Google PROFESSIONAL MACHINE LEARNING ENGINEER Exam Prep
Professional Machine Learning Engineer (Page 14 )

• A — Increased call deflection: o KCS disponibiliza conhecimento reutilizável para clientes e agentes, ajudando a resolver dúvidas sem abrir ou continuar chamadas.
• C — Reduced issue resolution time: os representantes reutilizam artigos e soluções já documentadas, acelerando o atendimento.
• E — Optimized use of resources: o conhecimento compartilhado reduz retrabalho e melhora o uso dos recursos da equipe.

• Namespace: The namespace associated with the code where the checkpoint was set.
• Time: When the checkpoint was captured or executed.

• Exception: Exceptions are viewed through debug logs or execution results, not as the primary Checkpoints tab information.
• Debug Statement: System.debug() output belongs in the Logs tab. A checkpoint captures program state, such as variables and their values, at a specific point in execution.

• Storage usage of files stored in Microsoft Teams: SharePoint site usage report

• Number of active users per team: Microsoft Teams usage report

1. SharePoint site usage report
2. Microsoft Teams usage report

• Two-part tariff pricing: $10 per unit
• Dual pricing: $20 per unit

Two-part tariff pricing

Dual pricing

• Division X, the seller, is credited with the market price: \(\$20\)
• Division Y, the buyer, is charged the marginal cost: \(\$10\)

• The requirement is an assessment of the Azure environment’s design, which requires a support plan offering proactive architectural guidance.
• Premier support would provide this capability, but it is a high-cost option.
• Since the requirement says to minimize expenses, Premier is not the best recommendation.
• A lower-cost plan with the required advisory/design-review capability—historically Professional Direct in Azure’s support-plan model—would be more appropriate.

• Economy: Obtaining inputs at the lowest appropriate cost.
• Efficiency: Making the best use of resources, such as staff, money, and equipment.
• Effectiveness: Achieving the intended objectives or outcomes.

• Injection
• Broken access control
• Authentication failures
• Security misconfiguration
• Cross-site scripting

• STIX: A standardized language and format for exchanging threat intelligence.
• OVAL: Used to describe and assess system vulnerabilities and configuration issues.
• Threat intelligence feed: Provides current indicators and intelligence about threats, but not necessarily a structured overview of common application exploitation methods.
• CVE: A catalog of individually identified, publicly disclosed vulnerabilities.

• Tracking who accessed or changed customer information
• Detecting unauthorized activity
• Supporting accountability and investigations
• Providing evidence for compliance and incident response

• Single sign-on not enabled: SSO improves convenience and centralized authentication, but it is not essential for access control.
• Complex passwords not required: This weakens authentication, but other controls may reduce the risk.
• Security baseline not consistently applied: A concern, but it is less specific and less immediately damaging than having no activity records at all.

• Crash cart: Provides local physical access, not remote access.
• Jump box: A secured intermediary used to reach devices over the normal network; it may be inaccessible if the appliance has not booted.
• Secure Shell (SSH): Requires the device’s operating system and network services to be running properly.

• Compute: Not used; no virtual warehouse is required.
• Data Storage: Not scanned again.
• Metadata: May help Snowflake manage and validate cached results, but it is not the Snowflake architectural layer identified as serving the request.
• Cloud Services: Handles query coordination, metadata services, authentication, and result-cache access.

• An Adj-SID does not necessarily need manual configuration.
• It can often be dynamically allocated by the router, although manual configuration may also be possible.
• The adjacency information is advertised through the IGP, such as IS-IS or OSPF.

• It is identified by an Adj-SID.
• It represents a particular adjacency of a node.
• Its meaning is generally local to the advertising node; unlike a Node-SID, it is not a globally reusable instruction across the network.

• Treatment one – easier to prepare: Convenience for the accounting team is not the main objective of financial reporting.
• Treatment two – fair representation: This is the correct choice because financial statements must faithfully represent the underlying transaction and its economic substance.
• Treatment three – maximises profit: Accounting policies must not be selected simply to manipulate or inflate reported profit.
• Treatment four – easier to understand: Understandability is important, but it cannot justify presenting information that is less faithfully represented.

• You can configure different pre-shared keys for different device groups.
• All cameras could use one key.
• All scanners could use another key.
• Each key can be associated with an appropriate role or access policy.

• Honeypots: Deliberately vulnerable or attractive systems designed to lure attackers. Legitimate users should not access them, so activity on a honeypot is a strong indicator of compromise.
• DNS sinkholes: Redirect requests for known malicious domains to a controlled internal system instead of allowing the connection. This can identify infected hosts attempting to contact command-and-control infrastructure.

• Tokenization: Replaces sensitive data with non-sensitive tokens; it protects data but does not primarily detect attackers.
• CI/CD: Automates software development and deployment processes.
• Threat modeling: Identifies potential threats and weaknesses before or during system design.
• Data obfuscation: Makes data harder to understand, but it is not an attacker-detection method.

• Internal hostnames and subdomains
• Network structure and naming conventions
• Servers and services
• Relationships between systems

• Determining ISP-assigned address space: Usually involves external registration and DNS information, not internal host discovery.
• Bypassing DNS sinkholing: Would require evidence that malicious DNS requests avoided redirection.
• Initial access: DNS queries alone do not show that the attacker gained access.
• Exfiltrating data: DNS tunneling can do this, but the snippet would typically show encoded or unusually long DNS queries.

• Install unauthorized or unsupported software
• Access features blocked by the manufacturer or operating system
• Customize system behavior beyond normal settings

Recommended process

1. Access each switch’s terminal.
2. Use help to identify available commands.
3. Use the MAC table command, such as:

text 
   show mac-address-table

1. Use VLAN information, such as:

text 
   show vlan

1. Use interface information, such as:

text 
   show ip interface brief

1. Record each device’s:

• Restrict a port to a defined number of MAC addresses
• Use statically assigned or “sticky” MAC addresses
• Shut down or restrict the port when violations occur

• Load balancer: Distributes application traffic; it does not protect switch MAC tables.
• IPS: May detect suspicious network activity, but is not the primary control for limiting switch MAC entries.
• NGFW: Filters traffic at the network boundary; it does not directly prevent MAC table flooding on access switches.

• Third-party attestation: Provides independent validation, but is typically periodic and performed by an external party.
• Penetration testing: Simulates attacks to identify exploitable vulnerabilities; it does not primarily track compliance.
• Vulnerability scans: Identify technical weaknesses, but do not provide a detailed review of procedures or compliance activities.

• User-generated content: precisa ser coletado, filtrado, anonimizado e rotulado.
• Moderation logs: podem conter dados inconsistentes, informações pessoais e decisões que precisam ser revisadas.
• Content moderation guidelines: são regras ou orientações, não dados diretamente utilizáveis para medir o comportamento do modelo.

• A hypervisor allows multiple virtual machines to run on one physical server.
• Each VM can have its own operating system, so the customer can consolidate Windows, Linux, and Unix servers.
• A Type 2 hypervisor runs on a host operating system and can be deployed on local hardware, helping address data privacy and avoiding ongoing public-cloud charges.

• Containers share the host operating system’s kernel.
• They are well suited to consolidating applications, but they generally cannot independently run a mix of unrelated kernels such as Windows, Linux, and Unix on one host.

• Design Studio > Emails ? create an email-template folder
• Design Studio > Landing Pages ? create a landing-page-template folder
• Add the relevant assets to each folder
• Right-click the folder and choose Share Folder
• Select the destination workspaces

• IaaS: você gerencia a VM e o sistema operacional.
• PaaS: o Azure gerencia a infraestrutura; você se concentra no aplicativo.

1. Create Workspace2 with Lakehouse2 — F
2. Migrate the Employee table to Lakehouse2 — E
3. Assign the data engineer the Contributor role for Workspace1 — D

Why

• Sharing Lakehouse1 would not provide the required isolation.
• Viewer access would not allow writing to Customer.
• Giving Contributor access to Workspace2 would expose the sensitive table.

• SQL injection
• Cross-site scripting (XSS)
• Malicious or malformed web requests
• Some application-layer abuse

• Layer 4 firewall: Filters based mainly on IP addresses, ports, and connections; it lacks detailed web-request inspection.
• NGFW: Provides broader network security, but a WAF is more specialized for protecting web applications.
• UTM: Combines multiple security functions, but it is not necessarily optimized for web-application protection.

• Create a CodeCommit repository in the secondary Region.
• Use CodeBuild to run a Git mirror operation from the primary repository to the secondary repository.
• Use EventBridge to detect relevant repository events, such as a pull request being merged.
• Invoke a Lambda function, which starts the CodeBuild project.

• B: Amazon S3 is object storage, not a Git repository that developers can use as a CodeCommit remote.
• C: CodeArtifact is intended for packages and artifacts, not source-code repository mirroring.
• D: An AWS Cloud9 environment is not an appropriate automated replication mechanism and would require manual or persistent infrastructure management.

apex 
Set<Id> accountIds = new Set<Id>{ accountId1, accountId2 }; 


List<Account> accounts = [ 
    SELECT Id, Name 
    FROM Account 
    WHERE Id IN :accountIds 
];

• A: LIMIT accepts a number, not a collection of IDs.
• B: Set.containsAll() checks set contents in Apex; it does not filter SOQL results.
• C: Database.query() accepts a dynamic SOQL string, not a Set<Id> directly.

• When the MVP will be defined — the MVP should already be understood sufficiently when assessing the business case.
• What the MVP is — useful information, but not the key requirement tested here.
• That the MVP is the same as project viability — incorrect. The MVP helps test viability; it is not identical to viability itself.

apex 
for (Account updatedAccount : Trigger.new) { 
    AccountTriggerHelper.notinyxternalSystem(updatedAccount.Id); 
}

• Too many callouts: The callout occurs asynchronously in the future method, and each future transaction can perform its permitted callouts.
• Too many SOQL queries: The SOQL query is inside the future execution, not repeatedly in the trigger transaction.
• Callout from triggers not supported: Direct callouts from triggers are prohibited, but the trigger delegates the callout to asynchronous Apex, which is the correct pattern.

• Asynchronous inference is designed for applications that submit individual inference requests, place them in a queue, and retrieve results later. It is useful when requests may take a long time or when traffic is intermittent.
• Batch transform is designed specifically to run inference over an entire large dataset stored in Amazon S3 and produce an output dataset.

• Large dataset/offline processing ? Batch transform
• Large or slow individual requests processed later ? Asynchronous inference

• Accept: Acknowledge the risk and take no additional action.
• Transfer: Shift the risk or financial impact to another party, such as through insurance or a contract.
• Mitigate: Reduce the likelihood or impact using controls, such as firewalls, training, or backups.
• Avoid: Eliminate the activity that creates the risk.

• Deploy five individual VMs: This does not create a VM scale set.
• Modify Availability Zones or Size settings: These actions do not create the required scale set and add unnecessary steps.
• VM orchestration mode: This is Flexible orchestration, intended for more individually managed VMs and mixed configurations. It is not the best choice for a straightforward group of identical instances.

• SAML (Security Assertion Markup Language): Commonly used for enterprise SSO between an identity provider and cloud applications. It sends signed authentication assertions to the service provider.
• RADIUS: Primarily used for centralized authentication to network devices, VPNs, and wireless networks.
• EAP: An authentication framework commonly used with network access technologies; it is not typically the SSO protocol for SaaS applications.
• OpenID: Related to identity authentication, but traditional enterprise SSO questions involving cloud applications generally expect SAML. OpenID Connect is a newer authentication layer built on OAuth 2.0.

• Accept terms of use
• Enter a temporary code or credentials
• Provide basic registration information

• Enterprise authentication — Authenticates organizational users, often with centralized identity services; it is not specifically designed for temporary guest access.
• Ad hoc network — A temporary device-to-device network, not an authentication process.
• WPA3 — A wireless security standard. It protects Wi-Fi access but does not provide the web-based temporary access workflow described here.

• Accept: Acknowledging the risk and choosing not to take additional action.
• Transfer: Shifting the risk or financial consequences to another party, such as an insurer.
• Mitigate: Reducing the likelihood or impact through controls, such as patching or access restrictions.
• Avoid: Eliminating the activity that creates the risk.

• Which host first communicated with the suspected malicious source
• Whether that host initiated suspicious traffic or merely received it
• Which other hosts show evidence of infection, such as connections from the originating host
• Which hosts have no related suspicious activity and are therefore clean

• An outbound connection to a suspicious external address
• A downloaded or executed malicious file
• Later lateral connections from that host to other internal systems
• Firewall logs showing the direction and timing of the traffic

• Regression predicts continuous numeric outcomes, such as house price, temperature, or sales amount.
• Linear regression assumes the target can be estimated from a linear relationship between the input features and the target.
• In Azure Machine Learning, you use a labeled dataset containing:

• Classification predicts a category or class, such as “spam” or “not spam.”
• Clustering groups similar records without a known label; it is an unsupervised learning technique.

• Orders contains order-level information, such as the order date.
• Order Line Items contains the products, quantities, and line amounts associated with each order.
• Merging them on the order key, such as OrderID, places the order date alongside each line item. This allows you to calculate total order value and analyze it over time.
• Product attributes can remain in the Products table and be related to the resulting line-item table through ProductID.

• Merging line items with products addresses product attributes but does not first provide the order-level date and value needed for time analysis.
• DAX calculated columns or lists add unnecessary model complexity.
• Calculating order counts is not the first requirement and does not combine the required order and line-item data.

• Data only needs to be current at 7 AM each day, so scheduled refresh is sufficient.
• Import mode provides the fastest report interaction because data is stored in Power BI’s in-memory engine.
• It supports row-level security (RLS), including the USERNAME() filter.
• You can limit the imported data to the current year and previous three years to reduce model size.
• DirectQuery would generally provide slower visual response times and is unnecessary here.
• Dual is mainly useful in composite models where some tables use DirectQuery and others use Import. That is not required by this scenario.
• Live connection is not appropriate because you are creating the semantic model.

• B — The glass core component is encased in a cladding.

• D — Light is transmitted through the core of the fiber.

• BNC connectors are associated mainly with coaxial copper cabling, not standard fiber connections.
• Data does not pass through the cladding; the cladding helps reflect and contain the light.
• RJ-45 connectors are used with copper Ethernet cabling, not fiber-optic interfaces.

• File hashes
• Strings and metadata
• Binary or code structure
• Known malicious patterns
• Embedded indicators or signatures

• Sandbox: Executes the file in an isolated environment; useful for observing behavior, but usually slower.
• Network traffic analysis: Examines communications generated by a system or application, not the file itself.
• Package monitoring: Tracks software packages or installations and is not the primary method for examining malware signatures.

• A is correct: It establishes the revised dates and identifies the effect on the critical path.
• B is premature: Stakeholders should be informed after the impact has been assessed and the updated schedule is understood.
• C is not automatically appropriate: Fast-tracking may introduce additional risk and should be considered only after analyzing schedule options.
• D is premature: Adding resources may not resolve a dependency and could increase costs without addressing the actual constraint.

• Azure DevOps Analytics views are accessed through the Power BI Azure DevOps (Boards only) connector.
• This connector authenticates directly to Azure DevOps Services and lets you select an Analytics view.
• Analytics views allow filtering and shaping data in the cloud before Power BI loads it.
• Azure DevOps Server (Boards only) is for an on-premises Azure DevOps Server instance, not Azure DevOps cloud.
• A generic OData Feed connection can access OData endpoints, but it is not the purpose-built connector for Azure DevOps Analytics views.
• “OData queries” describes the underlying technology or query approach, not the most appropriate Azure DevOps connector choice here.

• How quickly the vendor must respond to incidents or requests
• Availability or uptime requirements
• Resolution time targets
• Performance expectations and penalties for failing to meet them

• SOW (Statement of Work): Describes the work, deliverables, scope, and schedule.
• MOA (Memorandum of Agreement): Documents an agreement in which parties commit to specific responsibilities.
• MOU (Memorandum of Understanding): Records a general intention to cooperate; it may not be legally binding.

• A: Concerns task scheduling and avoiding missed runs, not directly sizing the warehouse.
• B: The amount of data in a stream alone does not reliably determine compute needs; the SQL processing is what matters.
• D: A multi-cluster warehouse addresses concurrency, not the compute size needed for one task execution.

• Maximum response and resolution times
• Expected uptime or availability
• Performance requirements
• Escalation procedures
• Consequences for failing to meet requirements

• SOW: Defines a project’s scope, tasks, deliverables, and schedule.
• MOA: Documents specific responsibilities agreed upon by two parties.
• MOU: Records a general understanding or intent to cooperate.

• A. IDS attack signatures: An IDS mainly detects suspicious network traffic. It is not the best control for scanning uploaded documents.
• C. Load balancer: Distributes traffic among servers and can improve availability, but does not specifically detect malware.
• D. WAF: Protects web applications from attacks such as SQL injection and command injection. It may inspect web requests, but malware scanning of PDF files is not its primary function.

• Crowd-sourced evaluation: Requires recruiting and managing many evaluators.
• Human-worker evaluation: Provides human judgment but requires coordination, labeling instructions, and quality control.
• RLHF: Is a training and alignment process, not simply a low-overhead model comparison method.
• Automatic model evaluation: Quickly evaluates outputs using predefined toxicity or quality metrics with minimal manual effort.

• Minimum
• Maximum
• Average
• Count
• Null/empty values
• Distinct values

• Enable column profile
• Enable details pane
• Show column profile in details pane