Banking CRCM Exam Actual Questions
CERTIFIED REGULATORY COMPLIANCE MANAGER (CRCM) (Page 41 )

• Answer: No.

• Why: Microsoft Managed Desktop (MD) is a managed service for end-user desktops (Windows devices used by people), not a service for provisioning, managing, or lifecycle-managing large fleets of virtual machines. It won’t help automate weekly creation and deletion of many VMs, especially with mixed OS (Windows and Linux).

• Relevant concept: To reduce administrative effort for rapidly provisioning and tearing down VMs, an Azure service like Azure DevTest Labs is appropriate. DevTest Labs lets you:

• Takeaway: The proposed solution (MD) does not meet the goal; the correct approach is to use a lab-oriented service such as Azure DevTest Labs for frequent, ephemeral VM deployments.

• 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:

• Event | search "error"
• Event | where EventType == "error"
• search in (Event) "error"

• 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.

• What it’s asking: In an ARM template, for an Availability Set, what value should you set for the property platformFaultDomainCount to maximize availability during fabric failures or maintenance?

• Key concept: Fault domains are groups of hardware that can fail independently. By spreading VMs across more fault domains, you reduce the risk that a single hardware fault takes out many VMs.

• Important detail: For managed Availability Sets, the number of fault domains you can use varies by region — typically 2 or 3.

• What to choose: Set platformFaultDomainCount to the maximum supported in your region. In most regions this is 3, which gives you the best resilience. If your region only supports 2, you’d use 2.

• Answer: 3 (Max Value). In ARM template syntax, this would appear as "platformFaultDomainCount": 3 (within the Availability Set resource).

• 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.

• 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.

• Why: A multi-modal embedding model maps both text and images into a shared vector space, enabling cross-modal similarity and retrieval (e.g., find images matching a text query or text matching an image).

• What it does: Enables efficient similarity search using a vector database or nearest-neighbor indexing.

• Why the other options aren’t suitable:

• Quick implementation note: you’d typically pair the embeddings with a vector store (e.g., ANN index) and possibly fine-tune on domain data for better alignment across modalities.

• What wipe does: A factory reset that erases all data, apps, and settings and returns the device to the OOBE. It removes the device from management and resets identity.
• Why not for this scenario: The goal is to repurpose the same device to a new user with Autopilot. Autopilot Reset keeps the device’s Azure AD/Intune identity and re-provisions the device for the next user without a full wipe.
• What Autopilot Reset does: Removes user data and apps but preserves device identity and management connection, then reapplies the Autopilot provisioning for the next user.
• When you’d use wipe instead: If you’re selling or permanently discarding the device, or you truly want to scrub all traces and start fresh without re-enrolling.

• Answer: Data enhancement.

• Why: Augmenting gold-layer tables with an additional dataset is enriching or enhancing the existing data to support the ad-hoc analysis. The other options don’t capture the idea of adding external data to improve the dataset (ad-hoc improvements = tweaks, last-mile dashboarding = building dashboards, last-mile ETL = ETL steps at the end of the pipeline). In practice, this is often also called data enrichment.

• The correct option is A: Data architecture.

• Why: In the AWS CAF, the Platform perspective covers the technical foundation and architecture that enable cloud workloads to run reliably and efficiently. This includes how data is modeled, stored, and accessed across the enterprise—the core of data architecture.

• Quick mapping for the other options:

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

• Program and project management
• Risk management
• Cloud financial management
• Data governance
• Portfolio management

• Sustainability
• Performance efficiency
• Reliability

Why

cmd 
PowerShell.exe -ExecutionPolicy Bypass -File C:\Scripts\ConfigureVM.ps1

Why the other options are less suitable

• GPO logon script: Runs only when a user logs on and may not run during initial VM provisioning.
• GPO startup script: Can work, but requires the VM to be joined to the domain and the policy to be applied. It is also normally evaluated at each startup.
• Place scripts in a VHD: Merely placing scripts on a disk does not cause them to execute.

• Step 1: From Power BI Desktop, select Get Data, choose Folder, enter the folder path, and click OK, then choose Transform data to open Power Query Editor.

• Step 2: In Power Query Editor, expand the Attributes column.

• Step 3: From the Power Query Editor, select the Content column and use Home > Combine Files (aka “Combine binaries”) to consolidate the files.

• 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.

• Answer: D. Select an IdP

• Why: To reduce the number of credentials across multiple SaaS apps, implement a single sign-on approach via an Identity Provider. The first step is choosing the IdP; once selected, you can enable SAML (A) or OAuth as supported by apps. Password vaulting (C) is not a true SSO solution, and B is not the foundational step.

• D. Provide public endpoints to store and retrieve data

• E. Manage the infrastructure layer and the operating system

• Classifying data
• Configuring access permissions, such as IAM policies
• Selecting or configuring available encryption options, depending on the requirement

• Improve QA testing efficiency: A prototype may help discover defects early, but that is not its greatest benefit.
• Decrease time allocated for user testing and review: Prototyping usually requires additional user involvement and review, not less.
• Minimize scope changes: Prototypes can actually reveal new requirements or cause scope changes. However, discovering those changes early is beneficial.

• 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.

• 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

• Correct choices: A and D.

• Why not B, C, or E?

• What the goal is: You want to add guest (external) users to your Azure AD tenant contoso.com, one entry per external user in a CSV.

• The proposed solution uses New-AzureADUser for each external user. Why this isn’t correct: New-AzureADUser creates a regular (internal) user in your directory. It does not create a guest/B2B user invite. So you’d end up with 500 ordinary accounts, not guest accounts with external access.

• The correct approach: Use New-AzureADMSInvitation for each external user. This invites the external user to your directory and creates a guest user (a B2B collaboration user) in your tenant. The invited user then accepts the invitation and gains access as a guest.

• Azure AD vs directory/tenant: Azure AD is the cloud identity service. A directory (also called a tenant) is your specific instance of that service (contoso.com in your scenario). The terms are often used interchangeably in exams.
• Guest user vs internal user: A guest user is external to your organization; an internal user is created directly in your directory as a normal employee/user account.
• B2B invitation: The process of inviting external users so they can collaborate in your resources; New-AzureADMSInvitation is designed for this.

• Answer: C (192.168.0.1-192.168.0.99, 192.168.0.101-192.168.0.254)

• Why: The interface is 192.168.0.100/24, so DHCP pools must be within the 192.168.0.0/24 subnet and cannot include the router’s IP (192.168.0.100), the network address (192.168.0.0), or the broadcast (192.168.0.255). Option C provides two ranges that exclude those addresses.

• Why the other options are invalid:

• Correct answer: D (Special category data)

• Why:

• Quick notes:

• Increase/decrease the number of epochs: Changes how long training runs, but does not reuse a pre-trained model.
• Unsupervised learning: Trains from unlabeled data and does not specifically address adapting an existing model.

• Drop violating frames
• Log or alert on the violation
• Shut down or restrict the port

• Load balancer: Distributes application traffic; it does not control switch MAC learning.
• IPS: May detect some network attacks but is not the primary control for switch MAC-table exhaustion.
• NGFW: Protects traffic crossing network boundaries, while MAC flooding occurs at the switching/LAN layer.

• En Cisco SD-Access, el fabric overlay se extiende hasta lo inalámbrico; los APs son nodos de borde del fabric y su tráfico queda dentro del overlay.
• El cliente inalámbrico genera tráfico que viaja por el overlay, pero no es un componente del fabric en sí.
• El WLC es principalmente una herramienta de gestión y control, no forma parte del overlay; el underlay es el transporte físico.

• 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.

• Correct answer: A. Inbound data transfer from the internet

• Why: AWS does not charge for data transferred into AWS from the internet. Inbound data transfer is free, regardless of the amount (in this case, 2 TB). The other options involve data leaving AWS or moving between AWS regions/AZs, which incur costs:

• Post-Incident Review (A) is the after-action step used to capture lessons learned, identify process gaps, and drive improvements across the incident response lifecycle. This directly supports process maturity and a stronger security posture.
• Incident Analysis (D) focuses on investigating root causes during/after an incident, which is important but more of a diagnostic activity than a standalone baseline improvement to maturity.
• Fast Eradication (B) and Incident Containment (C) are tactical actions aimed at stopping the incident, not on maturing the process.

• IaaS: You deploy and manage virtual machines, operating systems, disks, and networking. Azure manages the physical datacenter and host infrastructure.
• PaaS: Azure manages the operating system and runtime so you can focus on deploying applications, such as with Azure App Service or Azure Functions.
• SaaS: You consume a complete, provider-managed application.

• Correct answer: C — The model is deployed at UK border checkpoints.

• The EU AI Act applies to AI systems used within the EU or by entities established in the EU. So:

• 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.

• 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.

• Pay-as-you-go in Azure is an operating expense (OPEX) model, not an “elastic” expenditure model.
• Elastic or scalable terms describe capacity planning, not the cost category. The cost should be treated as OPEX since you pay for usage each month.
• The parallel question (Question 12) shows the correct approach is using an operational expenditure model.

• Answer: D) Shadow IT

• Why: Shadow IT is when employees use unsanctioned tools or bypass IT controls (like the VPN) to access services, often without IT oversight. It’s an internal behavior, not an external attacker’s activity.

• Not these:

• Quick note: This creates risk due to lack of visibility and control. Mitigations include a defined approved-tools list, enforcing VPN usage, device management, and continuous monitoring.

• 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.

• Co to znaczy: transakcje zachowuja sie tak, jakby byly wykonywane jeden po drugim. Dzieki temu nie ma brudnych odczytów, niepowtarzalnych odczytów ani phantom reads.

• Implementacja: PostgreSQL uzywa SSI (Serializable Snapshot Isolation). W praktyce oznacza to, ze jesli wykryje konflikt miedzy transakcjami, jedna z nich zostanie zakonczona bledem “serialization failure” i trzeba ja ponowic.

• Zalózmy tabele orders(customer_id int, amount int).

• Transakcja T1:

• Transakcja T2, równoczesnie:

• T1 ponownie wykonuje to zapytanie:

• Correct answer: enable SLAAC on an interface (C)

• Why the other options are not correct:

• Options:

• Answer: The correct choice is A (800-61r3).

• NIST SP 800-61 Rev. 3 is the dedicated guide for incident handling/response and reflects the latest guidance on the incident response lifecycle (preparation, detection/analysis, containment, eradication, recovery, post-incident activity).
• 800-61r2 is an older revision of the same incident response guide.
• 800-53 covers security and privacy controls, not a specific incident response standard.
• 800-171 focuses on protecting CUI in non-federal systems and networks, not incident response guidance.

• 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:

• B: Intégrez des résultats de due diligence tels que les évaluations de risque client.

• C: Utilisez des correspondants en aval pour enquêter et signaler les activités suspectes.

• CAPWAP (Control And Provisioning of Wireless Access Points) creates a tunnel between the AP and the Wireless LAN Controller over the IP network. The AP and WLC communicate via this CAPWAP tunnel, which can traverse Layer 2 or Layer 3 networks.
• The AP does not have to be on the same switch as the WLC. It can connect to any switch in the network, as long as there is IP reachability between the AP and the WLC. This is why option D is correct.
• The other options are incorrect:

• 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.

• A — conformant: Incorrect because one explicit requirement is violated.
• B — consistent: “Consistent” is not sufficient where the implementation contradicts a mandatory specification. The RPC mechanism is covered by the architecture, and the implementation differs from it.
• C — consistent: Incorrect reasoning: the RPC mechanism does have a defined architectural requirement, so conformance must be assessed.
• D — non-conformant: Correct because the implementation fails to follow the specified RPC/threading requirement.

• As the project progresses, you gain information, refine requirements, and lock down baselines (scope, schedule, cost). This reduces overall risk and uncertainty because more is known and addressed.
• At the same time, changes become more costly over time. Later changes can affect already approved baselines, cause rework, impact the critical path, require more coordination, and incur penalties or delays, all of which raise the cost of making changes.

• Context: You’re configuring a Windows Autopilot deployment profile (Profile1) for Windows 11 devices. The hotspot asks you to pick the correct behavior for two statements based on the profile’s settings.

• Box 1: “can modify the desktop settings only for themselves”

• Box 2: “Cortana settings” with options like computer name, keyboard layout, etc.

• Why: Table.Profile returns a per-column profile that includes statistics such as count, null count, distinct count, and standard deviation. It also provides min/max and average where applicable, which matches the requested metrics (Count, Average, Null count, Distinct count, Standard deviation).
• Syntax: Table.Profile(table as table, optional additionalAggregates as nullable list) as table
• How it helps: After running Table.Profile on your table, you can extract the per-column stats you need and display them in your semantic model or report.

• 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.