The correct option is You can capture multimodal cues such as tone and emotion for deeper insights although storage and compute costs increase.

Video interviews carry audio and visual signals that text cannot provide which can improve the richness of insights. Tone of voice, pacing, facial expressions and gestures can all help models detect sentiment and nuance more effectively than typed responses. This benefit comes with larger files and more complex processing which means higher storage usage and greater compute time for feature extraction and model inference. On Google Cloud you would typically store the raw footage in Cloud Storage and extract transcripts or features for downstream analytics in BigQuery or Vertex AI.

Model training completes faster because video contains denser information is incorrect because video is high dimensional and includes many frames and an audio track which expands the data volume and typically increases preprocessing and training time rather than reducing it.

Store the raw interviews in BigQuery to reduce costs and simplify analytics is incorrect because BigQuery is a data warehouse optimized for structured and semi structured data such as tables and it is not intended for storing large binary video files. Raw media should be stored in Cloud Storage and only derived metadata or transcripts should be loaded into BigQuery for analytics.

Overall storage and processing expenses will be substantially lower with video is incorrect because video requires significantly more storage than text and it also increases compute for decoding, transcription and visual analysis which generally raises costs.

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