SKILL BENCHMARK
Exploring Data Transformation with Google Cloud Literacy (Beginner Level)
- 30m
- 30 questions
The Exploring Data Transformation with Google Cloud Literacy (Beginner Level) benchmark assesses your exposure to data management and analytics using Google Cloud's storage and processing solutions. You will be evaluated on your knowledge of working with data, storage options for unstructured and structured data, enterprise data warehouses, and data processing pipelines. A learner who scores high on this benchmark demonstrates entry-level proficiency in data services and tools and an understanding of key concepts and jargon in Google Cloud data transformation.
Topics covered
- access GCS via the Cloud Console and create a bucket
- administer a Cloud SQL instance, create new users and databases, and modify security and network settings
- analyze generation and metageneration attributes
- change the instance state by setting an activation policy and connect to a Cloud SQL instance
- contrast Cloud SQL and other relational database management system (RDBMS) offerings
- contrast lift and shift and database migration approaches
- create and modify temporary holds on objects
- create, modify, and lock bucket retention policies
- define and analyze data governance
- define and mitigate the problem of data silos
- define object life cycle rules
- define unified batch and stream processing and outline the role of Dataflow and Apache Beam
- distinguish between first-, second-, and third-party data
- identify the place of GCS in Google Cloud offerings
- identify trade-offs when choosing HA configurations
- identify use cases of data lakes in big data and AI/machine learning (ML) processing
- implement object versioning
- leverage Cloud SQL Studio for web-based querying
- list the benefits and limitations of Firestore
- list the relational database offerings on Google Cloud
- outline considerations in choosing the right storage technology
- outline the features of Cloud SQL
- outline the role of Pub/Sub in messaging and streaming use cases
- outline the steps in the data value chain
- outline the strengths of BigQuery for online analytical processing (OLAP) uses
- outline the various considerations in choosing Bigtable
- provision a Cloud SQL instance using MySQL and observe configuration options for virtual machines and storage configuration, data protection, and maintenance
- use gsutil to create and list the details of buckets
- use gsutil to work with buckets
- work with folders and upload files to a bucket
