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Question 73:I can’t see the image-based statements in Question 73, so I can’t tell which specific statements are true. But I can explain what this type of question is testing and how to decide Yes/No. What Question 73 is testing Your understanding of the cloud service models and the shared responsibility model: IaaS, PaaS, and SaaS. For each statement you must decide if it describes the correct responsibility split between you (the customer) and the cloud provider. Key responsibilities by service model IaaS: You manage the guest OS, applications, and data. The provider manages virtualization, servers, storage, and networking. PaaS: You manage the applications and data. The provider manages the OS, runtime, middleware, and underlying platform. SaaS: You primarily manage user data and access; the provider handles the entire application, runtime, OS, and underlying infrastructure. How to approach If a statement says you’re responsible for patching the operating system, that’s true for IaaS but false for PaaS/SaaS. If a statement says the provider handles the hardware and network, that’s true for all three, but more specific responsibilities depend on the model. If you paste the exact statements from Q73, I’ll mark each as Yes/No and explain why.
Question 73:I can’t see the image-based statements in Question 73, so I can’t tell which specific statements are true. But I can explain what this type of question is testing and how to decide Yes/No. What Question 73 is testing
Question 25: Correct answer: D: a new Cognitive Services resource that uses the S0 pricing tier. Why: To enable OCR and text analytics in a Cognitive Search enrichment pipeline, you must attach a Cognitive Services resource to the skillset. For cost efficiency on a large document set, choose the base standard tier (S0) rather than higher tiers or a free tier, which may limit enrichments. The OCR capability comes from the Cognitive Services (Computer Vision/Read) and the S0 tier provides a balance of capability and cost.
Question 25:
Question 20: Correct answer: D and E. Why: Provisioning a QnA Maker service creates: - Azure App Service to host the QnA Maker web API (your App Service Plan AP1 will host the App Service resource). - Azure Cognitive Search to enable fast search over the knowledge base. Why not the others: - Language Understanding, Azure SQL Database, and Azure Storage are not automatically created by QnA Maker provisioning. Quick note: After provisioning, check RG1 to verify the new App Service and Cognitive Search resources.
Question 20:
what if different between speech recognizer and speach synthesizer Speech Recognizer (Speech-to-Text): Converts audio input (live mic or audio file) into text. Output is transcripts with timestamps, confidence scores, and optional punctuation. Use cases: meeting transcripts, voice commands, real-time captions. Speech Synthesizer (Text-to-Speech): Converts text (or SSML) into audio output. Output is audio data (wav/mp3 or streaming). Use cases: voice prompts, IVR responses, accessibility narrations. You can choose voices, languages, and adjust prosody (rate, pitch, volume). Key differences: - Input: Recognizer takes audio; Synthesizer takes text/SSML. - Output: Recognizer ? text; Synthesizer ? audio. - Features: Recognizer provides transcripts and timing; Synthesizer provides customizable voices (neural, SSML), multiple languages. - Typical scenarios: Transcription vs. generating spoken content. Quick example: Use Speech Recognizer to transcribe a meeting; use Speech Synthesizer to read a script aloud in a chosen voice.
what if different between speech recognizer and speach synthesizer
Question 17: Correct answer: A Anomaly Detector. Why: Anomaly Detector is designed to identify unusual values in time-series data. Your scenario has 100 machines × 50 sensors, generating minute-by-minute data, totaling 5,000 time-series. Anomaly Detector can process each time-series (or batches of series) to flag deviations that may indicate potential failures, which is exactly what you need for predictive maintenance. Why not the others: - Cognitive Search is for indexing and querying content, not for detecting anomalies in time-series data. - Form Recognizer extracts data from forms, not time-series sensor data. - Custom Vision analyzes images, not numeric sensor streams. Practical note: with 5,000 time series, you’d typically run anomaly detection per series (potentially in parallel) and aggregate results to identify which machines/sensors warrant attention.
Question 17: