Huawei H13-321 Exam Actual Questions
HCIP-AI-EI Developer V2.0 (Page 7 )

• Correct answer: B — A reservation for the server based on the MAC address.

• Why this is the best choice:

• Why the other options are less suitable:

• Quick takeaway: Use a DHCP reservation to give the file server a fixed IP while still benefiting from centralized DHCP management.

• RPO (Recovery Point Objective) — maximum acceptable data loss, measured in time. It specifies how far back in time you can tolerate data loss. Example: RPO of 2 hours means backups must occur at least every 2 hours.

• ARO (Annualized Rate of Occurrence) — expected frequency of a threat occurring per year. It estimates how often a risk is likely to happen on average (used in risk calculations).

• MTTR (Mean Time To Recover) — average time to recover/restore service after an incident. It includes detection, repair, and restoration time, and helps gauge downtime and alignment with RTO.

• Correct answer: C: Amazon Comprehend.

• Why: The library wants to classify books based on their text content. This is an NLP (natural language processing) task. Amazon Comprehend is AWS’s managed NLP service that can perform content classification (including creating custom classifiers) and extract meaningful information from text. The other services are not focused on text classification:

• Quick note: In practice, you’d store the books in S3, extract text if needed (e.g., from PDFs with Textract), and then use Comprehend to classify the documents into topics or genres.

• TCP is connection-oriented. It establishes a connection before data transfer using a three-way handshake (SYN, SYN-ACK, ACK), enabling reliable delivery, ordering, and flow control.

• UDP is connectionless. It sends datagrams without a prior connection and provides no guarantee of delivery, order, or protection against duplicates; any reliability is up to the application.

• B incorrectly states UDP uses acknowledgment packets; UDP has no built-in ACKs.
• C wrongly claims UDP provides reliable transfer and that TCP is connectionless.
• D misstates framing bits; neither protocol uses the listed TCP/UDP handshake bits in frames.

• The missing piece is to configure an event source mapping between the DynamoDB stream and the Lambda function. Enabling DynamoDB Streams alone doesn’t trigger the function—you must wire the stream to Lambda so events invoke the function.
• Note that changing StreamViewType (A) only affects what data is delivered (e.g., NEW_IMAGE) and does not trigger invocations. Options C and D are not required for invocation: you don’t need SNS, and increasing the timeout won’t enable the trigger.

• Port 515: LPD/LPR line-printer service
• Port 631: IPP — Internet Printing Protocol
• Port 9100: JetDirect/RAW printing, commonly used by network printers

• Linux machine: Linux could run these services, but the three printer-specific ports strongly indicate a printer.
• Router: Routers may use Telnet and HTTP, but normally would not expose ports 515, 631, and 9100 together.
• Windows machine: Windows can share printers, but this particular service combination is more characteristic of a network printer’s embedded operating system.

• prompt: the input or instruction given to the model
• completion: the desired response

json 
{"prompt":"Summarize this customer complaint.","completion":"The customer reports a delayed shipment."}

• B: A .txt file containing CSV-style lines is not automatically a valid fine-tuning dataset. The required structure and format must match the selected Bedrock model.
• C: Provisioned Throughput provides dedicated inference capacity; it does not fine-tune a model.
• D: Journals and textbooks might support domain adaptation or pretraining, but they do not provide the prompt-to-answer examples typically required for supervised fine-tuning.

• Answer: C — Spot Instances

• Why: The workload is a test environment that can be interrupted and does not need to run continuously. Spot Instances let you bid on unused EC2 capacity at a deep discount, making them the most cost-effective option for interruptible workloads.

• Why not the others:

• Quick tips:

• Each statement is evaluated as True (Yes) or False (No) independently.
• Key Azure facts to recall:

• Correct answer: A — The degradation of a service.

• Why: A service request is a formal request from a user for something that is pre-approved and routine (e.g., provisioning a laptop, replacing a toner cartridge). Those are typical service request actions. The degradation of a service, however, is an incident: it describes a problem affecting service quality or availability and is handled by Incident Management (and possibly Problem Management), not as a standard service request.

• Quick note on the other options:

• IPv6 address types you’ll typically see:

• Unique Local Addresses (ULAs)

• If an address begins with fdXX:… or fc00:…, place it under “Unique Local Address” (ULA).
• If it begins with 2000::/3, place it under “Global Unicast.”
• If it begins with fe80::, place it under “Link-local.”
• If it begins with ff00::, place it under “Multicast.”

• Correct choices: A and D.

• Why not B, C, or E?

• Side loading means installing software from sources outside official or trusted repositories.
• This process can bring in malicious payloads because the installed software hasn’t been vetted. A rootkit is a type of malware that hides its presence and maintains privileged access, making it a prime risk when using untrusted sources.
• Why not the other options:

• Only install apps from trusted sources; verify signatures.
• Use application whitelisting and security controls to block sideloading.
• Keep systems updated and use anti-malware tools capable of detecting rootkits.

• runParallel is defined as async, so it returns a Promise.
• To use the result, you attach a then handler (or use await inside another async function).

• A: “Async runParallel() .then(data);” is invalid. “async” is a keyword for function declarations, not something you put in front of a call, and you’d need a function as the then-argument.
• B: “runParallel().done(function(data){return data;});” .done is not standard in native Promises (it exists in some libraries). Without a compatible library, this won’t work.
• C: “runParallel().then(data);” is invalid because then expects a function as its argument; data is not a function.
• D: “runParallel().then(function(data){ return data; });” is valid (or equivalently, runParallel().then(data => data);).

• The robust, standard option is D.
• B could work only with a library that provides .done; otherwise it’s not reliable.

• State is stored remotely in Terraform Cloud/Enterprise, not as a local file.
• Terraform runs (plan/apply) can be executed on remote infrastructure, with results visible in the remote workspace UI.
• Supports workspace-based organization, state locking, and concurrency control.
• Useful for team collaboration, centralized governance, and auditable logs.

• In your configuration, you use a backend block like backend "remote" { ... }.
• You still run terraform init to configure/migrate to the remote backend.
• After setup, terraform plan/terraform apply may be executed remotely, and the outputs/logs appear in the Terraform Cloud/Enterprise UI rather than (only) in your local terminal.

• Other backends store state remotely but typically do not provide remote execution; with the remote backend you get remote runs and a collaborative UI, not just remote state storage.

• Context: You’re configuring a Windows Autopilot deployment profile (Profile1) for Windows 11 devices. The hotspot asks you to pick the correct behavior for two statements based on the profile’s settings.

• Box 1: “can modify the desktop settings only for themselves”

• Box 2: “Cortana settings” with options like computer name, keyboard layout, etc.

• In Azure, the typical cost approach for pay-as-you-go VMs is an Operational Expenditure (OPEX) model. You’re billed for actual usage each month.
• The term “elastic expenditure model” isn’t a standard Azure cost category. Elasticity describes scaling capacity, not the billing category itself.
• So recommending the elastic expenditure model is not correct for this goal.

• The solution does not meet the goal. Answer: No (B).

• Pay-as-you-go aligns with OPEX because costs vary with usage.
• Elasticity is about scaling resources, which affects performance and capacity, not the fundamental cost model.

• A) configure matching hold and delay timers

• B) configure matching key-strings

• C) configure matching priority values

• D) configure unique virtual IP addresses

• The question asks why some tasks appear locked in the HR Guided Setup Module’s Task view.
• The options are:

• C) They require other tasks to be completed first. In guided setup flows, tasks are usually gated by prerequisites to ensure proper sequencing and data/configuration dependencies. You typically unlock a task only after the necessary preceding tasks are finished.

• A) Elevated roles can restrict access in some contexts, but guided setup tasks are commonly gated by prerequisites rather than solely by role.
• B) Deprecation isn’t the general pattern for “locked” tasks in the guided setup.
• D) A plugin activation could unlock certain features, but the common gating mechanism is task dependencies, not plugin state.

• The page shows the answer as D, but based on how HR Guided Setup usually works, the more accurate reason is typically C (prerequisites). If you’re studying, treat C as the conceptual answer and be aware the listed key may be incorrect. If you want, I can walk through each option in more detail.

• Correct answer: B (No, it does not).

• A purchase agreement in Dynamics 365 SCM is a vendor-side price arrangement. It sets prices/discounts for what your company buys from suppliers, not for what you sell to customers.
• The objective in Question 2 is to give clients a quantity-based discount on items they purchase from you. To accomplish this, you should configure a sales price / trade agreement (on the customer/sales side), not a purchase agreement.
• In a sales price trade agreement, you can define quantity breaks such as:

• Create a Sales price trade agreement for the item and relevant customer (or customer group).
• Add quantity breaks: 51–100 with 10% discount, 101+ with 15% discount.
• Ensure the agreement is active for the desired date range.

• Interactive development and exploration
• Small datasets
• Lightweight transformations or testing
• Avoiding the overhead and cost of multiple workers

• Automated reports: Jobs may benefit from job clusters or SQL warehouses.
• Working with SQL in Databricks SQL: Databricks SQL warehouses are the relevant compute option.
• Automatically scaling for larger data: A multi-node cluster with autoscaling is more suitable.
• Large amounts of data: Distributed worker nodes are generally needed.

• Answer:

• Why:

• Why the other centers aren’t the right fit here:

• A assigned without var becomes a global variable (in non-strict mode). So after myFunction(), a = 5 is accessible.
• b is declared with var inside the function, so it’s local to that function (function-scoped). It’s not defined in the outer (global) scope.

• console.log(a) prints 5.
• console.log(b) throws a ReferenceError: b is not defined.

• Create Power Query parameters for the Azure SQL server/database—or at least the database name.
• Use the parameter in the PBIX connection.
• Publish the same PBIX separately to the relevant development, test, and production workspaces.
• Set each dataset’s parameter value in the Power BI service so it points to the corresponding Azure SQL database.

• Development dataset ? DevelopmentDB
• Test dataset ? TestDB
• Production dataset ? ProductionDB

• The same PBIX is used for every lifecycle stage.
• Each stage consumes data from its matching database.
• Deployment pipelines are not required.
• Azure SQL is a cloud source, so gateway administration is generally avoided.
• Only the parameter value differs between published datasets, reducing maintenance.

• Correct answer: D) Amazon Aurora Serverless

• Aurora Serverless is serverless, so compute capacity scales automatically and can pause during idle periods. This minimizes ongoing administration and you pay only for actual usage, which is ideal for infrequent workloads.
• PostgreSQL on EC2 requires full OS and DB administration.
• RDS for PostgreSQL is managed but still involves provisioning capacity and some maintenance tasks.
• Aurora PostgreSQL (non-Serverless) is managed but requires you to manage capacity, though with some automation; it’s not as hands-off as Serverless for intermittent usage.

• MFA verifies a user’s identity using additional authentication factors, such as a phone prompt or security token.
• It helps prevent unauthorized access even if a password is compromised.
• It does not provide secure storage or encryption of credentials used during automated deployment.

• A? ??? Aurora Replicas? ?? ??? ???? ?? ???, ?? ???? Network Load Balancer(NLB)? ??????. NLB? ?? 4 ?? ???? HTTP ??? ?? ???(Sticky sessions)? ????? ???? ????.
• ??? ?? ??????? ?? ?? ???? ??? ???? ?? ????? ????? ?? ?? ???(?? ?/??? ??)? ?????. ? ??? ALB?? ?? ????, NLB?? ???? ?????.
• ??? ??? ??? ??? ???? ?? ???? ???? HTTP ???? ??? ALB? ???? ???. A? ? ??? ??????.

python 
import azure.cognitiveservices.speech as speechsdk 


speech_config = speechsdk.SpeechConfig( 
    subscription="YOUR_KEY", 
    region="YOUR_REGION" 
)

Important Azure Speech SDK classes

• SpeechConfig

• AudioConfig

python 
  audio_config = speechsdk.audio.AudioConfig( 
      use_default_microphone=True 
  )

• SpeechRecognizer

python 
  recognizer = speechsdk.SpeechRecognizer( 
      speech_config=speech_config, 
      audio_config=audio_config 
  )

• SpeechSynthesizer

• AudioOutputConfig

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