Professional-Data-Engineer Exam Dumps - Google Professional Data Engineer Exam

To ensure that both production jobs with strict SLAs and ad-hoc queries have appropriate compute resources available while adhering to cost efficiency, setting up separate reservations and billing models for each project is the best approach. Here’s why option B is the best choice:

Separate Reservations for SLA and Ad-hoc Projects:

Creating two separate reservations allows for dedicated resource management tailored to the needs of each project.

The production project requires guaranteed slots with the ability to scale up as needed, while the ad-hoc project benefits from on-demand billing based on data scanned.

Enterprise Edition Reservation for SLA Project:

Setting a baseline of 300 slots ensures that the SLA project has the minimum required resources.

Enabling autoscaling up to 500 additional slots allows the project to handle occasional spikes in workload without compromising on SLAs.

On-Demand Billing for Ad-hoc Project:

Using on-demand billing for the ad-hoc project ensures cost efficiency, as users are billed based on the amount of data scanned rather than reserved slot capacity.

This model suits the less predictable and often lower-utilization nature of ad-hoc queries.

Steps to Implement:

Set Up Enterprise Edition Reservation for SLA Project:

Create a reservation with a baseline of 300 slots.

Enable autoscaling to allow up to an additional 500 slots as needed.

Configure On-Demand Billing for Ad-hoc Project:

Ensure that the ad-hoc project is set up to use on-demand billing, which charges based on data scanned by the queries.

Monitor and Adjust:

Continuously monitor the usage and performance of both projects to ensure that the configurations meet the needs and make adjustments as necessary.

Reference Links:

BigQuery Slot Reservations

BigQuery On-Demand Pricing

To enable different teams to manage their own data while allowing data exchange across the organization, using Analytics Hub is the best approach. Here’s why option C is the best choice:

Analytics Hub:

Analytics Huballows teams to publish their data as data exchanges, making it easy for other teams to discover and subscribe to the data they need.

This approach maintains each team's control over their data while facilitating easy and secure data sharing across the organization.

Data Publishing and Subscribing:

Teams can publish datasets they control, allowing them to manage access and updates independently.

Other teams can subscribe to these published datasets, ensuring they have access to the latest data without duplicating efforts.

Minimized Operational Tasks and Costs:

This method reduces the need for complex replication or data synchronization processes, minimizing operational overhead.

By centralizing data sharing through Analytics Hub, it also reduces storage costs associated with duplicating large datasets.

Steps to Implement:

Set Up Analytics Hub:

Enable Analytics Hub in your Google Cloud project.

Provide training to teams on how to publish and subscribe to data exchanges.

Publish Data:

Each team publishes their datasets in Analytics Hub, configuring access controls and metadata as needed.

Subscribe to Data:

Teams that need access to data from other teams can subscribe to the relevant data exchanges, ensuring they always have up-to-date data.

Reference Links:

Analytics Hub Documentation

Publishing Data in Analytics Hub

Subscribing to Data in Analytics Hub

Reference https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/flex

To create a robust and resilient SQL pipeline in BigQuery that handles retries and failure notifications, consider the following:

BigQuery Scheduled Queries: This feature allows you to schedule recurring queries in BigQuery. It is a straightforward way to run SQL transformations on a regular basis without requiring extensive setup.

Error Handling and Retries: While BigQuery Scheduled Queries can run at specified intervals, they don't natively support complex retry logic or failure notifications directly. This is where additional Google Cloud services like Pub/Sub and Cloud Functions come into play.

Pub/Sub for Notifications: By configuring a BigQuery scheduled query to publish messages to a Pub/Sub topic upon failure, you can create a decoupled and scalable notification system.

Cloud Functions: Cloud Functions can subscribe to the Pub/Sub topic and implement logic to count consecutive failures. After detecting three consecutive failures, the Cloud Function can then send an email notification using a service like SendGrid or Gmail API.

Implementation Steps:

Set up a BigQuery Scheduled Query:

Create a scheduled query in BigQuery to run your SQL transformation every two hours.

Configure the scheduled query to publish a notification to a Pub/Sub topic in case of a failure.

Create a Pub/Sub Topic:

Create a Pub/Sub topic that will receive messages from the scheduled query.

Develop a Cloud Function:

Write a Cloud Function that subscribes to the Pub/Sub topic.

Implement logic in the Cloud Function to track failure messages. If three consecutive failure messages are detected, the function sends an email notification.

Reference Links:

BigQuery Scheduled Queries

Pub/Sub Documentation

Cloud Functions Documentation

SendGrid Email API

Gmail API

After creating a Cloud Dataproc cluster, you can scale the cluster by increasing or decreasing the number of worker nodes in the cluster at any time, even when jobs are running on the cluster. Cloud Dataproc clusters are typically scaled to:

1) increase the number of workers to make a job run faster

2) decrease the number of workers to save money

3) increase the number of nodes to expand available Hadoop Distributed Filesystem (HDFS) storage