Create a custom practice set
Pick category, difficulty, number of questions, and time limit. Start instantly with your own quiz.
Generate QuizPick category, difficulty, number of questions, and time limit. Start instantly with your own quiz.
Generate QuizNo weekly quiz is published yet. Check the weekly page for the latest updates.
View Weekly PageFree practice for SSC, UPSC, Banking & Railway exams. No login required.
Answer: All of these
MLOps combines CI/CD, drift detection, A/B testing for production ML.
Answer: Function
Function calling enables LLMs to invoke APIs, run code, query databases.
Answer: True
PEFT methods update small subset of parameters, reducing compute/memory.
Answer: Thought
Chain-of-Thought prompting encourages LLMs to generate reasoning steps.
Answer: Clustering
Clustering discovers natural groupings in unlabeled data.
Answer: True
Feature engineering: domain knowledge, transformations, interactions, encoding.
Answer: Time Series Forecasting
Time series forecasting models temporal patterns for demand prediction, anomaly detection.
Answer: Serverless
Serverless query engines enable SQL queries on object storage using schema-on-read.
Answer: True
Data lakes store raw structured/unstructured data; warehouses store curated data.
Answer: Parquet
Parquet is columnar storage format optimized for analytics: compression, predicate pushdown.
Answer: Stream
Stream processing handles continuous data flows for real-time insights.
Answer: True
CDC captures database operations and streams to targets for real-time analytics.
Answer: True
IOCs are forensic artifacts indicating potential breaches. Used for proactive blocking.
Answer: True
Micro-segmentation creates granular security zones, containing breaches and limiting attacker movement.
Answer: Both A and B
MFA requires multiple authentication factors; adaptive authentication adjusts requirements based on risk context.
Answer: FAME
FAME provides demand incentives for EVs and charging infrastructure.
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.