Free DASCA SDS Practice Exam with Questions & Answers | Set: 3

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).

The monetization exercise is a critical phase in Big Data Business Model Maturity where organizations turn insights into new revenue opportunities.

Option A: Correct. Monetization often uncovers new product or service opportunities by leveraging analytics.

Option B: Correct. The first step is analyzing product usage patterns and customer behaviors to identify hidden value.

Option C: Correct. Insights help in building secondary recommendations or complementary products/services (cross-sell, upsell opportunities).

Thus, all three statements are valid, making the correct answer Option E (All of the above).

A Big Data Strategy document provides a framework for aligning data initiatives with organizational objectives. It typically includes:

Business strategy (Option A): Ensures that big data initiatives align with overall corporate strategy.

Key decisions (Option B): Identifies the decisions data will help optimize or automate.

Key business initiatives (Option D): Links big data projects with critical organizational initiatives.

Key business entities (Option E): Defines the core entities (customers, products, channels) around which data will be organized.

However:

Business decisions (Option C): This is redundant and not a standard section; "key decisions" covers this aspect already.

Thus, the correct answer is Option C (Business decisions).

Regression is a supervised learning technique where a model estimates the relationship between input features (independent variables) and an output (dependent variable).

Option A: Correct. The learning process involves optimizing model parameters (e.g., coefficients in linear regression) to minimize approximation error. Common loss functions include Mean Squared Error (MSE) or Mean Absolute Error (MAE).

Option B: Correct. Minimizing error enables the model to produce the closest possible outcomes to the actual observed values, ensuring accurate predictions.

Option C: Correct, since both A and B are true.

Option D: Incorrect.

Thus, regression optimization in machine learning aims to minimize approximation error and generate closest possible outcomes, making Option C the correct answer.

Machine Learning has broad applications across industries and technologies:

Fraud Detection (Option A): Detecting anomalies in financial transactions, credit card usage, and cybersecurity threats.

Web Search Results (Option B): Ranking algorithms (e.g., Google’s PageRank enhanced by ML techniques) improve relevance of search queries.

Real-time Ads (Option C): Online ad systems use reinforcement learning and recommendation models to target ads dynamically.

Pattern & Image Recognition (Option D): ML (especially deep learning) powers facial recognition, handwriting recognition, medical imaging, etc.

Since ML is used in all these applications, the correct answer is Option E (All of the above).