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Answer: True
Positional encodings add sequence order information to token embeddings, critical for language understanding tasks.
Answer: Attention Mechanism
Self-attention computes weighted relationships between tokens, enabling context-aware processing. Foundation of Transformer success.
Answer: Both A and B
Data catalogs enable discovery; data lineage tracks data flow and transformations. Both critical for data governance.
Answer: Edge
Edge computing processes data near source, reducing round-trip latency to cloud. Critical for IoT, autonomous vehicles.
Answer: True
CEP engines detect complex patterns, correlations, and sequences in real-time streams. Critical for fraud detection, monitoring.
Answer: Both A and B
Kafka excels at high-throughput event streaming; RabbitMQ at flexible routing. Both enable event-driven architectures.
Answer: All of these
Malware analysis combines: static (code inspection), dynamic (runtime behavior), sandboxing (isolated execution).
Answer: Both A and B
Managed secrets managers store, rotate, and control access to credentials, API keys, certificates. Critical for secure cloud applications.
Answer: UEBA / User and Entity Behavior
UEBA uses ML to establish behavioral baselines and detect anomalies indicating compromised accounts or insider threats.
Answer: True
IAM systems manage user identities, authentication, authorization. Critical for implementing least privilege access controls.
Answer: Both A and B
EDR monitors endpoint behavior; device posture assesses security configuration. Both enable continuous verification in Zero Trust.
Answer: All of these
Digital India ecosystem: UMANG for service delivery, MyGov for citizen engagement, DigiLocker for documents.
Answer: True
India's rapid 5G deployment driven by competitive telecom operators, government policy support, indigenous technology development.
Answer: Function
Function calling enables LLMs to invoke APIs, run code, query databases for tasks beyond text generation.
Answer: Transformer
Transformer architecture uses self-attention mechanisms to process sequences in parallel, enabling scaling to billions of parameters.
Answer: Parquet
Parquet is columnar storage format optimized for analytics: efficient compression, predicate pushdown, schema evolution.
Answer: Stream
Stream processing handles continuous data flows for real-time insights: fraud detection, monitoring, personalization.
Answer: All of these
Kafka (messaging), Flink (stream processing), Spark Streaming (micro-batch) enable real-time analytics with varying consistency guarantees.
Answer: Hunting
Threat hunting uses hypotheses, analytics, and analyst expertise to find hidden adversaries. Complements automated detection.
Answer: True
IOCs are forensic artifacts indicating potential breaches. Used for proactive blocking, hunting, and incident response.