Amazon AWS Certified Developer Associate Exam Prep
AWS Certified Developer - Associate DVA-C02 (Page 58 )

• Correct answer: B: Use a function alias with different versions.

• AWS Lambda versions are immutable. You publish a version for each code change.
• An alias is a movable pointer to a specific version (e.g., prod ? version 5).
• Deploying a new version is just publishing another version; directing production traffic to the new version is as simple as updating the alias.
• If you need to roll back, you re-point the alias to the previous version. This is minimal operational effort and avoids rebuilding pipelines or managing multiple deployment artifacts at runtime.

• A (blue/green with OpsWorks) adds complexity and tooling overhead.
• C (saving old deployment packages in S3) doesn’t provide runtime switching or instant rollback.
• D (CodePipeline) adds orchestration overhead; while useful, it’s more setup than the alias approach for simple rollback needs.

• Correct answers: IntegrationLatency and Latency.

• Why:

• Why not CacheHitCount/CacheMissCount: those relate to API Gateway caching. If you’re not using caching (or the problem isn’t caching-related), they won’t help diagnose a timeout.

• Practical tip: in CloudWatch, compare Latency and IntegrationLatency for the failing method/resource. If Latency is high but IntegrationLatency is low, the delay is outside the Lambda integration (gateway/network). If IntegrationLatency is high, the Lambda/backend is the bottleneck. Also review CloudWatch Logs for API Gateway and Lambda for additional clues.

• A. IDS attack signatures: An IDS primarily detects suspicious network activity and usually alerts rather than directly scanning and blocking uploaded files.
• C. Load balancer: Distributes traffic among servers but does not inspect files for malware.
• D. WAF: Protects web applications from attacks such as SQL injection, cross-site scripting, and some malicious requests. It is not primarily a PDF-malware scanning solution.

• Detect risky sign-ins from unfamiliar or unidentified IP addresses
• Evaluate user or sign-in risk
• Automatically require a password change because of a risky sign-in

• User risk policy: Can require a user to securely change their password when the account may be compromised.
• Sign-in risk policy: Can require MFA or block access based on the risk of a particular sign-in.

• Answer: False. Agents and managers CAN create knowledge articles from Community questions.

• Why: In ServiceNow, a Community question can be converted into a Knowledge article using the Create Knowledge (or Convert to Knowledge) action. This allows what customers asked to become a reusable article for others.

• How it works (high level):

• Permissions: This ability depends on having the right roles/permissions (e.g., agents or knowledge managers). If you don’t see the option, check your role assignments or the knowledge-management configuration.

• Quick take: If a question in Community is valuable for others, a trained agent can turn it into a Knowledge article, making it available for self-service.

• Workspace: Stores notebooks, libraries, jobs, and other project resources.
• Notebooks: Interactive documents containing code, queries, visualizations, and explanations. Common languages include Python, SQL, Scala, and R.
• Clusters: Groups of virtual machines that run Spark workloads. Clusters can be started, stopped, and automatically scaled.
• Data access: Databricks can read from services such as Azure Data Lake Storage, Azure Blob Storage, and Azure SQL.
• Jobs: Automate notebooks or data-processing workflows on a schedule or in response to events.

• Elicited and documented customer requirements
• Converted them into clear, measurable specifications
• Validated the requirements with the customer
• Used them as the basis for design, quality checks, and acceptance

• A: Requirements should reflect the customer’s needs—not the supplier’s standards.
• B: Iteration review planning does not replace proper requirements collection.
• D: A retrospective occurs after work is completed and is mainly for process improvement, not initial requirements definition.

• 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.

• A — Add a Path for Stage: A Path lets users update the Opportunity’s Stage directly from the record page, without opening the full edit form. It can also display guidance and key fields.
• B — Use a Kanban list view: Users can move Opportunities between stages by dragging records between columns, which is ideal when Stage is the only field they normally change.

• A limited lifetime
• Permissions defined by an IAM role or policy
• The ability to access AWS APIs without using long-term IAM user credentials

• Amazon API Gateway — Builds and manages APIs; it does not generally issue AWS credentials for applications.
• IAM users — Usually use long-term credentials, which are less suitable for applications.
• IAM instance profiles — Provide temporary role credentials to applications running on EC2 instances, but are specifically tied to EC2. The broader requirement described is best met by AWS STS.

• 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.

• View/inspect configuration: Possible.
• Modify the installed application or its S-Controls: Typically restricted in a test-drive org.

• On-Demand: You pay by the hour with no long-term commitment. This is ideal for workloads that are infrequent or have highly variable usage. Since this job runs only 24 hours once per year, you pay only for those 24 hours, with no wasted upfront or long-term payments.
• Reserved Instances: Require a 1- or 3-year commitment and pay for capacity whether you use it or not. For a workload that runs just 24 hours a year, you’d be paying for most of the term unused, making it far less cost-efficient.
• Spot Instances: Can be interrupted whenever AWS needs capacity, which is unacceptable for an uninterruptible workload.
• Dedicated Instances: Generally more expensive and aimed at licensing or compliance requirements; not needed for this use case.

• 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.

• Correct answer: S3 Versioning

• Why the other options aren’t suitable for this protection:

• Quick tip: For enhanced protection, enable Versioning (and consider related features like Object Lock for write-once scenarios in specific compliance cases).

• 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.

• What the dataflow options mean:

• Why C fits best:

• The answer: Question 51: C — Request trigger.

• To invoke a Logic App from an external service (here, the Azure Application Insights availability test) you need a trigger that starts on an incoming HTTP request. In Logic Apps, this is the trigger that exposes an HTTP endpoint — typically shown as "When a HTTP request is received" and referred to as the Request trigger.
• The other options aren’t appropriate:

• Create a Logic App with the Request trigger to generate a URL.
• Configure Application Insights to post the degraded-availability payload to that URL.
• Add actions (e.g., send email, follow-up) in the Logic App to notify the customer services team.

• Storage accounts: storage1, storage2, and storage3
• Log Analytics workspace: Analytics3

• Azure Backup reports can send the AzureBackupReports diagnostic log to eligible storage accounts. The storage account’s subscription and region do not have to match the Recovery Services vault, so all three listed storage accounts are valid.
• The Log Analytics workspace must be in the same region as the Recovery Services vault for this configuration. Vault1 and Analytics3 are both in West Europe, making Analytics3 the valid workspace.

• 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.

• Answer: S3 Inventory report.

• Why: The S3 Inventory report provides a scheduled listing of objects within a bucket, including metadata about each object. When you configure it to include object tagging, you can see tags for each object, and the object keys (which reflect prefixes) and bucket scope are captured in the report. This makes it suitable for auditing and tracking “tags, buckets, and prefixes” across S3 objects.

• What the other options do (not suitable for this purpose):

• Tip: If you need tags in the report, enable the inventory option to include object tagging in the configuration.

• Multiple AWS accounts: Create a separate account for each department or workload. This provides clear isolation and makes it easier to track each department’s usage and costs for chargeback.
• Consolidated billing: AWS Organizations can combine billing for multiple accounts into one central payer account. The company can still view costs by account while managing payment centrally.

• Placement groups: Control how EC2 instances are physically placed for networking or performance; they do not provide departmental billing separation.
• Edge locations: Infrastructure used by services such as Amazon CloudFront; unrelated to chargeback.
• AWS Config: Records and evaluates resource configurations and compliance; it is not a billing-separation mechanism.

• BCP: Maintains essential business operations during and after disruption. It is broader than IT recovery, but the question focuses on restoring the destroyed data center.
• Communication plan: Defines how the organization communicates with staff, the public, and other stakeholders.
• IRP: Handles security incidents such as malware, unauthorized access, or data breaches—not physical disaster recovery.

• User1 can sign in because the account is cloud-only. It does not depend on the on-premises domain.
• User3 can sign in because the account uses Password Hash Synchronization (PHS). Azure AD has a synchronized password hash and can validate the sign-in even when connectivity to on-premises Active Directory is unavailable.
• User2 cannot sign in because the account relies on Pass-through Authentication (PTA). PTA sends the authentication request to an on-premises PTA agent, which must contact a domain controller. With the internet connection from on-premises lost, that validation cannot occur.

• Actual filename: Document.xml.txt
• Explorer displays: Document.xml
• File type: Text Document (.txt)

text 
Document.txt ? Document.xml

• 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.

• 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.

• State is stored remotely in Terraform Cloud/Enterprise, not as a local file.
• Terraform runs (plan/apply) can be executed on remote infrastructure, with results visible in the remote workspace UI.
• Supports workspace-based organization, state locking, and concurrency control.
• Useful for team collaboration, centralized governance, and auditable logs.

• In your configuration, you use a backend block like backend "remote" { ... }.
• You still run terraform init to configure/migrate to the remote backend.
• After setup, terraform plan/terraform apply may be executed remotely, and the outputs/logs appear in the Terraform Cloud/Enterprise UI rather than (only) in your local terminal.

• Other backends store state remotely but typically do not provide remote execution; with the remote backend you get remote runs and a collaborative UI, not just remote state storage.

• 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.

• A separate scope using a /32 subnet: A /32 identifies one host, but it is not a practical DHCP scope for this purpose.
• A static IP within the DHCP range: This can cause an IP address conflict if DHCP assigns that address to another device.
• SLAAC: Used for automatic IPv6 address configuration, not for assigning a predictable IPv4 address through DHCP.

• 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

• The problem describes the class updating field-level security on some profiles. To prevent updating fields that are not updateable for the current user, you should check each field’s updateability before making changes. Use DescribeFieldResult.isUpdateable() (e.g., via Field.describe() on each field) to guard updates.

• A and D (With Sharing): Running a class with Sharing affects record visibility, not whether a field can be updated by a user. It doesn’t prevent updates to fields the user shouldn’t modify.
• C (WITH SECURITY_ENFORCED in SOQL): This enforces FLS/FRC during queries, but it doesn’t control whether the Apex code updates fields or modifies permissions. It’s not addressing the update behavior directly.

• 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.

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

 Bogotá, Colombia