10 CT Analysing Data Wk12 PostQuiz

Which open-source framework lets you create “expectations” that automatically test whether each batch of data meets defined quality rules before it is used for analysis?

• dbt Core

• Apache NiFi

• Great Expectations

• Apache Superset

During an ETL workflow, at which stage is schema validation most effective for catching type mismatches and missing mandatory fields?

• A. Extraction

• B. Transformation

• C. Loading

• D. Visualization

Amazon’s Deequ library is designed primarily to:

• Generate business-intelligence dashboards

• Orchestrate distributed machine-learning training jobs

• Define unit tests for data quality on Apache Spark datasets

• Replace relational database indexing

Which validation test would best detect the sudden appearance of an unexpected new category in a “payment_type” column?

• Null-value check

• Row-count check

• Domain (allowed-values) check

• Duplicate-row check

Talend Data Quality, Informatica, and Great Expectations all include features that allow analysts to:

• Clean and validate data

• Visualize data trends

• Build machine learning models

• Store large volumes of data

Which of the following is an example of profiling datasets and quantifying data-quality metrics?

• Build GPU-accelerated neural networks

• Profile datasets and quantify data-quality metrics (e.g., completeness, uniqueness, validity)

• Encrypt column-level data at rest

• Perform A/B testing on web applications

A pipeline ingests 15,000 IoT sensor readings every second and triggers near-real-time alerts. Which of the 3 V's is the primary challenge to evaluate in this scenario?

• Volume

• Variety

• Velocity

• Veracity

Combining structured sales ledgers, user-generated videos, and free-text product reviews into one analytics platform mainly tests which of the 3 V's?

• Variety

• Volume

• Velocity

• Validity

When high variety is expected, which storage solution is usually chosen because it can natively accept semi-structured and unstructured data?

• Column-store relational database

• In-memory key-value cache

• NoSQL document database (e.g., MongoDB)

• Write-ahead log

An e-commerce company must archive petabytes of historical click-stream logs while keeping query times reasonable. Which metric is most relevant when evaluating the volume aspect of its big-data architecture?

• Average network latency

• Scalability of storage capacity and parallel read throughput

• Transaction isolation level

• Ratio of read-to-write queries