PEOPLECERT ITIL4-DPI Exam Prep
ITIL 4 Strategist: Direct, Plan and Improve (Page 2 )

• An Azure pay-as-you-go subscription follows an operational expenditure (OpEx) model.
• With OpEx, the organization pays for services as they are used, typically based on consumption.
• There is no need to purchase and maintain physical servers upfront.
• This contrasts with capital expenditure (CapEx), where the company makes a significant upfront investment in infrastructure, such as on-premises Hyper-V hosts.

• 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.

• B. Data loss prevention (DLP): Helps prevent sensitive data from being copied, emailed, or exfiltrated. It is not the primary control for protecting a stolen laptop’s stored data.
• C. Operating system hardening: Reduces vulnerabilities by disabling unnecessary services and applying secure settings, but it does not necessarily protect the files if the disk is accessed directly.
• D. Boot security: Helps ensure the device starts only through trusted software or approved processes. It supports platform integrity but does not itself encrypt stored files.

• 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.

• 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.

• A — Custom Button: A button can launch a flow, typically by passing the flow’s URL and record context.
• D — From a Process: A Process can invoke a flow using an Apex action configured for flow invocation. However, this is automation-triggered, not directly accessed by a user.
• B — Quick Action: In current Salesforce functionality, a Flow Quick Action can launch a screen flow directly from a record page. This makes B a valid modern method.
• C — Visual Workflow Launcher: This is not generally listed as a standard access method in current Salesforce terminology; Salesforce commonly uses the Flow component on Lightning pages instead.

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• Vertical scaling (scale up) means increasing the size of an existing VM—for example, adding CPU or RAM.
• Vertical scaling down means reducing the VM’s size when less capacity is needed.
• Horizontal scaling (scale out) means adding more VM instances rather than making one VM larger.
• Elasticity is the broader ability to automatically adjust resources based on demand; it does not specifically mean adding CPU or memory to one VM.
• Agility refers to the ability to provision or change resources quickly.

• 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

• Users logging in more frequently than average
• Logins occurring during non-business hours
• Patterns that may indicate compromised credentials or suspicious access

• Login History: Shows individual login attempts and basic details, but does not provide behavioral analysis or averages.
• Login Inspector: Used to troubleshoot or inspect a specific login attempt, rather than analyze organization-wide patterns.
• Login Report: May provide reportable login information, but it is not the specialized tool for forensic behavioral analysis.

• 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.

• 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

• 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.

• Batch processing, such as scheduled data transformations
• Streaming processing, such as continuously ingesting events

• Manipulating data with multiple languages: Mainly a benefit of Databricks and Spark.
• Real-time notebook collaboration: A workspace/notebook feature.
• Alerts for query failures: A Databricks SQL or job-monitoring feature.
• Distributing complex operations: Primarily a Spark compute capability.

• Hardware and infrastructure
• System software
• Networks and communications
• Technology platforms and services

• Business Architecture: Defines business strategy, organization, functions, and processes.
• Data Architecture: Defines the structure and management of enterprise data.
• Application Architecture: Describes application systems and how they interact to support business and data processing.

• 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.

• Customer contains one row per customer and the customer’s Address ID.
• Address contains one row per address and the corresponding location details.

1. Select the Customer query.
2. Choose Merge Queries.
3. Match Customer[Address ID] with Address[Address ID].
4. Use a Left outer join so every customer is retained.
5. Expand the merged Address column and select City, State/Region, and Country.

• Append adds rows vertically and is used for tables with the same structure.
• Group By summarizes rows rather than adding related columns.
• Transpose switches rows and columns and does not establish a relationship.

1. The sender hashes the original message and sends the message plus its hash.
2. The receiver applies the same hashing algorithm to the received message.
3. The receiver compares the newly calculated hash with the sender’s hash.
4. Matching values indicate that the message was not altered in transit.

• A binary image is not the normal description of a hash result.
• Using a different algorithm produces a different type of digest, so the values cannot be directly compared.
• Hashing verifies integrity, but by itself does not prove who sent the message. Digital signatures or MACs address authentication and integrity together.

• 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.

• 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.

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• A — Reports: Reports are informational and don’t enforce access during an SSO transaction.
• B — Apex trigger and Permission Sets: A trigger could update permissions when a Case changes, but this creates synchronization and timing issues. It also changes Salesforce permissions rather than making a direct, real-time decision for the external system.
• C — Custom SAML JIT Provisioning: JIT provisioning is primarily for creating or updating users at login. It isn’t the appropriate mechanism for dynamically querying business records and deciding whether access should be granted.
• D — Connected App Handler: Designed for custom, programmatic control during connected-app authentication and authorization. Apex can check the user’s current open classified cases and permit or reject the SSO request.

• 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.

• SQL injection (SQLi): Attacks databases by inserting malicious SQL commands.
• Cross-site scripting (XSS): Injects scripts into web pages viewed by other users.
• Sideloading: Installing an application from outside the official app store. It may install unauthorized apps, but it does not generally unlock the operating system or enable restricted system features.

• 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.

• 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.

• 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.

1. Start with the Customer table.
2. Use Merge Queries and select the Address table.
3. Match Customer[Address ID] to Address[Address ID].
4. Use a Left Outer join so every customer is retained.
5. Expand only City, State/Region, and Country.

• Append adds rows vertically; it is for stacking similarly structured tables.
• Group By summarizes rows and does not retrieve address columns.
• Transpose swaps rows and columns and is unrelated to table relationships.

1. Download the updated VPN client configuration package from the Azure VPN gateway.
2. Remove or update the existing VPN client configuration on the Windows 10 workstation.
3. Install the new package and reconnect.

• B: Moving the task may affect the critical path and should not be done before evaluating the planned response and impacts.
• C: Excluding a required task is not appropriate without formal analysis and stakeholder approval.
• D: Lessons learned and the risk log should be updated, but only after or alongside managing the immediate issue; updating them is not the first action.

• Residual neural network (ResNet): Primarily used for computer vision tasks such as image classification.
• Support vector machine (SVM): Commonly used for classification and regression, not natural-language query generation.
• WaveNet: Designed mainly for generating or modeling audio and speech.

• Snapshot: Captures a point-in-time state for rollback. It is not an independent running VM and does not protect against failure of the underlying host or storage.
• Convert to dynamic disks: Changes disk allocation behavior; it does not provide redundancy or high availability.
• P2V: Converts a physical machine into a virtual machine. That is a migration process, not an availability solution.

• Application management: Controls which apps can be installed or used; it does not remove existing data after theft.
• Full-disk encryption: Protects data when the device is powered off or locked, but it may not help if the attacker has already unlocked the device.
• Containerization: Separates corporate data from personal data, but the attacker may still access the corporate container using the stolen credentials.

• Authenticating websites with TLS/HTTPS
• Enabling certificate-based VPN authentication
• Signing or encrypting email
• Supporting device or user authentication

• VLAN tagging: Uses network-frame tags, such as 802.1Q; it does not rely on X.509 certificates.
• LDAP: May integrate with PKI in some environments, but X.509 certificates are not most commonly associated with LDAP itself.
• MFA: Certificates can be one authentication factor, but X.509 is fundamentally a PKI technology.

• Low usage during the first three weeks
• Very high usage during the last week

• High availability: Keeps services running despite failures.
• High latency: Means slow response times; it is not a benefit.
• Load balancing: Distributes traffic across resources but does not specifically address scaling resources up and down.

• An Anypoint VPC provides private network connectivity for CloudHub applications.
• An IPsec VPN tunnel or AWS Direct Connect connects that VPC to the customer’s on-premises network without exposing the database to the public internet.
• The VPC and on-premises firewalls must allow the required traffic, routes, and ports.

• Static IP addresses only identify the application’s outbound traffic; they do not create a private network path to the database.
• A Dedicated Load Balancer does not replace the required network connection to the on-premises environment. It is also unnecessary merely to reach individual databases.
• Moving sensitive databases to a DMZ is not the intended architecture and could reduce their protection.

• tag=Priv searches the tag field for values beginning with Priv. The wildcard can match the remaining characters, so it matches Privileged.
• tag=Priv would search for the exact tag Priv, not Privileged.
• tag=priv* uses lowercase priv; tag matching is treated as case-sensitive here, so it may not match the tag Privileged.
• tag=privileged uses the wrong capitalization for the exact tag.

• netstat: Shows local network connections and listening ports, but not which WAN path carries the traffic.
• nslookup: Resolves domain names to IP addresses.
• ping: Tests reachability and latency but does not show the route.

Correct choices

• DB1: Azure Data Box Heavy
• Virtual machines: Azure Migrate

Why

• Replication with minimal downtime
• A test migration
• Final cutover after validation

1. Modify the database subnet NACL inbound rules to allow traffic from the new web subnet’s CIDR block on the MySQL port (3306).
2. Modify the database subnet NACL outbound rules to allow return traffic to the new web subnet’s CIDR block on the required ephemeral port range.

• 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.

• 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.

• Data lakes: scalable, low-cost storage for structured, semi-structured, and unstructured data.
• Data warehouses: reliable SQL analytics, governance, and performance features.

• Data lake: Primarily provides flexible, scalable storage but may lack warehouse-style reliability and governance.
• Data warehouse: Strong for structured analytics but less flexible for raw and unstructured data.
• All of these: The individual lake and warehouse do not, by themselves, unify the siloed architecture.

• 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.

• Microsoft 365 groups can contain users, but generally can’t contain other groups as members.
• Security groups can contain users and, where supported, other security groups.
• Distribution groups have different nesting restrictions and aren’t equivalent to security groups.
• Dynamic groups can’t be manually populated with members; their membership is calculated from rules.
• A group’s membership type (Assigned or Dynamic) and group type determine whether it can be added to another group.

• 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.

• Filtering or reshaping columns
• Joining with an analysis-specific dataset
• Creating calculated fields
• Applying business logic for a particular project

• Data blending: Combining different data sources, not specifically the final processing step.
• Data enhancement: A broad term for enriching data.
• Data testing: Checking data quality or pipeline correctness.
• Last-mile ETL: The precise term for additional transformations performed after the main pipeline, often directly before analytics or reporting.

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