A media company collects user clickstream logs (JSON files), video metadata, and customer support transcripts (unstructured text). They also have a relational database for billing. They want to run ad-hoc exploratory analytics across all these data types and also support a real-time dashboard for active users. Which storage approach best fits their needs?

Store everything in a single relational database and add columns for the unstructured data.

Use a data lake for all raw data and a data warehouse for the structured billing data and curated datasets.

Use only a data lake and run all analytics directly on the raw files.

Use only a data warehouse and load all unstructured data as text columns.

A logistics company has a massive amount of GPS data from delivery trucks, but it is stored in on-premises file servers in various formats. They want to analyze this data to optimize routes but lack the compute capacity to process it. What is the most effective way to unlock value from this data?

Delete old GPS data to reduce the volume and make analysis easier.

Hire more data analysts to manually review the GPS files.

Migrate the data to cloud storage and use cloud-based data processing services.

Purchase more on-premises servers to handle the processing load.

In the data value chain, what is the purpose of the 'analysis' stage?

To transform raw data into insights that inform decisions.

To collect data from various sources.

To implement decisions based on insights.

To store data in a secure and accessible manner.

A financial services firm needs to store transaction records for regulatory compliance and run complex analytical queries for fraud detection. The data is highly structured and must be queryable with low latency. Which storage solution is most appropriate?

A relational database used as the primary system of record.

A data lake to store all raw transaction files.

A NoSQL database for flexible schema storage.

A data warehouse optimized for analytical queries.

How does data generate business insights that inform decision-making?

By providing raw facts that are directly used without analysis.

By storing data in large databases for future reference.

By replacing human judgment entirely with automated decisions.

By being analyzed and interpreted to reveal patterns, trends, and correlations.

Google CloudCloud Digital Leader

Domain 2Objective 1

The Value of Data CLOUD-DIGITAL-LEADER Practice Questions (Page 1)

Part of the Exploring Data Transformation with Google Cloud domain, which accounts for ~16% of the CLOUD-DIGITAL-LEADER exam.

29questions here

6free pages

7concepts

~16%of the exam

Questions 1–5

1application · medium
A media company collects user clickstream logs (JSON files), video metadata, and customer support transcripts (unstructured text). They also have a relational database for billing. They want to run ad-hoc exploratory analytics across all these data types and also support a real-time dashboard for active users. Which storage approach best fits their needs?
Select an answer first
2application · medium
A logistics company has a massive amount of GPS data from delivery trucks, but it is stored in on-premises file servers in various formats. They want to analyze this data to optimize routes but lack the compute capacity to process it. What is the most effective way to unlock value from this data?
Select an answer first
3foundation · easy
In the data value chain, what is the purpose of the 'analysis' stage?
Select an answer first
4application · medium
A financial services firm needs to store transaction records for regulatory compliance and run complex analytical queries for fraud detection. The data is highly structured and must be queryable with low latency. Which storage solution is most appropriate?
Select an answer first
5foundation · easy
How does data generate business insights that inform decision-making?
Select an answer first

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