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You work for a gaming company that collects real-time player activity dat
a. This data is streamed into Pub/Sub and needs to be processed and loaded into BigQuery for analysis. The processing involves filtering, enriching, and aggregating the data before loading it into partitioned BigQuery tables. You need to design a pipeline that ensures low latency and high throughput while following a Google-recommended approach. What should you do?
Comprehensive and Detailed in Depth
Why C is correct:Dataflow is the recommended service for real-time stream processing on Google Cloud.
It provides scalable and reliable processing with low latency and high throughput.
Dataflow's streaming API is optimized for Pub/Sub integration and BigQuery streaming inserts.
Why other options are incorrect:A: Cloud Composer is for batch orchestration, not real-time streaming.
B: Dataproc and Spark streaming are more complex and not as efficient as Dataflow for this task.
D: Cloud Run functions are for stateless, event-driven applications, not continuous stream processing.
Dataflow Streaming: https://cloud.google.com/dataflow/docs/concepts/streaming-pipelines
Pub/Sub to BigQuery with Dataflow: https://cloud.google.com/dataflow/docs/tutorials/pubsub-to-bigquery
Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability. What should you do?
Comprehensive and Detailed In-Depth
The strategy requires storage in two specific regions with automated high availability (HA). Cloud Storage location options dictate the solution:
Option A: A dual-region bucket (e.g., us-west1 and us-east1) replicates data synchronously across two user-specified regions, ensuring HA without manual intervention. It's fully automated and meets the requirement.
Option B: Two single-region buckets with gcloud storage replication is manual, not automated, and lacks real-time HA (requires scripting and monitoring).
Option C: Multi-region buckets (e.g., us) span multiple regions within a geography but don't let you specify exactly two regions, potentially violating the restriction.
Option D: Two single-region buckets with Storage Transfer Service automate replication but aren't synchronous (batch-based), reducing HA compared to dual-region's real-time sync. Why A is Best: Dual-region buckets provide geo-redundancy across two exact regions (e.g., nam4 for us-central1/us-east1), ensuring data is always available with no manual setup. For example, gsutil mb -l nam4 gs://my-bucket creates this setup, aligning with Google's HA recommendations. Extract from Google Documentation: From 'Cloud Storage Bucket Locations' (https://cloud.google.com/storage/docs/locations): 'Dual-region buckets provide high availability by synchronously replicating data across two specific regions you choose, ensuring automated redundancy and accessibility within your approved locations.' Reference: Google Cloud Documentation - 'Cloud Storage Dual-Region' (https://cloud.google.com/storage/docs/locations#dual-region).
Why A is Best: Dual-region buckets provide geo-redundancy across two exact regions (e.g., nam4 for us-central1/us-east1), ensuring data is always available with no manual setup. For example, gsutil mb -l nam4 gs://my-bucket creates this setup, aligning with Google's HA recommendations.
Extract from Google Documentation: From 'Cloud Storage Bucket Locations' (https://cloud.google.com/storage/docs/locations): 'Dual-region buckets provide high availability by synchronously replicating data across two specific regions you choose, ensuring automated redundancy and accessibility within your approved locations.'
Option D: Two single-region buckets with Storage Transfer Service automate replication but aren't synchronous (batch-based), reducing HA compared to dual-region's real-time sync. Why A is Best: Dual-region buckets provide geo-redundancy across two exact regions (e.g., nam4 for us-central1/us-east1), ensuring data is always available with no manual setup. For example, gsutil mb -l nam4 gs://my-bucket creates this setup, aligning with Google's HA recommendations. Extract from Google Documentation: From 'Cloud Storage Bucket Locations' (https://cloud.google.com/storage/docs/locations): 'Dual-region buckets provide high availability by synchronously replicating data across two specific regions you choose, ensuring automated redundancy and accessibility within your approved locations.' Reference: Google Cloud Documentation - 'Cloud Storage Dual-Region' (https://cloud.google.com/storage/docs/locations#dual-region).
You are a Looker analyst. You need to add a new field to your Looker report that generates SQL that will run against your company's database. You do not have the Develop permission. What should you do?
Creating a custom field from the field picker in Looker allows you to add new fields to your report without requiring the Develop permission. Custom fields are created directly in the Looker UI, enabling you to define calculations or transformations that generate SQL for the database query. This approach is user-friendly and does not require access to the LookML layer, making it the appropriate choice for your situation.
You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?
Comprehensive and Detailed in Depth
Why A is correct:Cloud Composer is a managed Apache Airflow service, which is a popular open-source workflow orchestration tool.
DAGs in Airflow can be used to automate ETL pipelines.
Airflow's web interface and Cloud Monitoring provide comprehensive monitoring capabilities.
It also allows you to run existing shell scripts.
Why other options are incorrect:B: Dataflow requires rewriting the ETL pipeline using its SDK.
C: Dataproc is for big data processing, not orchestration.
D: Cloud Run functions are for stateless applications, not long-running ETL pipelines.
Cloud Composer: https://cloud.google.com/composer/docs
Apache Airflow: https://airflow.apache.org/
You are responsible for managing Cloud Storage buckets for a research company. Your company has well-defined data tiering and retention rules. You need to optimize storage costs while achieving your data retention needs. What should you do?
Configuring a lifecycle management policy on each Cloud Storage bucket allows you to automatically transition objects to lower-cost storage classes (such as Nearline, Coldline, or Archive) based on their age or other criteria. Additionally, the policy can automate the removal of objects once they are no longer needed, ensuring compliance with retention rules and optimizing storage costs. This approach aligns well with well-defined data tiering and retention needs, providing cost efficiency and automation.
