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| Section | Weight | Objectives |
|---|
| Building and operationalizing data processing systems | 30% | - Data pipeline implementation
- 1. Dataproc and Spark-based processing
- 2. Dataflow pipeline development
- Data ingestion and transformation
- 1. Pub/Sub streaming ingestion
- 2. ETL/ELT workflows
|
| Operationalizing data and ML pipelines | 30% | - Monitoring and troubleshooting
- 1. Performance optimization and debugging
- 2. Logging and observability
- Pipeline automation and orchestration
- 1. Cloud Composer workflows
- 2. Scheduling and monitoring pipelines
|
| Maintaining and optimizing data and ML solutions | 20% | - Machine learning integration
- 1. Vertex AI integration for pipelines
- 2. BigQuery ML usage
- Security and governance
- 1. IAM and access control
- 2. Data encryption and compliance
|
| Designing data processing systems | 20% | - Storage and data modeling
- 1. Data warehouse design using BigQuery
- 2. Data lake architecture on Google Cloud Storage
- Data pipeline architecture design
- 1. Batch vs streaming data processing selection
- 2. Scalable data ingestion design
|
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Google Data Engineer Sample Questions (Q38-Q43):
NEW QUESTION # 38
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?
- A. Change "person" to "person.city".
- B. Add ", UNNEST(person)" before the WHERE clause.
- C. Add ", UNNEST(city)" before the WHERE clause.
- D. Change "person" to "city.person".
Answer: B
Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma. Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu
NEW QUESTION # 39
You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?
- A. Cloud Functions
- B. Cloud Composer
- C. Cloud Dataflow
- D. Cloud Scheduler
Answer: D
NEW QUESTION # 40
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. Use Cloud SQL for storag
- 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. Add secondary indexes to support query patterns.
- H. Use Cloud Spanner for storag
Answer: E
Explanation:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform
NEW QUESTION # 41
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 Bigtable
- B. Cloud Datastore
- C. Cloud SQL
- D. Cloud Spanner
Answer: C
NEW QUESTION # 42
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?
- A. Cloud Datastore
- B. Cloud SQL
- C. Cloud BigTable
- D. BigQuery
Answer: C
NEW QUESTION # 43
......
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