Scrum PSK I Exam Prep
Professional Scrum with Kanban (Page 4 )

• Correct answer: C) After installing ES on the search head(s) and running the distributed configuration management tool.

• ES requires the Splunk_TA_ForIndexes.spl add-on on indexers to apply index-time configurations. It should be deployed via the distributed configuration management (DCM) flow after ES is installed on the search heads, so the indexers receive the proper configuration in a controlled, versioned bundle.

• A) Deploying when adding apps to the deployment server isn’t the ES-specific order; it bypasses the ES-instantiated workflow.
• B) Installing Splunk_TA_ForIndexes.spl first isn’t the recommended order; ES needs to be installed on the search heads first.
• D) It isn’t limited to indexer cluster sites or cluster master commands; the standard flow uses DCM to push the TA to indexers in general.

• Correct answer: D – Do not enable default administrative accounts to ensure accountability.

• A – Do not use encryption methods … backups and sensitive information: Encryption helps protect data; not using it increases risk, not reduces insider threats.
• B – Do not allow administrators to use unique accounts during installation: We actually want unique admin accounts, not the opposite.
• C – Do not control the access to administrators and privileged users: Failing to control access directly enables misuse.

• The app on EC2 connects to Aurora using DB credentials stored on the instance. Secrets Manager centralizes these credentials and can rotate them automatically, eliminating manual updates on each EC2 host.
• Automatic rotation reduces operational overhead and enhances security because credentials are rotated on a schedule without code changes.
• The app can retrieve the current secret at runtime (e.g., via the AWS SDK) and use it to authenticate to Aurora, avoiding reliance on locally stored files.

• B (Parameter Store) can store secrets but lacks seamless built-in rotation for database credentials like Secrets Manager; rotation often requires more custom work.
• C (S3) would require complex tooling to rotate and securely fetch credentials and still risks exposure if access policies aren’t perfect.
• D (Encrypted EBS volumes) would store credentials locally on each instance and does not automate rotation or centralized management.

• Who performed the action
• What action occurred
• When it occurred
• Where it originated
• Whether it succeeded or failed

• Investigating suspicious activity: Important, but investigation depends on having adequate audit data first.
• Storage allocation: Necessary for retention, but storage does not help if the records lack meaningful details.
• Actions after audit failure: Part of operational planning, not the primary assessment of the application’s audit capability.

• 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.

• 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.

• 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.

• Numerical flexibility: Adjusting the number of employees, such as using temporary, part-time or contract staff.
• Temporal flexibility: Varying when employees work, such as through flexitime, overtime or different working hours.
• Shift system flexibility: Changing work patterns or shift arrangements; it is not specifically about moving between tasks.

• nomes de pessoas;
• organizações;
• locais;
• datas;
• números de processos;
• leis ou contratos.

• NER = identifica informações específicas e classificáveis.
• Sumarização = reduz um documento e apresenta seus pontos principais.

1. Get Data ? Folder in Power BI Desktop.
2. In Power Query Editor, expand the Attributes column to expose metadata such as hidden status and other file attributes.
3. Remove the Content column (and any other data columns you do not need).

• Name
• Extension
• Date accessed
• Date modified
• Folder Path
• Attributes
• Content

• 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.

• The mapping and resolution of endpoint identities (EIDs) to their location (RLOCs) in an SD-Access fabric is handled by the control plane using the LISP protocol.
• Why not the others:

• A single Construction Job can use multiple Machinery records.
• A single Machinery record may be used on multiple Construction Jobs.
• This is a many-to-many relationship, which Salesforce models using a junction object.
• The junction object can contain fields such as:

• Master-detail to Machinery
• Master-detail to Construction Job
• Date and cost fields

• Enable the owner dimension
• Allocate a standard costing inventory model

• Owner dimension (B): This dimension Lets you track who owns the stock. By enabling the owner dimension, items can be configured as either company owned or consignment (vendor-owned) when you create or edit items. Without enabling this dimension, you can’t designate ownership per item.
• Standard costing inventory model (D): Selecting a costing model determines how item costs are calculated and posted. Standard costing provides a fixed cost per unit, which is commonly used for agreed pricing scenarios like consignment arrangements for easier accounting and valuation. It helps ensure consistent cost treatment across transactions for items that can be owned by the company or on consignment.

• Batch dimension (A): Batch tracking is unrelated to ownership and isn’t required to support company-owned vs. consignment configurations.
• Moving average costing (C): This is an alternative costing method. It’s not required to enable ownership config, and the exam’s expected choice focuses on standard costing for this scenario.

• 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.

• 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:

• 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.

• A — SCIM synchronizes changes to user attributes from the identity provider to GitHub.
• B — SCIM deactivates GitHub accounts when users are removed or deactivated in the identity provider.
• D — SCIM automates user provisioning when new users are added to the identity provider.

• C: SCIM does not delete organization repositories.
• E: SCIM does not generate REST API authentication tokens.
• F: SCIM handles identity lifecycle management, not repository permission configuration based on roles.

• A. Implement retries with exponential backoff

• C. Reduce the frequency and/or size of requests

• B. Use PutRecord instead of PutRecords — PutRecord sends one record per call and does not remove the underlying throughput limitation. It may increase request overhead.
• D. Use Amazon SNS instead — SNS is not a direct fix for Kinesis shard throttling and changes the required streaming architecture.
• E. Reduce KCL consumers — KCL consumers read from Kinesis; they do not solve producer-side PutRecords throttling.

• A. A virtual network gateway
• D. A virtual network

• Azure virtual machines must be connected to an Azure virtual network (VNet). The VNet provides private IP addressing and network connectivity for the VMs.
• A virtual network gateway enables communication between the Azure VNet and the on-premises network, typically through a site-to-site VPN or ExpressRoute connection.

• Load balancer: Distributes traffic among Azure VMs; it does not connect Azure to an on-premises network.
• Application gateway: Provides web-application load balancing and Layer 7 routing; it is not the normal resource for on-premises connectivity.
• Gateway subnet: A gateway subnet is required inside the VNet when deploying a virtual network gateway, but the question’s intended two primary resources are the VNet and virtual network gateway.

• 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.

• 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.

• 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.

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

• R-squared (R²): mede quanto da variação dos valores reais é explicada pelo modelo. É mais usado em regressão, não em classificação.

• Accuracy (acurácia): mede a proporção de previsões corretas em relação ao total. É adequada para a classificação de imagens da questão.

• RMSE (Root Mean Squared Error): mede o tamanho médio dos erros entre valores previstos e reais, penalizando erros maiores. É usado principalmente em regressão.

• Learning rate (taxa de aprendizado): controla o tamanho dos ajustes feitos pelo modelo durante o treinamento. Não é uma métrica de avaliação; é um hiperparâmetro.

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

• Basic: Self-help only; no technical support or design assessment.
• Standard: Technical support, but not the full proactive architectural guidance required here.
• Professional Direct: Provides proactive guidance and architecture-related assistance.
• Premier: Also supports this, but is a more expensive, enterprise-level option.

• Assign users or their team an appropriate security role.
• Ensure the role has the required table privileges, such as read, create, write, and append.
• Share the app with that security role in the app designer.

• Dynamics administration center: Used for environment and service administration, not routine app access configuration.
• Manage Roles: Not the specific Dataverse area used to define app and data permissions.
• Security Roles: Define what users can access and which operations they can perform.

• 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.

• Answer: Internal load balancers (ILB) — option C.

• An ILB has a frontend IP address that must be a private IP within the subnet’s address space. You pick this IP when you create the ILB, and it can be Static or Dynamic.
• Other options don’t require a per-subnet IP:

• The stage has a default file format defined (e.g., when you create the stage with FILE_FORMAT = (...)), and Snowflake will use that default.

• You use a named file format attached to the stage, or you specify an inline format in the COPY command, e.g., COPY INTO my_table FROM @my_stage FILE_FORMAT = (TYPE = 'CSV');

• Use a stage default: omit FILE_FORMAT in COPY.
• Or rely on a named/inline format: specify FILE_FORMAT in COPY or set a format on the stage.

• Correct answer: All four statements are true (A, B, C, and D). The given key (A,B) is likely incorrect.

• A) Customers can change the size of the Warehouse after creation — Yes. You can resize a virtual warehouse after it’s created using ALTER WAREHOUSE ... SET WAREHOUSE_SIZE.
• B) A Warehouse can be resized while running — Yes. Snowflake supports online resizing; you can change the size without stopping queries.
• C) A Warehouse can be configured to suspend after a period of inactivity — Yes. You can enable auto-suspend with a delay (AUTO_SUSPEND).
• D) A Warehouse can be configured to auto-resume when new queries are submitted — Yes. You can enable auto-resume (AUTO_RESUME) so a suspended warehouse automatically resumes on new queries.

• Example commands:

• Even if a warehouse is resizing or auto-suspending, queries will be queued and then executed once the warehouse has the appropriate capacity.

• 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:

• 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.

• This is a lookup relationship. Salesforce does not cascade access from a child (Opportunity) to its parent (Account) via implicit sharing.
• To view the Account, the user must have explicit access to the Account (via its OWD/sharing, or object permissions).

• The user has Read/View (or higher) on Account via profile/permission set.
• Manual sharing or a sharing rule grants that Account to the user.
• The Account is exposed by a broader permission like View All/Modify All on Account.
• Role hierarchy with Grant Access Using Hierarchies could grant access to Accounts owned by others, if applicable.

1. Ingestion Lambda receives requests from API Gateway and places messages in an SQS queue.
2. Processing Lambda is triggered by the queue and loads records into Aurora PostgreSQL.

• Decoupling: API request handling is separated from database loading.
• Buffering: SQS absorbs bursts when incoming traffic exceeds the database’s processing capacity.
• Controlled concurrency: Configure the SQS-triggered Lambda’s concurrency and batch size to avoid overwhelming Aurora or exhausting database connections.
• Automatic retries: Messages that fail processing can return to the queue and be retried. A dead-letter queue can capture persistent failures.
• Lower operational effort: The solution remains serverless and avoids managing EC2 servers or Kubernetes clusters.

• A: EC2 and Tomcat add server-management overhead.
• B: DynamoDB/DAX would require changing the database model and does not solve the Aurora ingestion design.
• C: SNS is useful for notifications and fanout, but SQS is better for durable buffering and backpressure in this producer-consumer workflow.

• Windows 10 VMs
• Windows Server 2012 or later VMs
• Running VMs—they do not need to be shut down
• Debian 8.2+ VMs, assuming the specific version is supported
• Stopped or deallocated VMs

kusto 
Event | where EventType == "error"

• == is the KQL equality operator.
• -eq is a PowerShell operator, not KQL.
• is is not the correct equality operator in this context.

text 
Event | where EventType –eq "error"

• Event | search "error" — valid, broad search.
• search in (Event) "error" — valid, broad search.
• Event | where EventType == "error" — valid, targeted filter.

• wyjasnic istniejace pytania z tej strony,
• wskazac, dlaczego dana odpowiedz jest poprawna lub bledna (z uwzglednieniem celów egzaminu),
• omówic koncepcje zwiazane z Google Cloud AI.

• Answer: D

• The strongest security comes from enforcing access at the storage layer. An IAM policy scoped to the Cognito identity prefix restricts each user to their own S3 prefix (folder), so users can only access their own files regardless of client or server code.
• This per-user isolation reduces risk of cross-user data access and is more robust than UI validation or Lambda/API validation alone.
• Implementation note: attach a policy to the Cognito identity role that allows actions on arn:aws:s3:::your-bucket/${cognito-identity.amazonaws.com:sub}/* (per-user prefix). Ensure TLS in transit and enable at-rest encryption (SSE-S3 or SSE-KMS). Use multipart upload for large files up to 300 MB.

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.

• Premium file shares: contoso104 only
• Blob access tiers: contoso101, contoso102, and contoso103

• Premium Azure Files shares require a storage account with the FileStorage account kind. This is a specialized account type and is not used for blob storage.
• Blob access tiers such as Hot, Cool, and—where supported—Archive apply to blob-capable accounts, such as General-purpose v2 or BlobStorage accounts shown in the exhibit.
• contoso104 is therefore excluded from the blob-tier selection because it is the premium Files account.

• Why: The data is unlabeled, so you can’t train a model with known input-output pairs. Unsupervised learning discovers structure in the data by grouping similar customers into clusters or tiers without labels.
• What it does: Clustering and segmentation (e.g., k-means, hierarchical clustering, DBSCAN, Gaussian mixtures) to form meaningful groups.
• Why the other options aren’t as suitable:

• On-Demand: You pay by the hour with no long-term commitment. This is ideal for workloads that are infrequent or have highly variable usage. Since this job runs only 24 hours once per year, you pay only for those 24 hours, with no wasted upfront or long-term payments.
• Reserved Instances: Require a 1- or 3-year commitment and pay for capacity whether you use it or not. For a workload that runs just 24 hours a year, you’d be paying for most of the term unused, making it far less cost-efficient.
• Spot Instances: Can be interrupted whenever AWS needs capacity, which is unacceptable for an uninterruptible workload.
• Dedicated Instances: Generally more expensive and aimed at licensing or compliance requirements; not needed for this use case.

• A) MTU mismatch: OSPF neighbors must exchange Hello packets with a compatible MTU. If MTUs differ, the neighbor conversation fails and the adjacency cannot become Full.

• B) Incorrect subnet mask: Routers must be in the same IP subnet on the connected link to form a neighbor relationship. A subnet mismatch prevents Hello packets from being considered valid on the same network.

• D) Area ID not the same: Neighbors must share the same Area ID. A mismatch stops adjacency from forming beyond the 2-Way state.

• E) Different metric set on each end of the link: Cost differences do not prevent OSPF adjacency; they affect routing decisions, not neighbor formation.
• F) Router ID not defined: OSPF can auto-choose a router ID; an undefined ID is not a prerequisite for adjacency to reach Full.

• Honeypots: Decoy systems or services designed to attract attackers. Legitimate users should not access them, so activity on a honeypot is a strong indicator of unauthorized activity. Security teams can also observe attacker behavior.

• DNS sinkhole: Redirects requests for known malicious domains to a controlled internal server or blocks them. If an infected workstation attempts to contact command-and-control infrastructure, the sinkhole records the request and helps identify the compromised device.

• Tokenization: Protects sensitive data by replacing it with tokens.
• CI/CD: Automates software development and deployment.
• Threat modeling: Identifies potential risks before or during system design.
• Data obfuscation: Makes data harder to understand, but does not detect intruders.

• Correct answer: S3 Versioning

• Why the other options aren’t suitable for this protection:

• Quick tip: For enhanced protection, enable Versioning (and consider related features like Object Lock for write-once scenarios in specific compliance cases).

• Enable the owner dimension

• Assign a standard costing inventory model

• Batch dimension tracks batches or lots, such as expiration or production batches. It does not identify inventory ownership.
• Moving average is a costing method, but it is not the required choice for this consignment configuration.