Adobe 9A0-303 Exam Actual Questions
Adobe Photoshop CS6 ACE Exam (Page 15 )

• 25 — Traditional SMTP server-to-server mail transfer; not normally used for secure client submission.
• 110 — POP3, used to retrieve email and typically unencrypted.
• 143 — IMAP, used to access email and typically unencrypted.

• Context: You’ve enabled auditing in Dataverse and need to trim audit data to free storage. The question asks which deletion options you can use.

• What the options typically mean:

• About the provided answer (A, C, D): The list includes “by column,” which isn’t a typical or supported way to purge audit data. The more plausible trio is A (by record), B (date range), and C (by table older than date). If the exam expects A, C, D, that seems incorrect or mis-stated in the source.

• Practical takeaway: To reduce audit storage, use per-record deletions, per-table purge by date, and/or date-range deletions as supported by the environment’s audit management UI or admin docs.

• 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.

• The correct answer is “Think and work holistically” (Option B).

• Focus on value: ensures outcomes matter to stakeholders, but isn’t specifically about end-to-end delivery.
• Optimize and automate: about efficiency and performance, not the holistic view.
• Collaborate and promote visibility: about teamwork and sharing information, not the end-to-end scope.

• You want: a row for each year and each product (year+product), plus a summary row for each year that totals all products (year only).
• GROUP BY ROLLUP(YEAR(SO.ModifiedDate), P.Name) yields:

• GROUP BY CUBE(YEAR(SO.ModifiedDate), P.Name) produces all combinations of the grouping columns, including:

• This adds extra rows (per-year totals by product across all years and a grand total) that aren’t needed for your stated goal.

• If you want exactly the (Year, Product) and (Year) rows, you can use GROUPING SETS ((Year, Product), (Year)) which is equivalent to Rollup in this two-column case but makes intent explicit.

• SELECT YEAR(SO.ModifiedDate) AS OrderYear, P.Name AS ProductName, SUM(SO.OrderQty) AS TotalQty

• 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.

• 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.

• Correct answer: C) After installing ES on the search head(s) and running the distributed configuration management tool.

• ES requires the Splunk_TA_ForIndexes.spl add-on on indexers to apply index-time configurations. It should be deployed via the distributed configuration management (DCM) flow after ES is installed on the search heads, so the indexers receive the proper configuration in a controlled, versioned bundle.

• A) Deploying when adding apps to the deployment server isn’t the ES-specific order; it bypasses the ES-instantiated workflow.
• B) Installing Splunk_TA_ForIndexes.spl first isn’t the recommended order; ES needs to be installed on the search heads first.
• D) It isn’t limited to indexer cluster sites or cluster master commands; the standard flow uses DCM to push the TA to indexers in general.

• Correct answer: B) Context window.

• Why: The context window is the number of tokens the foundation model can process in a single inference. It sets the maximum length of the combined input (prompt) and the model’s output. A larger context window allows more content to be included in one prompt.

• How to use: Check the FM’s context window value and ensure your prompt length plus the desired completion stays within that limit. If needed, summarize or split input across multiple prompts.

• Why the other options aren’t correct:

• Quick tip: Verify the specific FM’s context window for Bedrock, since different models have different token limits.

• The options:

powershell 
Get-AzureVM -ServiceName <service-name> -Name <vm-name> | 
  Set-AzureStaticVNetIP -IPAddress <private-ip> | 
  Update-AzureVM

• New-AzureRMVMConfig configures VM settings but does not itself assign the static IP.
• Set-AzureSubnet is not the appropriate cmdlet for assigning a VM’s private IP.
• The Azure portal option mentioned in the question was not the intended method for this older deployment model.
• Changing settings in Windows Network and Sharing Center is incorrect because Azure assigns the IP through the VM’s virtual network interface; manually configuring it inside the guest can cause conflicts.

• Geo-redundant storage (GRS) replicates data to a secondary Azure region, but the secondary copy is normally unavailable for read access.
• RA-GRS provides geo-replication and read-only access to the secondary region, satisfying all requirements.
• Zone-redundant storage (ZRS) replicates data across availability zones within the same Azure region, not separate geographic regions.
• Locally redundant storage (LRS) keeps copies within a single datacenter or region and does not provide geographic resiliency.

• Domeny tworza restrykcjony razem z typem. Wykorzystujesz je w kolumnach, aby miec spójna walidacje danych w wielu miejscach.
• To nie jest pelny niestandardowy typ z wlasnym input/output funkcjonowaniem. Aby stworzyc typ z wlasnym I/O i operacjami, uzylbys CREATE TYPE (lub rozszerzen w C). Domeny to uproszczona forma ograniczen na istniejacy typ.

• CREATE DOMAIN positive_int AS integer NOT NULL CHECK (VALUE > 0);
• CREATE TABLE orders (id serial, qty positive_int);

• latwo wymuszaja wspólne reguly dla wielu kolumn/naglówków,
• zmiana regul w jednym miejscu (domena) automatycznie wplywa na wszystkie kolumny ja uzywajace.

• 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.

• Developed and maintained independently
• Deployed without changing the entire application
• Scaled separately based on demand
• More resilient when another service has problems

• A: Functional testing is useful, but it is not the main practice described.
• B: Automation helps deployment, but the scenario focuses on architectural decomposition.
• C: Multiple locations improve availability, not application modularity.

• Why: After listing all resources, you need to assign who is responsible for each task. A RACI chart (Responsible, Accountable, Consulted, Informed) documents these roles for every activity.

• What RACI means:

• Why the others aren’t suitable here:

• Quick example: Task "Create user authentication module" — Responsible: Developer, Accountable: Tech Lead, Consulted: Security Architect, Informed: PM.

• Automation handles repetitive tasks, and orchestration coordinates responses across tools (SIEM, SOAR, EDR, etc.).
• This allows the security team to process more events with the same number of people, improving throughput and response times.
• Analysts can focus on more complex tasks like investigation and threat hunting instead of manual triage.

• It increases complexity: automation can reduce complexity when well-implemented; complexity is not the main intended benefit.
• It removes technical debt: automation/OR focuses on current incident response efficiency, not primarily on architectural debt.
• It adds additional guard rails: governance is important but not the core value of automation/orchestration within a SIEM.

• Define the contractor’s deliverables, responsibilities, acceptance criteria, and deadlines.
• Agree on a reporting cadence to monitor progress.
• Allow the local company to use its expertise and manage its own work.

• A: “Under the supervision of the project manager” suggests excessive direct control and is less empowering.
• B: Micromanaging prevents the contractor from functioning independently and is inefficient.
• D: Deploying the contractor’s staff under the PM’s supervision creates a staffing/control arrangement rather than empowering the contracted company.

• Answer: A — EC2 Image Builder

• Why: EC2 Image Builder is AWS’s fully managed service designed to automate the creation, testing, and ongoing management of custom EC2 AMIs. It lets you build image pipelines that start from a base image, apply software/components, run tests, and publish/distribute the resulting AMIs across regions or accounts.

• How it works (high level):

• Why not the other options:

• Quick tip: Use EC2 Image Builder to reduce manual image maintenance and drift, and to enforce consistent, tested base images across environments.

• 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:

• The diagnostic output shows that the protocol is TCP (proto=6) and the session state is 2.
• In FortiGate’s session output, state=2 maps to SYN_SENT. This means the TCP SYN has been sent but the handshake hasn’t completed yet.
• Therefore, the session is not ESTABLISHED, FIN/ACK-related states, or other states; it’s in SYN_SENT.

• The question asks why some tasks appear locked in the HR Guided Setup Module’s Task view.
• The options are:

• C) They require other tasks to be completed first. In guided setup flows, tasks are usually gated by prerequisites to ensure proper sequencing and data/configuration dependencies. You typically unlock a task only after the necessary preceding tasks are finished.

• A) Elevated roles can restrict access in some contexts, but guided setup tasks are commonly gated by prerequisites rather than solely by role.
• B) Deprecation isn’t the general pattern for “locked” tasks in the guided setup.
• D) A plugin activation could unlock certain features, but the common gating mechanism is task dependencies, not plugin state.

• The page shows the answer as D, but based on how HR Guided Setup usually works, the more accurate reason is typically C (prerequisites). If you’re studying, treat C as the conceptual answer and be aware the listed key may be incorrect. If you want, I can walk through each option in more detail.

• 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.

• What the dataflow options mean:

• Why C fits best:

• The Accrue liability on product receipt flag controls whether a liability is accrued and posted at the time of receiving the item.
• If this checkbox is unchecked, receipts won’t create postings to the Product receipt journal. Enabling it ensures the system posts inventory value and the vendor liability when the item is received.

• Go to the Item model group setup form.
• Enable (check) Accrue liability on product receipt.
• Save.

• The other option, Include physical value, affects how physical value is handled but is not the direct cause of postings to the product receipt journal. If issues persist after enabling accrual, you may need to verify related posting definitions or journal setup.

• An IDS monitors network traffic for signs of attacks and raises alerts. It can be deployed out-of-band (via a SPAN/mirror port or a tap) so it analyzes traffic without being inline, meaning it doesn’t measurably affect latency.
• An IPS (intrusion prevention system) is inline and can block threats, but that processing can introduce some latency and potential throughput impact.
• A firewall also inspects traffic and can add latency, though usually modest with proper hardware, but its primary purpose isn’t continuous passive analysis.
• A load balancer distributes traffic and isn’t used for intrusion analysis.

• 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.

• Import mode loads the data into the Power BI model. Visuals query the in-memory model, which generally provides faster calculation and rendering than DirectQuery.
• Import mode also minimizes online processing because report interactions do not send queries back to the dataflow or SQL Server.
• A daily refresh can load data through the previous day, satisfying the freshness requirement without unnecessary hourly processing.

• DirectQuery keeps querying the data source or dataflow during report use, increasing online processing and potentially slowing visuals.
• Adding a gateway enables connectivity for on-premises sources, but it does not remove the performance disadvantages of DirectQuery.
• An hourly Power Automate refresh is unnecessary when the requirement is only data through the previous day and creates additional processing overhead.

• 6-phone boxes: 6x
• 8-phone boxes: 8x

• Problème: le système d’alertes signale des fluctuations tout en manquant les activités qui restent anormalement constantes sur une période longue. Il faut donc un scénario qui capte les anomalies qui se maintiennent dans le temps plutôt que les pics ponctuels.
• Peer: permet de comparer un client à ses pairs; utile pour repérer des écarts relatifs, mais ne cible pas nécessairement les motifs d’anomalie persistante sur plusieurs périodes.
• Revenus mapping: relie les transactions à des sources ou flux de revenus; utile pour l’analyse des fonds, mais pas directement pour détecter une anomalie longue durée.
• Below-the-line: conçu pour détecter des activités qui passent en dessous des seuils habituels ou qui demeurent anormales sur une longue période, comblant ainsi la faiblesse des alertes à long terme.

• Answer: C – netstat -an

• netstat -an lists all active connections and listening ports with numeric addresses, without resolving names. This lets you spot DoS patterns such as a flood of connections to a specific port or many connections from a single or few IPs.
• The other options don’t help as directly:

• Windows: netstat -an and look for numerous ESTABLISHED connections to a service or many SYNs to a port.
• Filter by port, e.g., netstat -an | findstr :80, or on Linux: ss -tunap | grep ':80'.

• Netstat provides a snapshot. DoS detection often benefits from continuous monitoring, flow data, and IDS/IPS logs.

• 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.

• Transact-SQL (T-SQL) is used to query relational data in Azure SQL Database, not telemetry data from monitoring services.
• To analyze monitoring data from Azure SQL Database Intelligent Insights and Azure Application Insights, you should use the Kusto Query Language (KQL) in Azure Monitor Logs/Log Analytics (and in Application Insights Analytics).
• Using KQL lets you query telemetry tables such as requests, dependencies, pageViews, and customEvents (from App Insights) and any integrated monitoring data you’ve ingested into Log Analytics.

• Ensure Intelligent Insights and Application Insights data is available in a Log Analytics workspace or accessible via Application Insights Analytics.
• Go to Azure Monitor > Logs (or Application Insights > Analytics) and write KQL queries, e.g.:

Why B works

• AWS KMS keys are Regional resources. A normal customer managed key in Region A cannot be used directly in Region B.
• A customer managed multi-Region KMS key (MRK) has related key replicas in both Regions. The replicas share key material and key ID, although each has a Regional ARN.
• With client-side encryption, the application encrypts the data before storing it in S3. Cross-Region replication then copies already-encrypted objects, so S3 does not need to decrypt and re-encrypt them with a different Regional key.

Why the other choices are weaker

• A and C: SSE-S3 uses Amazon-managed S3 keys, not the required customer managed KMS key.
• D: SSE-KMS can work only if the solution creates and configures the appropriate KMS key or multi-Region key replica in each Region. As written, it says to create a customer managed KMS key in each Region, which implies separate keys—not the same KMS key. Replication also requires additional KMS permissions and destination encryption configuration.

• Risk transfer means shifting the financial impact of a threat from the organization to another party (the insurer). Buying cyber insurance accomplishes this shift.
• It is not Accept: acceptance of risk would be tolerating it without action.
• It is not Mitigate: mitigation involves reducing likelihood or impact with controls, not shifting costs.
• It is not Avoid: avoidance would be not engaging in the activity that creates the risk; insurance does not remove the risk itself.

• Who performed the action
• What action occurred
• When it occurred
• Where it originated
• Whether it succeeded or failed

• Investigating suspicious activity: Important, but investigation depends on having adequate audit data first.
• Storage allocation: Necessary for retention, but storage does not help if the records lack meaningful details.
• Actions after audit failure: Part of operational planning, not the primary assessment of the application’s audit capability.

• Most recent update: orders results by the date the article was last updated (newest first).
• Relevancy: orders results by how closely articles match your search terms.
• Number of views: orders results by how often an article has been viewed (popularity).

• Popularity is not a standard separate sort option in Knowledge Base (the related concept is typically covered by “Number of views”).
• Manager assignment has no bearing on how Knowledge Base results are ranked.

• Correct answer: Day 3 (Option B)

• Why: In Engagement Studio, when the program is paused, the prospects’ wait times are also paused and do not count down. They resume from the same point when the program restarts. Here, the prospect was at day 3 of a 5-day wait when paused, so they resume on day 3 after the 1-day pause.

• MFA verifies a user’s identity using additional authentication factors, such as a phone prompt or security token.
• It helps prevent unauthorized access even if a password is compromised.
• It does not provide secure storage or encryption of credentials used during automated deployment.

• Correct answer: All four statements are true (A, B, C, and D). The given key (A,B) is likely incorrect.

• A) Customers can change the size of the Warehouse after creation — Yes. You can resize a virtual warehouse after it’s created using ALTER WAREHOUSE ... SET WAREHOUSE_SIZE.
• B) A Warehouse can be resized while running — Yes. Snowflake supports online resizing; you can change the size without stopping queries.
• C) A Warehouse can be configured to suspend after a period of inactivity — Yes. You can enable auto-suspend with a delay (AUTO_SUSPEND).
• D) A Warehouse can be configured to auto-resume when new queries are submitted — Yes. You can enable auto-resume (AUTO_RESUME) so a suspended warehouse automatically resumes on new queries.

• Example commands:

• Even if a warehouse is resizing or auto-suspending, queries will be queued and then executed once the warehouse has the appropriate capacity.

• 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.

1. Ingestion Lambda receives requests from API Gateway and places messages in an SQS queue.
2. Processing Lambda is triggered by the queue and loads records into Aurora PostgreSQL.

• Decoupling: API request handling is separated from database loading.
• Buffering: SQS absorbs bursts when incoming traffic exceeds the database’s processing capacity.
• Controlled concurrency: Configure the SQS-triggered Lambda’s concurrency and batch size to avoid overwhelming Aurora or exhausting database connections.
• Automatic retries: Messages that fail processing can return to the queue and be retried. A dead-letter queue can capture persistent failures.
• Lower operational effort: The solution remains serverless and avoids managing EC2 servers or Kubernetes clusters.

• A: EC2 and Tomcat add server-management overhead.
• B: DynamoDB/DAX would require changing the database model and does not solve the Aurora ingestion design.
• C: SNS is useful for notifications and fanout, but SQS is better for durable buffering and backpressure in this producer-consumer workflow.

1. Create a new AMI or copy the existing AMI while specifying EBS encryption and an appropriate AWS KMS key.
2. This creates an encrypted AMI backed by encrypted EBS snapshots.
3. Copy the encrypted AMI to the destination Region, using a KMS key available there.
4. Launch the application stack from the encrypted AMI.

• B: AWS KMS does not encrypt an existing unencrypted AMI in place. You must create an encrypted copy.
• C: AWS Certificate Manager manages TLS certificates, not EBS or AMI encryption.
• D: Enabling encryption by default affects newly created EBS volumes and snapshots; it does not encrypt an existing unencrypted AMI during copying.

• Answer: No

• Why: Using several Azure AD directories (tenants) would create separate identity stores for each division, which significantly increases administrative overhead. You’d need cross-tenant access management, separate governance, and duplicated admin efforts, which goes against the goal of minimizing administrative work.

• How to meet the goal instead:

• 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.

• IPsec: Protects traffic at the network layer, commonly for VPNs; it is not WPA2’s authentication protocol.
• SSL: Protects specific application sessions and has largely been replaced by TLS.
• SSH: Secures remote command-line administration.

• The AWS Support Concierge is a specialized team within Enterprise Support whose primary role is handling billing and account questions and helping with account setup and getting started.
• They are not the source for architecture guidance or development assistance; those services are provided by Technical Account Managers (TAMs) or Solutions Architects as part of Enterprise Support.
• For technical support cases, customers typically interact with AWS Support engineers or the TAMs, not the Concierge. The Concierge may route or coordinate related requests, but they don’t provide the technical resolution.

• What Concierge does: billing and account inquiries, onboarding help, coordination with AWS support.
• What Concierge does not do: provide architecture/design guidance or in-depth technical support case resolutions.

• 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.

• prompt: the input or instruction given to the model
• completion: the desired response

json 
{"prompt":"Summarize this customer complaint.","completion":"The customer reports a delayed shipment."}

• B: A .txt file containing CSV-style lines is not automatically a valid fine-tuning dataset. The required structure and format must match the selected Bedrock model.
• C: Provisioned Throughput provides dedicated inference capacity; it does not fine-tune a model.
• D: Journals and textbooks might support domain adaptation or pretraining, but they do not provide the prompt-to-answer examples typically required for supervised fine-tuning.