SAP C_MDG_90 Exam Prep
SAP Certified Application Associate - SAP Master Data Governance (Page 19 )

• 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.

• A. Number ? Decimal — Correct. Salesforce Number fields are represented in Apex as Decimal.
• D. Date/Time ? Datetime — This is correct conceptually, but “Dateline” is not an Apex type. If the option literally says “Dateline,” it is incorrect.
• E. Checkbox ? Boolean — Correct. A checkbox contains true or false.

• Number ? Integer — A Number field maps to Decimal, even when its scale is zero.
• Text Area ? List<String> — A text area maps to a single String, not a list.

• The listener has a virtual IP address on the load balancer.
• The active SQL Server replica must respond using the listener’s IP address, not its own VM IP.
• Floating IP allows the listener IP to be preserved and returned by the active replica.
• This is required for the SQL Always On listener configuration.

• A health probe for the SQL listener/cluster role
• The SQL Server listener port, commonly 1433
• Appropriate backend pool membership

• 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.

• Correct answer: A) Hashing

• Why: Hashing produces a fixed-size digest of the software. If the final build is altered in any way, the hash will change, enabling you to detect tampering by comparing the stored digest with a freshly computed one.

• Why not the others:

• Business
• People
• Governance
• Platform
• Security
• Operations

• Cloud financial management
• Portfolio and program management
• Risk management
• Data governance
• Compliance and oversight

• A: Using an external OAuth 2.0 identity provider could provide unified identity, but it does not specifically address Salesforce capacity or high-volume login limits.
• B: Making the Experience Cloud community a SAML identity provider and using JIT provisioning is a possible federation pattern, but it may not be appropriate for this scale without Salesforce validation.
• C: A default account is unrelated to solving identity unification and high login throughput. Person Accounts can be supported in appropriate B2C Commerce integrations.

1. Create a custom Sensitive Information Type (SIT) in the Microsoft Purview portal.
2. Use a regular expression to match the 32-character alphanumeric account number.
3. Reference that SIT in the DLP policy protecting SharePoint Online.

• Format-based data ? custom SIT with RegEx
• Built-in sensitive data ? Microsoft-provided SIT
• Document classification/protection ? sensitivity labels
• Security investigation queries ? Advanced Hunting, not DLP detection

• vSmart controls the overlay control plane. It uses OMP to exchange routing information, distribute policies, and determine how WAN Edge devices reach one another.
• vManage is the centralized management and configuration platform.
• vBond is the orchestrator. It helps devices authenticate, discover controllers, and traverse NAT during onboarding.
• APIC-EM is not the SD-WAN control-plane component described here.

Why B works

• AWS KMS keys are Regional resources. A normal customer managed key in Region A cannot be used directly in Region B.
• A customer managed multi-Region KMS key (MRK) has related key replicas in both Regions. The replicas share key material and key ID, although each has a Regional ARN.
• With client-side encryption, the application encrypts the data before storing it in S3. Cross-Region replication then copies already-encrypted objects, so S3 does not need to decrypt and re-encrypt them with a different Regional key.

Why the other choices are weaker

• A and C: SSE-S3 uses Amazon-managed S3 keys, not the required customer managed KMS key.
• D: SSE-KMS can work only if the solution creates and configures the appropriate KMS key or multi-Region key replica in each Region. As written, it says to create a customer managed KMS key in each Region, which implies separate keys—not the same KMS key. Replication also requires additional KMS permissions and destination encryption configuration.

• Forecasting lets sales leaders predict revenue for upcoming fiscal periods, compare forecasts with targets, and review performance by salesperson or team.
• Opportunity Stages help determine the likelihood that opportunities will close. Each stage can have a probability, which contributes to forecast calculations.

• Sales Quotes: Used to present pricing or product proposals to customers; they do not provide revenue forecasting.
• Opportunity List View: Helps users filter and view opportunities, but it is not the forecasting tool.

• What the dataflow options mean:

• Why C fits best:

• Accept: Acknowledge the risk and choose not to take additional action.
• Transfer: Shift the risk or its financial impact to another party, such as through insurance or outsourcing.
• Mitigate: Reduce the likelihood or impact of the risk, such as by implementing security controls.
• Avoid: Eliminate the activity or condition that creates the risk.

• Digitally signing the software: Verifies authenticity and integrity. It does not hide how the software works.
• Limiting third-party libraries: May reduce dependencies and attack surface, but does not directly prevent reverse engineering.
• Using compile flags: Some compiler options can remove debugging information or optimize code, which may provide limited protection, but this is not the primary technique identified for preventing reverse engineering.

• Amazon S3 — Object storage, not a database.
• Amazon DynamoDB — Fully managed NoSQL key-value and document database.
• Amazon Redshift — Managed data warehouse designed for analytics, not primarily transactional MySQL workloads.

• The correct setting to configure is the Enrollment Status Page (ESP) profile.
• Why: ESP tracks the enrollment progress and logs during Autopilot provisioning. You can enable log collection within the ESP policy so that, if ESP times out, the user can collect and share the logs with support.
• Optional enhancement: In the ESP policy (or the Autopilot diagnostics settings), you can enable:

• Automatic enrollment settings: control how devices auto-enroll, not log collection.
• Windows Autopilot deployment profile: controls deployment steps but not ESP log collection.
• Device configuration profile: applies settings but doesn’t provide ESP log collection mechanics.

• MAC flooding: Overwhelms a switch’s MAC address table, potentially causing traffic to be broadcast.
• DNS spoofing: Provides false DNS responses to redirect users.
• ARP poisoning: Sends false ARP information to associate an attacker’s MAC address with another device’s IP address.

• BIOS language support: Useful for usability, but not whether the PC can operate electrically.
• Supply chain security: Concerns procurement and hardware trust, not deployment across countries.
• Power efficiency: Important for energy usage, but it does not ensure compatibility with local power systems.

• IaaS: You manage the virtual machine’s operating system, configuration, applications, and data. Azure manages the physical infrastructure and virtualization platform.
• PaaS: You deploy applications without managing the underlying operating system or servers.
• SaaS: You consume a complete application managed by the provider, such as Microsoft 365.

• Reusing features across different models and teams
• Consistent feature definitions
• Managing feature versions and metadata
• Using features for both training and inference

• SageMaker Data Wrangler: Prepares and transforms data; it is not primarily a shared feature repository.
• SageMaker Clarify: Helps detect bias and explain model predictions.
• SageMaker Model Cards: Documents models, including intended use, risks, and evaluation results.

• AWS Budgets lets you create a cost or usage budget and configure alerts when actual or forecasted spending reaches a threshold. Notifications can be sent through email or Amazon SNS.
• Amazon CloudWatch can monitor AWS billing metrics and trigger an alarm when estimated charges exceed a specified amount.

• Amazon SQS is a message queue, not a cost-monitoring service.
• Cost Explorer analyzes spending but does not directly provide threshold notifications.
• AWS Cost and Usage Report delivers detailed billing data for analysis; it does not itself send threshold alerts.

• Primary and core nodes: Use On-Demand Instances because the workload is business-critical. These nodes manage the cluster and store/process HDFS data.
• Task nodes: Use Spot Instances in an instance fleet. Task nodes are suitable for interruption because they do not hold essential cluster data and can be replaced or diversified across instance types.
• Terminate the entire cluster after processing: The job runs only about six hours each morning, and EMRFS stores data in Amazon S3. Keeping the cluster running for the rest of the day would waste money.

• A: Using Spot for primary and core nodes risks interruption of the critical cluster.
• C: All On-Demand is reliable but unnecessarily expensive; terminating the cluster helps, but Spot task nodes provide greater savings.
• D: Leaving primary and core nodes running after the task finishes continues to incur costs. Savings Plans also are less appropriate for this intermittent workload.

• Aggregation happens once, during the scheduled job.
• Dashboard users query a relatively small, precomputed table.
• Repeated users do not trigger expensive aggregations against the detailed sales table.
• Overwriting is appropriate because the summary is rebuilt for the reporting period.

• B: Structured Streaming is unnecessary for data that only needs daily refreshes and adds operational/compute overhead.
• C: A view recomputes the aggregation at query time, increasing latency and query cost.
• D: Delta caching may improve reads but does not replace the need to materialize the expensive aggregation, and cache availability is not the main design guarantee.

• Equipment and hardware
• Facilities
• Inventory
• Backup media
• Supplies stored at an alternate or remote location

• Personal belongings of staff: Usually not organizational assets.
• DR line-item revenues: Revenue is a financial impact, not a physical asset.
• Cloud-based applications: These are primarily logical or information-processing assets, even though they depend on physical infrastructure.

• The legacy system creates a signed JWT assertion.
• Salesforce validates the assertion using a certificate configured on a Connected App.
• Salesforce issues an access token for the integration user.
• No refresh token is required; the system can request a new access token when needed.

• Web Server Flow: normally uses a client secret and authorization/refresh tokens.
• Username-Password Flow: requires storing Salesforce credentials and is discouraged for integrations where stronger options are available.
• User Agent Flow: intended for user-interactive applications, not unattended server-to-server processing.

• Microsoft Teams: Connects to Teams-related data, but it isn’t the primary connector for the Power Apps data tables.
• Personal Analytics: Provides personal productivity insights, not application data.
• SQL Server database: Only applies if the app explicitly uses SQL Server as its data source.
• Dataflows: Used for preparing and centralizing data, not as the normal direct connection to a Power Apps app.

• Amazon GuardDuty: Detects suspicious activity and threats using security-related data sources; it is not primarily a software vulnerability scanner.
• Amazon Detective: Helps investigate and analyze the root cause of security findings after an event.
• Amazon Cognito: Provides user authentication and authorization for applications.

• Server — provides services or resources
• Endpoint — a user device connected to the network
• Ethernet — a networking technology, not a filtering device
• Firewall — filters and controls traffic

• Idioma: “Responda em português.”
• Tamanho: “Responda em até três frases.”
• Formato: “Use uma lista com três recomendações.”
• Objetivo: “Recomende produtos com base nas necessidades do cliente.”

• Mudar o tamanho do LLM: pode alterar capacidade e custo, mas não garante idioma ou respostas curtas.
• Aumentar a temperatura: aumenta a criatividade e a aleatoriedade; não controla diretamente o tamanho ou idioma.
• Aumentar o Top K: permite considerar mais tokens prováveis, aumentando a variedade; também não é o mecanismo principal para controlar idioma ou comprimento.

• Identity provider (IdP): Authenticates users and asserts their identity.
• Service provider (SP): The application being accessed—in this case, Salesforce/the employee portal.
• Identity store: A directory that stores user identities, such as Active Directory.
• Authentication store: A system used to validate credentials.

• Two VMs
• Distributed across two separate availability zones

• Generates a syslog message
• Shuts down the PoE port
• Places the port into error-disabled (err-disabled) state
• Frees the power allocation

• A device starting to draw power does not necessarily generate a syslog message.
• Monitoring does not pause data traffic while checking power.
• Drawing less than the configured minimum does not automatically mean the device has failed and should be disconnected.

• SOW
• SLA
• MOA
• MOU

• A. IDS attack signatures: An IDS primarily detects suspicious network activity and usually does not provide dedicated file-upload malware scanning. It is generally detective rather than preventive.
• C. Load balancer: Distributes traffic among servers and improves availability; it does not inspect documents for malware.
• D. WAF: Helps block web-application attacks such as SQL injection, command injection, and XSS. Although a WAF may inspect upload requests, malware detection in PDF files is more directly addressed by antivirus or content-scanning functionality.

• A: Daily involvement is unnecessary and may not solve the availability problem.
• C: Clarifying all requirements at the start conflicts with agile’s iterative refinement.
• D: Simply reviewing based on availability leaves the problem unresolved and allows rework to continue.

• 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.

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. 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.

powershell 
Get-AzureVM -ServiceName <service-name> -Name <vm-name> | 
  Set-AzureStaticVNetIP -IPAddress <private-ip> | 
  Update-AzureVM

• New-AzureRMVMConfig configures VM settings but does not itself assign the static IP.
• Set-AzureSubnet is not the appropriate cmdlet for assigning a VM’s private IP.
• The Azure portal option mentioned in the question was not the intended method for this older deployment model.
• Changing settings in Windows Network and Sharing Center is incorrect because Azure assigns the IP through the VM’s virtual network interface; manually configuring it inside the guest can cause conflicts.

• 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.

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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