pipelines UI or the Kubeflow Pipelines SDK to schedule recurring pipeline runs based on cron expressions or intervals. For example, you can schedule your pipeline to run every month to retrain your model on the latest data.
The other options are not as suitable for this use case. Using a model trained and deployed on BigQuery ML is not recommended, as BigQuery ML is mainly designed for simple and quick machine learning tasks on large-scale data, and does not support complex models or custom code. Writing a Cloud Functions script that launches a training and deploying job on AI Platform is not ideal, as Cloud Functions has limitations on the memory, CPU, and execution time, and does not provide a user interface for managing and tracking your pipeline. Using Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model is not optimal, as Dataflow is mainly designed for data processing and streaming analytics, and does not support model serving or monitoring.
References: 1: Kubeflow Pipelines overview 2: Build a pipeline 3: Scale your machine learning training and prediction workloads 4: Export and import pipelines 5: Build components and pipelines : [Schedule recurring pipeline runs] : [BigQuery ML overview] : [Cloud Functions documentation] : [Dataflow documentation]
NEW QUESTION # 243
You work at a gaming startup that has several terabytes of structured data in Cloud Storage. This data includes gameplay time data, user metadata, and game metadata. You want to build a model that recommends new games to users that requires the least amount of coding. What should you do?
Answer: A
Explanation:
The best option to build a game recommendation model with the least amount of coding is to use BigQuery ML, which allows you to create and execute machine learning models using standard SQL queries. BigQuery ML supports several types of models, including matrix factorization, which is a common technique for collaborative filtering-based recommendation systems. Matrix factorization models learn latent factors for users and items from the observed ratings, and then use them to predict the ratings for new user-item pairs.
BigQuery ML provides a built-in function called ML.RECOMMEND that can generate recommendations for a given user based on a trained matrix factorization model. To use BigQuery ML, you need to load the data in BigQuery, which is a serverless, scalable, and cost-effective data warehouse. You can use the bq command- line tool, the BigQuery API, or the Cloud Console to load data from Cloud Storage to BigQuery.
Alternatively, you can use federated queries to query data directly from Cloud Storage without loading it to BigQuery, but this may incur additional costs and performance overhead. Option A is incorrect because BigQuery ML does not support Autoencoder models, which are a type of neural network that can learn compressed representations of the input data. Autoencoder models are not suitable for recommendation systems, as they do not capture the interactions between users and items. Option C is incorrect because using TensorFlow to train a two-tower model requires more coding than using BigQuery ML. A two-tower model is a type of neural network that learns embeddings for users and items separately, and then combines them with a dot product or a cosine similarity to compute the rating. TensorFlow is a low-level framework that requires you to define the model architecture, the loss function, the optimizer, the training loop, and the evaluation metrics. Moreover, you need to read the data from Cloud Storage to a Vertex AI Workbench notebook, which is an instance of JupyterLab that runs on a Google Cloud virtual machine. This may involve additional steps such as authentication, authorization, and data preprocessing. Option D is incorrect because using TensorFlow to train a matrix factorization model also requires more coding than using BigQuery ML. Although TensorFlow provides some high-level APIs such as Keras and TensorFlow Recommenders that can simplify the model development, you still need to handle the data loading and the model training and evaluation yourself. Furthermore, you need to read the data from Cloud Storage to a Vertex AI Workbench notebook, which may incur additional complexity and costs. References:
* BigQuery ML documentation
* Using matrix factorization with BigQuery ML
* Recommendations AI documentation
* Loading data into BigQuery
* Querying data in Cloud Storage from BigQuery
* Vertex AI Workbench documentation
* TensorFlow documentation
* TensorFlow Recommenders documentation
NEW QUESTION # 244
You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by AI Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?
Answer: C
Explanation:
By running your machine learning (ML) training job in a custom container, you can use ML frameworks, non-ML dependencies, libraries, and binaries that are not otherwise supported on Vertex AI.
Model and your data are too large to fit in memory on a single machine hence distributed training jobs.
https://cloud.google.com/vertex-ai/docs/training/containers-overview
NEW QUESTION # 245
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