Modernizing Data Lakes and Data Warehouses with Google Cloud

There are a number of common challenges encountered by data engineers. Which of the following approaches best address the challenge of data accuracy and quality?

• Build ETL pipelines to clean and transform data that is then stored in a data warehouse.

• Optimize queries for performance

• Consolidate disparate datasets and data formats into a data lake.

• Manage server and cluster capacity and make sure that enough capacity exists to carry out the ETL jobs

State True or False in terms of Cloud storage

1. a. Coldline Storage is ideal for data you plan to read or modify on average once per month or less.

2. b. Nearline Storage is a better choice than Standard Storage or Coldline Storage in scenarios where slightly lower availability, a 90-day minimum storage duration, and higher costs for data access are acceptable trade-offs for lowered at-rest storage costs.

• TT

• TF

• FT

• FF

________ is used when transformation occurs in an intermediate service before it is loaded into the target.

• ELT

• ETL

• EL

• ET

1. State True or False:-

   1. a. BigQuery supports clustering for both partitioned and non-partitioned tables.

   2. b. ARRAYS can be a standalone field type or part of a STRUCTS field in BigQuery?

• TT

• TF

• FT

• FF

Which of the following statements (select two)  are  incorrect in respect to Normalization:-

• Normalizing increases the orderliness of the data, it is useful for saving space.

• Normalizing the data means turning it into a relational system.

• Normalizing is the strategy of allowing duplicate field values for a column in a table in the data to gain processing performance.

• Normalizing data enables BigQuery to more efficiently distribute processing among slots resulting in more parallel processing and better query performance.