The Open Group OGEA-10B Exam Actual Questions
TOGAF Enterprise Architecture Bridge (Page 10 )

• A: True. You can change the size of a Virtual Warehouse after creation (via UI or ALTER WAREHOUSE).

• B: True. A warehouse can be resized while it’s running; the resize will take effect without needing to stop queries.

• C: True. A warehouse can be configured to suspend after a period of inactivity (AUTO_SUSPEND).

• D: True. A warehouse can be configured to auto-resume when new queries are submitted (AUTO_RESUME).

• The Balanced Scorecard translates an organization’s vision and strategy into a set of concrete objectives and metrics across four perspectives (financial, customer, internal processes, learning & growth). This framework explicitly links IT goals to business goals and provides a way to measure and monitor alignment over time.

• Enterprise architecture (EA): Great for governance, standards, and roadmaps to ensure IT assets support business needs, but it’s more about structure and alignment at design time rather than directly measuring ongoing alignment of goals.
• KPIs: Useful metrics, but they don’t by themselves provide a full framework to ensure IT goals are aligned with organizational strategy.
• Enterprise dashboard: A visualization tool for metrics; it helps report status but doesn’t inherently establish or assess alignment.

• Disabling GPS: Affects location services, not email connectivity.
• Configuring 4G usage only: Does not enable international data access and could prevent connectivity if 4G is unavailable.
• Connecting using a hotspot: Could provide data, but it is not the most direct solution and depends on another network or device.

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• Prompts and Suggestions: Retained for 28 days
• User Engagement Data: Kept for Two Years

• Security Development Operations (SDO): a security-focused take on DevOps, integrating security into the development pipeline.
• Service Data Object (SDO): a data representation used in some middleware or enterprise apps.
• Software-Defined Overlay (SDO): a networking overlay concept.
• Secure Data Object: a generic data-security term.

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

• The correct answer: B) Synthetic identity theft.

• Synthetic identity theft involves creating a new, forged identity by combining information from multiple real people (e.g., names, dates of birth, SSNs) to commit fraud.
• In the scenario, Adam “created a new identity by obtaining information from different victims,” which matches the idea of synthesizing data to form a fake person.

• Medical identity theft: using someone’s medical information to obtain care or services.
• Tax identity theft: using someone’s personal info to file fraudulent tax returns.
• Social identity theft: impersonating someone to deceive others socially or publicly.

• Look for phrases like “create a new identity” or “combine information from multiple people” to identify synthetic identity theft.

• The correct answer is “Think and work holistically” (Option B).

• Focus on value: ensures outcomes matter to stakeholders, but isn’t specifically about end-to-end delivery.
• Optimize and automate: about efficiency and performance, not the holistic view.
• Collaborate and promote visibility: about teamwork and sharing information, not the end-to-end scope.

• The target API uses HTTPS, so Mule must validate the server’s TLS certificate.
• The TLS configuration is set to None, meaning no custom truststore is configured.
• Mule therefore uses the JRE’s default truststore to determine whether the target certificate is trusted.
• If the issuing CA certificate is in that truststore, the TLS handshake can succeed.
• If it is absent—or the certificate is otherwise invalid—the request fails with a TLS/handshake error.

• A Storage Gateway File Gateway provides an on-premises NFS/SMB file share that's backed by objects in S3. This is exactly what you need to migrate an NFS workload: applications continue using NFS, while the actual data moves to and is stored in S3.
• It can cache frequently accessed data locally for performance, while uploads go to an S3 bucket.
• Why the other options aren’t suitable:

• Answer: C — 192.168.0.0/25 as summary and 192.168.0.0/27 for each floor

• Why:

• 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:

• Answer: A) Clear the Windows event logs

• Windows event logs (System, Security, Application) are key forensic artifacts. If an attacker clears these logs, there’s no built-in local record of actions taken, making it harder for investigators to trace what happened.

• Modify the system time: may misalign timestamps but does not erase past events and can be easily detected.
• Alter log permissions: could prevent new log entries, but it doesn’t remove existing ones and can raise alarms due to permission changes.
• Reduce log retention settings: affects only future logs; it doesn’t wipe already recorded activity.

• In real environments, defenders mitigate this risk with tamper-evident, centralized logging (remote SIEM, write-once storage, log integrity checks) to preserve evidence even if a host is compromised.

• The correct answer is A: CCTV recordings are not regularly reviewed.

• CCTV is a monitoring/control mechanism. If recordings aren’t reviewed, security events (break-ins, policy violations, equipment tampering) can go undetected and unaddressed.
• Regular review is essential for deterrence, incident response, and forensic evidence. Without it, the entire CCTV control provides little value.

• B (records deleted after one year) affects forensic availability, but it’s a data-retention issue rather than the ongoing effectiveness of the surveillance program.
• C (footage not recorded 24x7) creates gaps in coverage, which is a concern, but regular review of available footage still supports detection; 24x7 coverage is a separate policy/perimeter issue.
• D (cameras not in break rooms) is more about privacy and space planning than core data-center security monitoring.

• Goal: Identify the host that originated the infection, then label each remaining host as infected or clean based on log evidence.

• Logs to review (across hosts and firewall):

• How to identify the origin:

• How to classify other hosts:

• Quick tip: use the Reset All option if you want to re-run the scenario and practice again.

• SageMaker endpoint: Not required because the model is being used through Amazon Bedrock.
• SageMaker Model Registry: Used for managing SageMaker models, not for making a Bedrock custom model available.
• Grant access in Amazon Bedrock: IAM permissions may still be needed, but granting access alone does not provide the required inference capacity.

• Short answer: The correct term is Anti-forensics (option D), not “Felony.”

• Why: The actions described—hiding files in slack space, changing file headers, placing suspicious files in executables, and altering metadata—are classic anti-forensics techniques. They’re used to hinder or obscure forensic analysis, not to label the act as a legal category.

• About the options:

• Note: The provided answer key lists B (Felony), which is misleading for this exam objective. The exam typically tests knowledge of forensics concepts, where anti-forensics is the correct category.

• 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.

• AWS IoT Greengrass supports local ML inference using models trained in Amazon SageMaker.
• The model and inference code run on the factory’s local server, so processing continues even when internet connectivity is unavailable.
• A Greengrass component can:

• This provides low latency and avoids sending images to AWS for every inference.

• A: Uses AWS-hosted services and therefore depends on internet connectivity. It would fail during an outage.
• C: Snowball Edge is unnecessary for this use case and adds deployment complexity. A local Greengrass server is the intended edge-inference solution.
• D: Amazon Monitron is designed for monitoring industrial equipment health, not analyzing camera images with a custom SageMaker model.

• Ablauf: Er wird in der gleichen Transaktion ausgeführt, bei der z. B. ein Datensatz erstellt oder aktualisiert wird. Der Benutzer wartet, bis der Workflow abgeschlossen ist.
• Stufen: Kann vor dem Vorgang (pre-operation) oder nach dem Vorgang (post-operation) laufen.
• Zweck: Sofortige Validierung oder Anpassung von Daten, ggf. auch das Verhindern eines Vorgangs, wenn Bedingungen nicht erfüllt sind.

• Automatisierte Flows (Power Automate, automatisierte/ Background Flows) laufen asynchron im Hintergrund, nachdem der Trigger erkannt wurde, und blockieren den Benutzer nicht.

• The 99.99% availability SLA for VMs is achieved when you run VMs across two or more Availability Zones.
• Two VMs in a single Availability Zone do not meet the 99.99% target (their SLA is lower, e.g., around 99.95% or less depending on configuration).

• Deploy at least two VMs across two different Availability Zones (one VM in each zone, or both in two zones with a load balancer).
• This provides the required zone redundancy and the 99.99% SLA.

• Amazon DynamoDB — Managed NoSQL database, not PostgreSQL.
• Amazon Athena — Serverless service for querying data in S3; intended for analytics, not OLTP.
• Amazon EMR — Managed cluster platform for big-data frameworks such as Hadoop and Spark.

• Two VMs
• Distributed across two separate availability zones

• Permanent table (E): Correct. A dropped permanent table can be restored with UNDROP TABLE during its Time Travel retention period. Permanent tables also have Fail-safe protection.
• Transient table: It can also generally be restored during its shorter Time Travel period, although it has no Fail-safe. Therefore, excluding it makes the question questionable.
• Temporary table: Usually cannot be recovered after it is dropped; its lifecycle is limited to the session.
• Materialized view (D): The answer key treats it as recoverable, but recovery behavior depends on the specific Snowflake object/version capabilities. It should not be confused with an ordinary view.

• Question 142: Security governance is MOST associated with which IT infrastructure component? Network / Application / Platform / Process
• Answer: D. Process

• Security governance is about how decisions are made, accountability, and oversight to ensure security objectives align with business goals. These are implemented through governance processes (policy development, risk management, assurance, monitoring, escalation, etc.).
• The other options (Network, Application, Platform) are concrete IT components; governance affects them, but it’s exercised primarily through the processes that govern how those components are managed and controlled.

• In governance-related questions, focus on the decision-making, accountability, and control processes rather than the underlying technology components.

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• A ??????? system software ????????????????????????????????? ??????????????????????????????????
• B ?????????????????????????? ????????????????????????? ?????????????????????????????
• D ????????????????????????? ?????????????? ?????????????????????????????????????????

• Amazon Kendra is a managed enterprise search service that can index content stored in S3 and support natural-language queries to find text inside documents (PDFs, Word files, etc.).

• Rekognition analyzes images/videos, not document text search.
• Polly is a text-to-speech service.
• Lex is a chatbot service.

• Create a Kendra index.
• Add a data source of type S3 and point it to your bucket (with an IAM role that allows access).
• Configure/document conversion if needed, then index the data.
• Use the Kendra API or console to run natural-language queries against your indexed documents.

• Incidents & alerts — see how the phishing activity was detected, correlated, and investigated.
• Threat Explorer — search for the malicious messages, recipients, URLs, attachments, and campaign activity.
• Email and collaboration reports — assess how many users or messages were affected.
• Remediation details — verify whether messages were quarantined, removed, or otherwise contained.

• Microsoft 365 admin center — general tenant administration.
• Microsoft Entra admin center — identity and access management.
• Exchange admin center — mail-flow and Exchange configuration, rather than full phishing-incident investigation.

• You want: a row for each year and each product (year+product), plus a summary row for each year that totals all products (year only).
• GROUP BY ROLLUP(YEAR(SO.ModifiedDate), P.Name) yields:

• GROUP BY CUBE(YEAR(SO.ModifiedDate), P.Name) produces all combinations of the grouping columns, including:

• This adds extra rows (per-year totals by product across all years and a grand total) that aren’t needed for your stated goal.

• If you want exactly the (Year, Product) and (Year) rows, you can use GROUPING SETS ((Year, Product), (Year)) which is equivalent to Rollup in this two-column case but makes intent explicit.

• SELECT YEAR(SO.ModifiedDate) AS OrderYear, P.Name AS ProductName, SUM(SO.OrderQty) AS TotalQty

• Azure Resource Manager templates: Use ARM templates to define the VM infrastructure and baseline configuration as code. This ensures all Windows Server 2016 VMs are created with a uniform setup, minimizing manual configuration.

• PowerShell Desired State Configuration (DSC) extension for Windows: DSC enforces a defined state across all servers and can automatically remediate drift. This keeps the VMs in the desired configuration with minimal ongoing admin effort.

• Basic: Self-help only; no technical support or design assessment.
• Developer/Standard: Technical support, but generally not the full proactive architecture guidance required here.
• Professional Direct: Includes proactive guidance and architecture recommendations.
• Premier: Also supports this, but is not the minimum-cost option.

• 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.

• Key difference: DirectQuery leaves data in the on-prem SQL Server and queries it live. That means every visual request can trigger online processing on the source, which can be slower and adds load on the on-prem system. In contrast, Import loads data into the Power BI data model, so visuals render from the in-memory engine (faster).

• Why that helps with the requirements:

• Why not A or B: Both rely on live data via DirectQuery, which contradicts the goal to minimize online processing and visuals performance.

• Why not D: It uses Import, but refreshing hourly adds unnecessary processing and complexity when daily refresh already satisfies currency needs.

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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