CIMA BA3 Exam Prep
Fundamentals of Financial Accounting (Page 11 )

• The inference container image or built-in algorithm
• The location of model artifacts in Amazon S3
• IAM permissions through the execution role
• Optional environment variables and networking settings

1. Create AWS::SageMaker::Model
2. Create an AWS::SageMaker::EndpointConfig that references the model
3. Create AWS::SageMaker::Endpoint that references the endpoint configuration

• AWS::SageMaker::Endpoint hosts a model but does not define the model itself.
• AWS::SageMaker::NotebookInstance provides a development environment.
• AWS::SageMaker::Pipeline orchestrates ML workflow steps.

• 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.

• Creating and managing users and roles
• Granting and revoking roles and privileges
• Managing access-control responsibilities

• SYSADMIN: Manages warehouses, databases, schemas, and other objects—not primarily users and roles.
• PUBLIC: Automatically granted to every user; it is not an administrative role.
• ACCOUNTADMIN: Has the highest level of access, but should be reserved for account-level administration and emergencies rather than routine security tasks.

• Guiding principles
• Governance
• Service value chain
• Practices
• Continual improvement

• Guiding principles: Recommendations that guide decisions and actions; they do not describe the entire system.
• Four dimensions: Perspectives used to ensure a balanced approach to service management.
• Service relationship: Describes cooperation between a service provider and service consumer, rather than the whole value-creation system.

• ARP poisoning: Redirects or intercepts local network traffic; it would more likely affect communication paths or cause man-in-the-middle behavior.
• Jamming: Interferes with wireless signals. The scenario does not indicate a wireless outage.
• Kerberoasting: Attempts to obtain service-account password hashes from Kerberos; it is a credential attack, not typically a direct service outage.

• Matching hashes: The software is unchanged.
• Different hashes: The software was modified or corrupted.

• Encryption: Protects confidentiality.
• Baselines: Define approved configurations or normal performance.
• Tokenization: Replaces sensitive data with tokens.

python 
def add_integers(a, b): 
    return a + b

• Uses def to define a function.
• Accepts two parameters, a and b.
• Uses return to send their sum back to the caller.
• Uses indentation to define the function body.

python 
result = add_integers(3, 4) 
result is 7

• The VM keeps its configuration, disks, IP settings, and associated resources.
• The operation may cause downtime.
• You cannot guarantee that it will move specifically to Host2; Azure selects the new host.
• Update management is for update configuration, not host relocation.
• Moving to another subscription does not move the VM to a different physical host.
• Usage location is unrelated to VM placement.

• Exploitability
• Attack complexity
• Required privileges
• User interaction
• Impact on confidentiality, integrity, and availability

• CVE: Identifies and tracks a specific vulnerability; it does not primarily measure severity.
• OSINT: Open-source intelligence gathered from publicly available sources.
• SOAR: Security orchestration, automation, and response technology used to automate security workflows.

• 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.

kusto 
Event 
| summarize make_set(EventID) by Computer

• summarize groups records.
• by Computer creates one result row per virtual machine.
• make_set(EventID) returns an array containing unique event IDs for that computer.

kusto 
| summarize make_list(EventID) by Computer

• A private IP address on the virtual network
• An associated public IP address

• Review iteration length: Changing duration does not resolve unclear requirements.
• Limit work in progress: Useful when excessive multitasking is the problem, but it does not directly clarify requirements.
• Review individual performance: The issue is a process and backlog-quality problem, not necessarily an individual performance problem.

• Cutover: The old system is replaced immediately. It may be fast, but any failure can cause significant disruption.
• Phased: The system is introduced in stages, which reduces implementation risk but may prolong the transition.
• Pilot: The system is first deployed to a limited group or location. This is useful for testing but does not necessarily minimize downtime for the entire organization.

• B: Trigger.Once + scheduled job

• C: 10-minute processing-time trigger

• 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.

• Uses on-premises data or resources
• Runs components in the cloud
• Can scale across multiple cloud environments when demand increases

• Public cloud: Resources are hosted entirely by a cloud provider.
• Private cloud: Resources are dedicated to one organization, usually in its own environment.
• Hybrid: Combines on-premises and cloud resources.

• 800 devices ? a scalable, centralized solution is needed.
• Four hardware/vendor classes ? the tool must handle different drivers and images.
• Two user groups ? it should allow different configurations for each group.
• “Distribute custom images” ? an imaging/deployment tool is required, not just a file-copy utility.

• Customers access the portal without needing a full Dynamics 365 license.
• They submit case information through portal forms.
• The submitted data is stored in Dataverse tables, such as the Case table.
• Security is controlled using web roles and table permissions, so customers can access only appropriate records.

• Dynamics 365 Connector — connects apps to Dynamics 365 data but is not the portal’s underlying data platform.
• Microsoft SharePoint — useful for documents and collaboration, not the standard case-management data source for portals.
• Microsoft Azure Storage — stores files or other data, but is not the native portal business-data platform.

• Manage multiple organizations from one hierarchy.
• Enterprise-wide policies, including repository visibility, forking, and GitHub Copilot policies.
• Centralized identity and access management, such as SAML SSO, SCIM provisioning, and—where applicable—Enterprise Managed Users.
• Centralized billing and licensing, including organization and Copilot seat management.
• Enterprise audit log, which aggregates activity across organizations.
• Security and compliance oversight, such as organization security visibility and enterprise-wide controls.
• Enterprise support and account management features, depending on the plan.

• Organization: manages its own repositories, teams, members, and local policies.
• Enterprise: governs multiple organizations and can enforce policies across them.

• Self-signed certificate: Causes a certificate warning, not incorrect IP resolution.
• Nameserver record: A DNS record problem would typically affect multiple users, not just one.
• IP helper: Relays DHCP or other broadcast traffic between networks; it does not control website name resolution.

Question 3: AWS Storage Gateway File Gateway

• A: S3 bucket per user — S3 is object storage, not a traditional shared file system; mounting it requires extra tools and is less efficient.
• B: File Gateway — Provides a local file share using standard file protocols while storing files in Amazon S3. This preserves convenient local access and extends storage capacity.
• C: WorkSpaces and WorkDocs — Designed for virtual desktops and document collaboration, not large shared file storage.
• D: EC2 with EBS — EBS is attached to one EC2 instance and is not designed for direct multi-user file sharing.

• A separate scope using a /32 subnet: A /32 identifies one host, but it is not a practical DHCP scope for this purpose.
• A static IP within the DHCP range: This can cause an IP address conflict if DHCP assigns that address to another device.
• SLAAC: Used for automatic IPv6 address configuration, not for assigning a predictable IPv4 address through DHCP.

• Leadership and stakeholder commitment
• Business capability and skills
• Organizational culture
• Availability of resources
• Ability to manage and sustain change
• Impact of the proposed transformation

• How ready the organization is
• Which areas require urgent attention
• How difficult each issue may be to resolve
• Risks that could affect implementation

• A: Places the assessment in Phase B and refers vaguely to creating views; this is not the prescribed technique.
• B: An Implementation Factor Assessment and Deduction Matrix is not the primary TOGAF technique described for business transformation readiness.
• D: A Business Scenario helps identify requirements and stakeholder concerns, but it is not the main technique for assessing organizational readiness.

• The policy is confidential, and the requester is no longer authorized merely because they used to work for the organization.
• The security professional should not disclose it by email or display it on a device without authorization.
• Using official channels allows the organization’s owner or information custodian to verify:

• Developed and maintained independently
• Deployed without changing the entire application
• Scaled separately based on demand
• More resilient when another service has problems

• A: Functional testing is useful, but it is not the main practice described.
• B: Automation helps deployment, but the scenario focuses on architectural decomposition.
• C: Multiple locations improve availability, not application modularity.

• Mesh: WAPs communicate wirelessly with one another, often to extend coverage without wired connections. It does not specifically describe centralized WLC management.
• Point-to-point: Connects two specific devices or locations.
• Ad hoc: Wireless devices communicate directly with one another without an access point.

• Writing with Apache Spark, typically as Delta tables.
• Reading with Apache Spark for processing and analysis.
• Reading with T-SQL through the lakehouse’s automatically generated SQL analytics endpoint.
• Reading with KQL through Fabric’s query capabilities that can access lakehouse data.

• Eventhouse: Optimized for real-time and KQL workloads, but it is not the natural destination for Spark-based writes or general lakehouse storage.
• Datamart: Intended mainly for relational analytics and Power BI; it is not designed for Spark-based data ingestion.
• Warehouse: Primarily T-SQL-based and structured. It does not provide the same native Spark write experience or flexibility for semi-structured data.

• You provision the virtual machine.
• You manage the guest operating system, applications, and configuration.
• Azure manages the physical servers, datacenter, and underlying virtualization infrastructure.

• If the Facebook user is new, Salesforce invokes createUser.
• If the user already exists, Salesforce can invoke updateUser.
• The handler can read the latest data from Facebook, such as name, email, or other profile attributes, and update the corresponding Salesforce user.

• B: A scheduled job is unnecessary and would not provide real-time synchronization during authentication.
• C: A Facebook signed request is not the standard mechanism for updating Salesforce user records in this authentication-provider setup.
• D: SAML Just-in-Time provisioning applies to SAML identity providers, not Facebook OAuth authentication.

• Updating the CRL: Revokes compromised certificates, but does not fix the CA flaw that allowed them to be created.
• Changing passwords: May be necessary if accounts were compromised, but it does not address the certificate-generation vulnerability.
• Implementing SOAR: Improves security automation and response; it does not remediate the underlying PKI weakness.

• Customize Opportunity Contact Role

• Add the Opportunity Contact Role related list to the Opportunity page layout

• Campaign Member Role relates contacts or leads to campaigns, not opportunities.
• Campaign Member related list would expose campaign participation, not the stakeholders for the opportunity.
• Customize Campaign Role also concerns campaigns.

• B. Users accessing Salesforce from public Wi-Fi

• C. Users choosing the same password as their Facebook password

• A: 2FA does not protect an already-authenticated, unlocked session if someone accesses an unattended laptop.
• D: Weak password-reset questions are an account-recovery weakness. 2FA primarily strengthens login authentication, not necessarily the security-question reset process.

• 16 VMs: one failure loses 25 GB and 10 cores—only 6.25% of memory.
• 8 VMs: one failure loses 12.5% of memory.
• 4 VMs: one failure loses 25%.
• 1 VM: one failure loses the entire application environment.

• Users retain their existing usernames and passwords.
• Existing accounts can be used to access Azure-based resources.
• Administrators avoid manually recreating thousands of accounts.
• The transition is less disruptive before the on-premises data center is retired.

• Input feature distributions differ from training data ? Analyze dataset drift metrics

• Model accuracy drops without code changes ? Analyze dataset drift metrics

• Endpoint latency increases under load ? Investigate scaling and infrastructure metrics

• 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.

• Modelo de embeddings multimodal: transforma texto e imagens em vetores no mesmo espaço semântico. Isso permite comparar uma consulta textual com imagens, ou uma imagem com textos relacionados.
• Modelo generativo multimodal: interpreta diferentes modalidades e gera conteúdo, mas não é a escolha específica para indexação e busca vetorial.
• Modelo de geração de imagens: cria imagens, portanto é inadequado.
• Modelo de embeddings de texto: não atende consultas com imagens.

• A is correct: Days beyond the included period incur an extra daily charge.
• D is incorrect: Daily use after 10 days is not free.
• B and C: Data-transfer pricing can depend on the direction and service terms. The exam’s intended distinction is that the included device usage period is the clearly stated no-cost benefit.

• User1 can sign in because the account is cloud-only. It does not depend on the on-premises domain.
• User3 can sign in because the account uses Password Hash Synchronization (PHS). Azure AD has a synchronized password hash and can validate the sign-in even when connectivity to on-premises Active Directory is unavailable.
• User2 cannot sign in because the account relies on Pass-through Authentication (PTA). PTA sends the authentication request to an on-premises PTA agent, which must contact a domain controller. With the internet connection from on-premises lost, that validation cannot occur.

• The requirement explicitly says the organization wants to use SAML to secure Salesforce API access.
• The SAML Bearer Assertion Flow exchanges a SAML assertion for a Salesforce OAuth access token.
• It is intended for server-to-server or other noninteractive integrations where the application has already been authenticated by an identity provider.
• The connected app can be configured so administrators authorize or preauthorize applications that consume the APIs.

• B — JWT Bearer Flow: Uses a signed JWT, not a SAML assertion. It would be appropriate if the requirement specified JWT-based authentication.
• C — SAML Assertion Flow: This is not the standard Salesforce OAuth flow name; the relevant name is OAuth 2.0 SAML Bearer Assertion Flow.
• D — User-Agent Flow: Designed for interactive browser-based authorization, not a SAML-based server-to-server integration.

• Pull: Downloads changes from the central repository and updates the local repository.
• Push: Sends local commits to the central repository.
• Commit: Records local changes in the repository’s history.
• Merge: Combines changes from different branches.
• Clone: Creates a new local copy of a repository.

• 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.

• A Layer 2 switch checks the destination MAC address in its MAC address table (CAM table).
• If the destination MAC is unknown, the switch cannot select a specific outgoing port.
• It therefore performs unknown-unicast flooding, sending the frame out every other port in that VLAN.
• The frame is not sent back through the port where it arrived.

sql 
SELECT COUNT(*) FROM table;

• A: It does not normally need to scan all data files for this operation.
• B: Results are not simply reused from a query cache every time. Caching is separate from Delta’s metadata optimization.
• D: The relevant metadata is maintained through the Delta transaction log, not directly treated as a Parquet-file metadata operation in this context.

• You normally do not create a new sensitive information type for credit card numbers. Microsoft Purview already provides a built-in Credit Card Number sensitive information type.
• The required sequence should generally be:

• A. Tuning system software: This is a corrective action, not an analysis method. Tuning should be based on measured performance data.
• B. User dissatisfaction reports: Useful as an indicator, but subjective and insufficient for diagnosing the cause.
• D. Off-peak utilization and response time: This may hide problems occurring during peak periods. Performance analysis should cover representative or peak workloads.