DASCA SDS Exam With Confidence Using Practice Dumps

The Chief Data Monetization Officer (CDMO) is a C-level executive role emerging in data-driven organizations. Their focus is on turning data into business value and revenue growth.

Statement i: Correct. The CDMO is responsible for value realization from data and analytics investments.

Statement ii: Correct. The CDMO owns decisions around investment prioritization for data initiatives.

Statement iii: Correct. Since monetization is directly linked to business performance, the CDMO must be accountable for revenue and margin impact.

Hence, all three statements are correct, making the right answer Option C (All of the above).

In hypothesis testing, two main types of errors are defined:

Type I Error (Option A): Occurs when the null hypothesis (H₀) is true, but we incorrectly reject it. This is known as a false positive. Example: Concluding a drug is effective when it is not.

Type II Error (Option B): Occurs when the null hypothesis (H₀) is false, but we fail to reject it. This is a false negative. Example: Concluding a drug has no effect when it actually does.

Logical Error / Hypothesis Error (Options C and D): Not standard terms in statistical hypothesis testing.

Thus, the “wrong negation of a true null hypothesis” refers to a Type I Error (false positive).

Apache Avro is a widely used framework within the Hadoop ecosystem for data serialization and data exchange.

Option A (Correct): Avro is a compact, fast, binary data serialization format. It allows efficient storage and exchange of structured data.

Option B (Correct): Avro supports Remote Procedure Call (RPC). It provides a framework for RPC communication, making it easier for distributed applications to exchange data across systems.

Option C: Correct, since both statements are true.

Option D: Incorrect because Avro is indeed both a serialization framework and RPC-based.

In data engineering workflows, Avro is valuable because it is schema-based (defined using JSON), highly interoperable, and ensures compatibility across different programming languages. This makes it essential in big data pipelines, Kafka messaging, and Hadoop ecosystem tools.

Thus, the correct answer is Option C (Both A and B).