PCI CPSA Exam Actual Questions
Card Production Security Assessor (CPSA) Qualification Exam (Page 5 )

• In the source query: apply UPPER() or LOWER() to the column.
• In Power Query Editor: use Text.Upper or Text.Lower on the column, then load.

• This fixes the root cause when comparisons/joins depend on case.
• In DirectQuery, transformations are pushed to the source or query layer; ensure the transformation is supported by your data source/connector.
• After normalization, verify relationships and any downstream visuals that rely on that column.

• 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.

• 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.

• 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.

• Shadow IT describes employees using unsanctioned tools or methods to access systems, often without IT oversight (e.g., bypassing the VPN).
• Not A (Espionage): this implies external covert actors; here the activity is internal.
• Not B (Data exfiltration): there’s no explicit data transfer shown, just bypassing the VPN.
• Not C (Nation-state attack): this involves external political/strategic actors; not the internal behavior described.

• Implement policy and training to enforce approved tools.
• Use IAM, VPN enforcement, and network access controls to ensure only sanctioned access.
• Monitor for unsanctioned tools and provide secure, approved alternatives.

• 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.

• Domeny tworza restrykcjony razem z typem. Wykorzystujesz je w kolumnach, aby miec spójna walidacje danych w wielu miejscach.
• To nie jest pelny niestandardowy typ z wlasnym input/output funkcjonowaniem. Aby stworzyc typ z wlasnym I/O i operacjami, uzylbys CREATE TYPE (lub rozszerzen w C). Domeny to uproszczona forma ograniczen na istniejacy typ.

• CREATE DOMAIN positive_int AS integer NOT NULL CHECK (VALUE > 0);
• CREATE TABLE orders (id serial, qty positive_int);

• latwo wymuszaja wspólne reguly dla wielu kolumn/naglówków,
• zmiana regul w jednym miejscu (domena) automatycznie wplywa na wszystkie kolumny ja uzywajace.

• 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.

• Why: After listing all resources, you need to assign who is responsible for each task. A RACI chart (Responsible, Accountable, Consulted, Informed) documents these roles for every activity.

• What RACI means:

• Why the others aren’t suitable here:

• Quick example: Task "Create user authentication module" — Responsible: Developer, Accountable: Tech Lead, Consulted: Security Architect, Informed: PM.

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

• Answer: D) Out-of-band management

• Why this is correct:

• Why the other options aren’t suitable here:

• Correct answer: D — AWS Migration Hub

• AWS Migration Hub provides a single location to track the progress of application migrations. It offers a centralized dashboard to view migration status across multiple tools and projects.

• AWS Application Discovery Service helps inventory and map on-prem applications and their dependencies, not a single progress tracker.
• AWS Application Migration Service (formerly SMS) performs the actual lift-and-shift migrations, but isn’t the centralized tracker itself.
• AWS Service Catalog manages catalogs of IT services for self-service deployment, not migration progress.

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• Multi-pass wipe: Overwrites the drive’s existing data multiple times, making recovery much more difficult while preserving the drive’s functionality.
• Single-pass wipe: May be sufficient in some modern sanitization standards, but is presented here as less secure than multiple overwrites.
• Degaussing: Uses a strong magnetic field to disrupt data. It can render the drive unusable or unreliable, reducing its resale value.
• Pinning: Is not an appropriate data-sanitization method.

• 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.

• 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.

• The correct answer is Mass Transfer Tool.

• The Mass Transfer Records tool is Salesforce’s built-in way to reassign ownership of existing records in bulk. In this scenario, you need to transfer ownership of the departed rep’s accounts and opportunities to a new rep while keeping the team structure intact.

• Go to Setup > Data Management > Mass Transfer Records.
• Choose the object: Accounts (and then Opportunities, if needed).
• Set “Transfer From” (the leaving rep) and “Transfer To” (the new rep).
• Decide whether to transfer related records as well (e.g., opportunities tied to accounts) and proceed.
• Review and confirm.

• Data Loader / Data Import Wizard are for bulk import/export, not bulk reassigning ownership.
• Dataloader.io is a third-party tool; the built-in Mass Transfer tool is the standard Salesforce method for this task.

• 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.

• 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:

• Use a field on Account to show the aggregated yearly shipments (a Roll-Up Summary).
• A Master-Detail relationship between Shipment and Account is required for Roll-Up Summary to work (and to ensure access is governed by the parent Account).
• OWD and the relationship type determine what users can see. If you want to restrict shipment-level access while still showing a summary on Account, Private OWD on Shipment plus Master-Detail helps, because access to shipments is controlled by the Account record.

• A (Public OWD on Shipment with Process Builder and a Lookup to Account): Not ideal. Public access would expose shipments broadly, and a lookup cannot support a built-in roll-up summary.
• B (Private OWD on Shipment, Roll-Up Summary, Master-Detail to Account): Best answer. Private shipments, but because of Master-Detail, access and the roll-up are tied to the Account. The Account can show the yearly total via a Roll-Up Summary field.
• C (Controlled by Parent on Shipment, Trigger, Master-Detail): Master-Detail is correct, but using a trigger is unnecessary for a straightforward roll-up; roll-ups are the standard, maintainable solution.
• D (Private OWD on Shipment, Trigger, Lookup to Account): More complex and not as efficient; no automatic roll-up, and lookup breaks the ease of summarizing by Account.

• 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: C – netstat -an

• netstat -an lists all active connections and listening ports with numeric addresses, without resolving names. This lets you spot DoS patterns such as a flood of connections to a specific port or many connections from a single or few IPs.
• The other options don’t help as directly:

• Windows: netstat -an and look for numerous ESTABLISHED connections to a service or many SYNs to a port.
• Filter by port, e.g., netstat -an | findstr :80, or on Linux: ss -tunap | grep ':80'.

• Netstat provides a snapshot. DoS detection often benefits from continuous monitoring, flow data, and IDS/IPS logs.

• 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.

• App Tag: a label that reflects the app’s enterprise readiness based on its Cloud Confidence Index (CCI) rating. It lets you quickly group apps (e.g., Poor, Average, Good) for policy decisions.
• App Score: a numeric value representing the CCI score. It provides a precise threshold you can use to enforce policies (e.g., block apps with Score below 50).

• You can create policies that block or restrict apps with a Poor App Tag or with an App Score under a chosen threshold.
• These two aspects let you filter cloud apps by CCI ratings during policy evaluation.

• CCL isn’t described as a real-time policy facet in this scenario.
• GDPR Readiness is a separate consideration and not the two CCI aspects used for real-time protection policy in this context.

• Create and manage catalogs of approved IT services
• Govern and publish infrastructure-as-code (IaC) templates, commonly AWS CloudFormation templates
• Control which users can deploy specific products and configurations
• Apply versioning, permissions, and deployment constraints

• AWS Resource Explorer: Searches and discovers AWS resources; it does not govern IaC templates.
• AWS Organizations: Manages multiple AWS accounts, policies, and consolidated billing.
• AWS Systems Manager: Helps operate and manage deployed resources, such as patching and automation, but is not primarily a catalog for governed IaC products.

• 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.

• Global Unicast: These are the IPv6 addresses that are globally routable on the Internet. They’re allocated by Regional Internet Registries (RIRs) and can be reached from anywhere with proper routing (similar to IPv4 public addresses). Example ranges include 2000::/3.

• Link-local: fe80::/10. Used only on the local link; not routable beyond the directly connected network.

• Unique Local Addresses (ULA): fc00::/7. Private addresses for internal use within an organization; not publicly routable on the Internet (like IPv4 private addresses).

• Multicast: ff00::/8. One-to-many addresses used for group communication, not unicast routing.

• Strongest encryption algorithms: Choose the strongest available encryption and integrity options, typically AES with the largest key size and a secure integrity algorithm.
• Encrypt all traffic: Select tunnel mode rather than transport mode. Tunnel mode encrypts the entire original IP packet, which is appropriate for a site-to-site VPN between branch networks.
• Secret-password authentication: Configure a pre-shared key (PSK) on both VPN concentrators. The key must match at both ends.

• 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.

• 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.

• 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.

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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