Last updated on May 12, 2026
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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:
Question 12: Correct answers: A, B, F. Why: - A. The index size will increase. Enabling CMK encryption adds encryption metadata and key management data, which increases index size. - B. Query times will increase. Encryption/decryption overhead and key retrieval can slow queries. - F. Azure Key Vault is required. CMK means you store/manage keys in Key Vault; it’s a billable, required service for CMK. Why the other options are incorrect: - C (self-signed X.509 certificate required) is not a requirement for CMK. - D (index size will decrease) and E (query times will decrease) contradict the expected impact of CMK.
Question 12:
GIF also we are using ? No, GIF is not supported for Form Recognizer input. The allowed formats are JPG, PNG, PDF (text or scanned), or TIFF. If you have GIFs, convert them to one of the supported formats (e.g., convert to JPG/PNG or PDF) before training or uploading. Also ensure each file is under 50 MB and, when possible, use text-embedded PDFs to reduce errors.
GIF also we are using ?
Question 11: Correct answers: A, C, F. Why: - Form Recognizer accepts documents in these formats: JPG, PNG, PDF (text or scanned), or TIFF. (TIFF is sometimes used in archival/scanned docs.) - Text-embedded PDFs are preferred because they minimize character extraction and location errors. - Training data must be under 50 MB per file. So A, C, and F are the three files that meet these input requirements; the other options (B, D, E) likely fail one of the criteria (wrong format or too large). If you want, describe the formats of A, C, and F to confirm they meet all three criteria.
Question 11:
Question 10:The correct answer is B: A new query key was generated. Explanation: The REST call to: POST .../regenerateKey?api-version=2017-04-18 with body {"keyName": "Key2"} regenerates the specified account key. Since you specified Key2, only the secondary key is regenerated; the primary key (Key1) remains unchanged. This operation updates the Cognitive Services account keys within Azure, not anything in Azure Key Vault. “Query key” refers to the key used to authorize API requests to the service (subscription key), so regenerating Key2 yields a new value for that key.
Question 10:The correct answer is B: A new query key was generated. Explanation:
Why pull first, its create is first is itYes. The sequence is correct because you need a base image first. Step 1: Pull the base container image (Anomaly Detector) to have a starting point. Step 2: Create a Dockerfile to capture the exact changes you want (reproducible build). Step 3: Build and push the customized image to Azure Container Registry (ACR). Step 4: Distribute a docker run script to deploy the container on devices. Why not start with the Dockerfile? You need the base image to reference in the FROM line, and you can only push a built image to ACR, not an unbuilt modification.
Why pull first, its create is first is itYes. The sequence is correct because you need a base image first.
Anomaly Detector
Dockerfile
docker run
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