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Google GCP-DE 試験シラバストピック:

Google Data Engineer 認定 GCP-DE 試験問題：

1. 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?

A) Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
B) Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
C) Use the TABLE_DATE_RANGE function
D) Use the WHERE_PARTITIONTIME pseudo column

2. You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

A) Cloud SQL
B) Cloud Datastore
C) Cloud Spanner
D) Cloud Bigtable

3. Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A) Cloud Pub/Sub, Cloud SQL, and Cloud Storage
B) Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
C) Cloud Pub/Sub, Cloud Dataflow, and Local SSD
D) Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

4. You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A) Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
B) The Python code should be stored in a revision control system and modified as the incoming data's schema changes
C) Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
D) Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
E) Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
F) Merge the transformed tables together with a SQL query

5. You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A) Add secondary indexes to support query patterns.
B) Add secondary indexes to support query patterns.
C) Use Cloud Spanner for storag
D) Use Cloud Dataflow to transform data to support query patterns.
E) Use Cloud Dataflow to transform data to support query patterns.
F) Use Cloud SQL for storag
G) Use Cloud Spanner for storag
H) Use Cloud SQL for storag

質問と回答：