AWS Data Engineering - Elements of Data

The five Vs of data are volume, velocity, variety, veracity, and value.

• True

• False

All data needs to be cleaned and transformed before it can be used in data analysis.

• True

• False

Unstructured data is considered cold and takes the most work to analyze.

• True

• False

Streaming ingestion is suitable for data that arrives in small bits at a continuous pace.

• True

• False

When data veracity is compromised, it does not significantly impact decision-making.

• True

• False

What type of data is easiest to query?

• Structured

• Semistructured

• Unstructured

Which of the following is NOT one of the key takeaways regarding data variety?

• Most of the growth of available data is unstructured.

• Data variety does not influence pipeline design.

• • Different data types lend themselves to certain types of processing.

What should be considered when choosing a storage solution for high-velocity data?

• Cost-effectiveness only

• Speed of access and scalability

• Long-term storage capacity only

Which of the following best describes the impact of data volume and velocity on a data pipeline?

• They determine the cost and complexity of the pipeline.

• They have no impact on the design of the pipeline.

• They only affect the ingestion stage of the pipeline.

What is a key consideration when integrating different data sources in a pipeline?

• Reducing the number of data sources

• Ensuring all data is unstructured

• Managing differences in structure and content