ASQ CCQM Exam Prep
Certified Construction Quality Manager (Page 12 )

• Drops packets when traffic exceeds a certain bitrate: This describes traffic policing, not shaping.
• Fragments large packets and queues them: Shaping does not fragment packets as its primary congestion-control action.
• Drops packets randomly from lower-priority queues: Packet dropping may occur if a shaping queue becomes full, but it is not the defining behavior.

• Shaping: buffers and delays excess traffic.
• Policing: drops or marks traffic that exceeds the configured rate.

• C — Custom buttons display at the top and bottom of a page: Correct. In Salesforce Classic, buttons can appear in the page’s button areas, including the top and bottom of record pages.
• D — Custom buttons are available for Person Accounts: Correct. Person Accounts use the Account object, so Account custom buttons can be available on Person Account layouts.
• E — Custom buttons can reference an external app: Correct. A URL custom button can launch or navigate to an external application or website.

• A: JavaScript custom buttons are a Salesforce Classic feature and are not supported in Lightning Experience. They do not enhance Lightning Experience.
• B: The User object does not generally support custom buttons in the same way standard business objects do.

• Financial performance
• Customer perspective
• Internal business processes
• Learning and growth

• Increasing the number of indicators can make the scorecard cluttered and less useful. Measures should be limited to those that are strategically important.
• The scorecard is mainly a tool for implementing and monitoring strategy, not for formulating strategy.
• It should contain both leading indicators and lagging indicators. Leading indicators predict future performance, while lagging indicators measure results already achieved.

• Entradas grandes, de até aproximadamente 1 GB.
• Processamentos demorados, que podem levar até cerca de 1 hora.
• O cliente envia a solicitação e consulta o resultado depois, sem precisar manter uma conexão aberta durante todo o processamento.

• Real-time inference: indicada para respostas imediatas, com entradas menores e processamento rápido.
• Serverless inference: adequada para cargas variáveis e solicitações menores; não é ideal para processamento longo e arquivos grandes.
• Batch transform: processa grandes volumes em lote, mas não atende à necessidade de respostas próximas do tempo real.

• 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.

• Modelo de embeddings multimodal: transforma texto e imagens em vetores no mesmo espaço semântico. Isso permite comparar uma consulta textual com imagens, ou uma imagem com textos relacionados.
• Modelo generativo multimodal: interpreta diferentes modalidades e gera conteúdo, mas não é a escolha específica para indexação e busca vetorial.
• Modelo de geração de imagens: cria imagens, portanto é inadequado.
• Modelo de embeddings de texto: não atende consultas com imagens.

• Database data: Aggregates are used to retrieve structured data, commonly from entities and database sources.
• Screen scope: A Screen Aggregate belongs to the screen where it is defined and is not directly reusable as a screen-level aggregate elsewhere.
• Asynchronous and parallel execution: Multiple Screen Aggregates can run asynchronously and in parallel, helping the screen load data efficiently.

• 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.

• Documented and evaluated
• Assessed for security, cost, and operational impact
• Reviewed and approved by the appropriate personnel
• Tested before implementation
• Tracked with version history and rollback procedures

• Risk management process: May help assess the issues, but it does not authorize and manage design changes.
• Product design process: Applies primarily while creating a new design.
• Design review process: Normally evaluates a design before release; this design is already released.

• Career enhancement — A mentor can provide guidance, advice, networking support and help the mentee identify career opportunities.
• Support with new challenges — A mentor can help the mentee think through unfamiliar situations and develop confidence and practical solutions.
• Personal development — Mentoring can improve skills such as communication, leadership, decision-making and self-awareness.

• Solving all problems — The mentor supports and guides the mentee but should not take responsibility for solving every problem.
• Discipline would be dispensed if they make a mistake — Mentoring does not remove normal accountability or workplace consequences. Mistakes should instead become learning opportunities.

• T?o m?t Systems Manager Automation runbook d? ch?y l?nh kh?i d?ng l?i ?ng d?ng trên các EC2 instance.
• T?o m?t Amazon EventBridge rule ph?n ?ng khi CloudWatch alarm chuy?n sang tr?ng thái ALARM.
• Ð?t Automation runbook làm target c?a EventBridge rule.

1. Log c?a ?ng d?ng du?c ghi vào CloudWatch Logs.
2. CloudWatch alarm phát hi?n nhi?u l?i liên quan d?n vi?c restart.
3. Alarm chuy?n sang tr?ng thái ALARM.
4. EventBridge nh?n s? ki?n thay d?i tr?ng thái này.
5. EventBridge g?i Systems Manager Automation runbook.
6. Runbook restart ?ng d?ng trên các EC2 instance, không restart c? instance.

• A: SNS không tr?c ti?p g?i Systems Manager Automation runbook nhu m?t target native.
• B: Lambda có th? th?c hi?n vi?c restart, nhung ph?i t? x? lý vi?c xác d?nh instance và ch?y l?nh t? xa.
• C: Lambda ch? làm trung gian d? g?i runbook, không c?n thi?t vì EventBridge có th? g?i runbook tr?c ti?p.

• Accept: Acknowledging the risk without taking additional action.
• Transfer: Shifting responsibility or financial impact to another party, such as through insurance or outsourcing.
• Mitigate: Reducing the likelihood or impact of the risk through controls.
• Avoid: Eliminating the activity or condition that creates the risk.

• 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.

sql 
CREATE TABLE my_table 
USING CSV 
OPTIONS (path '/path/to/csv');

sql 
CREATE TABLE my_table 
USING CSV 
LOCATION '/path/to/csv';

• USING CSV specifies the file format.
• path or LOCATION specifies where the files are stored.
• USING DELTA would be used only when the source data is in Delta format.
• FROM is used in a SELECT statement, not normally as the format declaration in CREATE TABLE.

• The VM keeps its configuration, disks, IP settings, and associated resources.
• The operation may cause downtime.
• You cannot guarantee that it will move specifically to Host2; Azure selects the new host.
• Update management is for update configuration, not host relocation.
• Moving to another subscription does not move the VM to a different physical host.
• Usage location is unrelated to VM placement.

text 
heartrateheartrateheartrate

• Spark column objects can be multiplied, for example:

python 
  df["heartrate"] * 3

• A DataFrame itself cannot be multiplied, but the expression is referring to a column.
• It is also not primarily a Python syntax error; the code was parsed, then Spark failed while resolving the column name.

• A — VARIANT "null" values are not the same as SQL NULL: Correct.

• B — Frequent, single-row DMLs: Incorrect.

• C — Validate data before loading into the target table: Correct.

• D — Use staging tables to manage MERGE statements: Correct.

• Check whether the data is accurate, complete, and properly prepared.
• Confirm that the pattern is not caused by an outlier, data-quality problem, or modeling issue.
• If the data is valid, treat the result as a potentially valuable new business insight.

• A: A data scientist may help, but expert validation and data-quality checking come first.
• C: Filtering out inconvenient data risks hiding a valid insight.
• D: Einstein’s result should not be accepted without verification simply because it produced the finding.

• 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.

• Approve major information security policies
• Allocate funding and resources
• Set the organization’s risk appetite
• Ensure security supports business objectives

• Evaluating vendors is generally handled by security, procurement, or technical teams.
• Assessing risks is typically performed by security and risk-management professionals, with management accepting or prioritizing the risks.
• Monitoring regulatory adherence is usually handled by compliance, audit, or security functions.

• Separate accounts/domains create a strong administrative boundary between non-production and production.
• Separate projects provide distinct IAM boundaries, billing, quotas, service accounts, and resources.
• Developers can receive permissions in the development project without automatically gaining access to production.
• Production administrators can be restricted from development resources, supporting separation of duties and least privilege.

• A: Creates excessive project fragmentation without being required by the stated requirement.
• C: Keeps development and production under one account and does not clearly separate administrative duties.
• D: Uses one account and only two projects; it provides some project isolation but weaker organizational separation.

• The legacy system creates a signed JWT assertion.
• Salesforce validates the assertion using a certificate configured on a Connected App.
• Salesforce issues an access token for the integration user.
• No refresh token is required; the system can request a new access token when needed.

• Web Server Flow: normally uses a client secret and authorization/refresh tokens.
• Username-Password Flow: requires storing Salesforce credentials and is discouraged for integrations where stronger options are available.
• User Agent Flow: intended for user-interactive applications, not unattended server-to-server processing.

1. Product + ProductSubCategory: Inner join

1. Result + ProductCategory: Left outer join

• Microservices: An application-design approach that divides an application into smaller services. It does not inherently reduce the number of operating systems.
• Virtualization: Creates virtual machines, but each VM generally requires its own complete operating system. This improves isolation but does not minimize OS instances as effectively.
• Infrastructure as code: Uses code to automate infrastructure deployment and configuration. It improves consistency but does not determine how many operating systems are used.

• 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.

• 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.

• Server provisioning
• Operating systems and runtime infrastructure
• Scaling
• Much of the maintenance and patching

• Thin clients reduce endpoint hardware requirements but do not specifically streamline code deployment.
• Private cloud provides dedicated cloud infrastructure but usually requires more management and expense.
• Virtual machines improve resource utilization, but administrators still manage the VMs, operating systems, and deployments.

• 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.

• Subject: The entity that initiates an action—here, Derrick/user account.
• Object: The resource being accessed— the file.
• Action: Reading the file.

• The current and previous versions
• Which clauses were changed
• When each revision occurred
• Potentially, who made the changes

• Version validation: Verifies a version’s accuracy or integrity but does not primarily track revisions.
• Version changes: Not the standard name for a formal document-management process.
• Version updates: Describes updating a document, not the complete process of managing and tracking its revisions.

• Azure creates a corresponding service principal in Microsoft Entra ID.
• The identity’s lifecycle is tied to the Azure resource.
• If the resource is deleted, Azure automatically deletes the identity.
• Only the associated resource can use that identity to request tokens.
• You can grant the identity permissions to access services such as Key Vault or Storage without storing credentials in code.

• IKE: Negotiates security parameters for IPsec VPNs; it does not identify switch ports.
• VLAN: A logical network segmentation method, not a discovery protocol.
• netstat: Displays local network connections and listening services, but cannot identify the physical switch or port.

• AWS AppSync is designed for serverless applications that need to retrieve and combine data from multiple sources.
• A pipeline resolver can invoke multiple functions or data-source resolvers, including resolvers connected to different DynamoDB tables.
• The application can receive the combined result through one GraphQL request, without requiring custom aggregation logic in the application.
• This is operationally efficient because AppSync manages the API layer and resolver execution.

• Athena Federated Query is intended primarily for analytics and ad hoc querying, not low-latency application data retrieval.
• Querying DynamoDB through Athena can consume DynamoDB read capacity and may affect table performance.
• It also introduces analytical-query overhead and is not the normal choice for a microservice web application.

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

• Cloud financial management
• Portfolio management
• Data governance
• Risk management
• Compliance

Simple explanation

• The Azure VPN gateway to connect to
• Authentication settings
• Routes for networks that the client can access

• Red team: Simulates attackers; exploits weaknesses to test security.
• Blue team: Defends systems; monitors, detects, and responds to attacks.
• Purple team: Combines red- and blue-team efforts to improve detection and defense.
• White team: Oversees or manages the test, defining rules, scope, and authorization.

• The VPN gateway is in VirtualNetworkA.
• Gateway transit is configured on the peering associated with the VNet that contains the gateway—here, VirtualNetworkA.
• A peered VNet may use the remote gateway through the appropriate peering option, but this does not automatically update an existing P2S client’s routes.
• The Windows 10 workstation’s P2S VPN client package was created before VirtualNetworkB was peered, so it may not have a route for VirtualNetworkB.

• Confirm the required columns and data types
• Identify the target/label column
• Handle missing or duplicate records if present
• Transform data into a suitable format
• Split it into training and validation/test datasets

1. Prepare the training data
2. Select an algorithm or configure AutoML
3. Train the model
4. Evaluate its performance
5. Tune hyperparameters if needed
6. Deploy the model

• SAML: Enables federated authentication and single sign-on between the IdP and SaaS providers.
• OAuth tokens: Allow delegated application access; OAuth is not primarily the first step for employee SSO.
• Password vaulting: Stores or autofills multiple passwords but does not eliminate the separate credentials or provide true federation.

• Writing with Apache Spark, typically as Delta tables.
• Reading with Apache Spark for processing and analysis.
• Reading with T-SQL through the lakehouse’s automatically generated SQL analytics endpoint.
• Reading with KQL through Fabric’s query capabilities that can access lakehouse data.

• Eventhouse: Optimized for real-time and KQL workloads, but it is not the natural destination for Spark-based writes or general lakehouse storage.
• Datamart: Intended mainly for relational analytics and Power BI; it is not designed for Spark-based data ingestion.
• Warehouse: Primarily T-SQL-based and structured. It does not provide the same native Spark write experience or flexibility for semi-structured data.

• SOW
• SLA
• MOA
• MOU

• A: Configure security groups — Customers define inbound and outbound rules for resources such as Amazon EC2 instances.
• B: Classify company assets — Customers decide how their data and assets should be classified and protected.

• C: Amazon S3 is a regional service; customers select a Region, not individual Availability Zones for buckets.
• D: AWS manages the infrastructure and patching for Amazon DynamoDB.
• E: AWS manages the infrastructure used by AWS Lambda; customers do not select EC2 instances for Lambda functions.

• B — AWS DataSync: Transfers data between storage systems, such as S3, file systems, and on-premises storage. It is not a marketplace or catalog for third-party datasets.
• C — Kinesis Data Streams and CodeCommit: CodeCommit is a source-code repository, not a third-party data provider. Kinesis is for streaming data ingestion.
• D — Kinesis Data Streams and ECR: ECR stores container images, not datasets. Kinesis does not provide access to ECR data as an external dataset source.

1. Define Power Platform DLP policies

1. Add custom prompt modifications

1. Conduct adversarial testing

• Replaces the verbal message — this describes non-verbal communication being used instead of words.
• Substitutes for verbal communication — similar; it means communicating without speech.
• Confuses the verbal message — this occurs when non-verbal signals contradict or obscure the spoken message.

• Create and manage approved IT services as products
• Control which users can deploy those products
• Apply governance, permissions, and constraints
• Use and manage infrastructure-as-code templates, such as AWS CloudFormation templates

• AWS Resource Explorer: Helps find and browse AWS resources; it does not govern IaC deployments.
• AWS Organizations: Manages accounts, consolidated billing, and organization-wide policies.
• AWS Systems Manager: Provides operational management, such as patching, automation, and parameter storage, but it is not primarily used to catalog and govern IaC-based service deployments.

• Secure: uses encryption, such as HTTPS or SFTP.
• Cross-platform: supported by many languages and operating systems.
• Internet-friendly: designed to communicate across networks, usually using standard Internet protocols.

• JSON/REST over HTTPS — widely supported, secure, and Internet-friendly.
• SOAP over HTTPS — also secure and highly cross-platform, though more heavyweight.
• XML over SFTP — secure file-based exchange, but it is not an API-style interface.

• B — Increase provisioned concurrency for Lambda

• D — Refactor the Lambda functions

• E — Use Amazon RDS Proxy

• 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.