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| Section | Weight | Objectives |
|---|---|---|
| Operationalizing data and ML pipelines | 30% | - Pipeline automation and orchestration
|
| Building and operationalizing data processing systems | 30% | - Data ingestion and transformation
|
| Designing data processing systems | 20% | - Storage and data modeling
|
| Maintaining and optimizing data and ML solutions | 20% | - Machine learning integration
|
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NEW QUESTION # 39
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?
Answer: C
NEW QUESTION # 40
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?
Answer: C
Explanation:
Reference:
https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-ap
NEW QUESTION # 41
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.
Answer: D
Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview
NEW QUESTION # 42
What are all of the BigQuery operations that Google charges for?
Answer: C
Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing
NEW QUESTION # 43
What are two methods that can be used to denormalize tables in BigQuery?
Answer: C
Explanation:
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information. The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data
NEW QUESTION # 44
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