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| Section | Weight | Objectives |
|---|---|---|
| Ensuring solution quality | 28% | - Reliability and performance
|
| Building and operationalizing data processing systems | 24% | - Data ingestion and integration
|
| Designing data processing systems | 22% | - Batch and streaming data processing design
|
| Operationalizing machine learning models | 26% | - ML pipeline integration
|
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NEW QUESTION # 315
You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?
Answer: D
NEW QUESTION # 316
Which software libraries are supported by Cloud Machine Learning Engine?
Answer: A
Explanation:
Cloud ML Engine mainly does two things:
Enables you to train machine learning models at scale by running TensorFlow training applications in the cloud.
Hosts those trained models for you in the cloud so that you can use them to get predictions about new data.
Reference: https://cloud.google.com/ml-engine/docs/technical-overview#what_it_does
NEW QUESTION # 317
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?
Answer: D
Explanation:
Dialogflow is used to do voice analytics on human computer interaction.
NEW QUESTION # 318
You need to create a SQL pipeline. The pipeline runs an aggregate SQL transformation on a BigQuery table every two hours and appends the result to another existing BigQuery table. You need to configure the pipeline to retry if errors occur. You want the pipeline to send an email notification after three consecutive failures. What should you do?
Answer: C
NEW QUESTION # 319
You designed a data warehouse in BigQuery to analyze sales data. You want a self-serving, low-maintenance, and cost-effective solution to share the sales dataset to other business units in your organization. What should you do?
Answer: B
Explanation:
The key requirements for sharing the sales dataset are:
Self-serving for other business units.
Low-maintenance.
Cost-effective.
Sharing with other business units (implying potentially different projects).
Analytics Hub (Option C) is designed precisely for this purpose of sharing data assets (like datasets) in a governed, discoverable, and self-service manner across an organization and even externally.
Self-Serving: Consumers (other business units) can browse available datasets in an exchange and subscribe to them. This makes it easy for them to discover and access the data they need without manual intervention from the data provider for each request.
Low-Maintenance for Provider: Once a dataset is published as a "listing" in Analytics Hub, the provider doesn't need to manage individual access requests or data copying for each new consumer project that subscribes. Updates to the source dataset are reflected for subscribers.
Cost-Effective:No Data Duplication for Sharing: When a dataset is shared via Analytics Hub, subscribers query the data directly from the provider's project (unless the provider explicitly opts for a replicated dataset model, which is less common for internal sharing where live access is preferred). This avoids storage costs associated with duplicating large datasets in multiple projects.
Query Costs: Query costs are typically borne by the subscriber's project.
Governed Sharing: Analytics Hub provides a centralized way to manage and audit data sharing.
Let's analyze why other options are less suitable:
A (Enable access to authorized views): Authorized views are a good way to share specific slices or aggregations of data without exposing the underlying tables. However, managing authorizations for potentially many views across many business units/projects can become less "self-serving" and more "low- maintenance" than a dedicated data exchange platform. Discoverability is also less centralized.
B (BigQuery Data Transfer Service to copy): This creates data copies, which increases storage costs and can lead to data staleness if the copy schedule isn't frequent enough. It's not "low-maintenance" as it requires managing DTS jobs and storage for copies. It's generally not the most cost-effective way to share for querying.
D (Create and share views with users): Similar to authorized views, but sharing directly with individual users can be a permissions management challenge at scale compared to project-level or group-level subscriptions facilitated by Analytics Hub. It lacks the "exchange" concept for discovery and self-service subscription by business units/projects.
Reference:
Google Cloud Documentation: Analytics Hub > Overview. "Analytics Hub is a platform that lets you create and manage exchanges of data assets efficiently and securely... Data providers can publish listings that reference shared datasets. Subscribers can view these listings and thensubscribe to them. When a subscriber subscribes to a listing, Analytics Hub creates a linked dataset in the subscriber's project that references the shared dataset." Google Cloud Documentation: Analytics Hub > Key benefits. "Simplified data sharing: Providers share data once, and subscribers access it in their own projects without data movement... Cost efficiency: Subscribers pay for queries run against shared data, not for storing the data." This aligns with self-serving, low- maintenance, and cost-effective sharing.
NEW QUESTION # 320
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