Cisco 300-715 Exam Prep
Implementing and Configuring Cisco Identity Services Engine (300-715 SISE) (Page 11 )

• Data source: Folder
• Transformation: Remove the Content column (or keep only the file-metadata columns)

Why

• File name
• Extension
• Date modified
• Folder path
• Content — the binary contents of the file

1. Initiation

1. Planning

1. Execution

1. Monitoring and controlling

1. Closing

• Menerjemahkan soal ke bahasa Indonesia.
• Menyebutkan jawaban yang benar.
• Menjelaskan alasan jawaban tersebut.
• Memberi tahu jika kunci jawaban yang tersedia tampaknya keliru.
• Menjelaskan istilah teknis penting dalam soal.

• C. Case after-insert trigger that publishes a Platform Event

• D. Custom Apex controller that publishes a Platform Event

• A. Trigger with a callout: Salesforce triggers should not make direct synchronous callouts, and this tightly couples transaction processing to the external system.
• B. Workflow Rule with Outbound Message: This can react to record changes, but it cannot reliably capture a user clicking a menu item. It also does not provide the same flexible event-driven model as Platform Events.

• Identify the causes of delays.
• Improve its workflow and collaboration.
• Adjust tools or development practices.
• Discuss improvements during retrospectives and apply them in future sprints.

• A: An impediment log can help track issues, but merely maintaining one does not remove the impediments.
• B: Directing the team toward solutions reduces team ownership and self-organization.
• C: Adding impediments to the product backlog may be appropriate in some cases, but it does not automatically ensure they are addressed promptly.
• D: Best reflects agile leadership: empowerment, continuous improvement, and removal of impediments.

• Azure Machine Learning Studio is designed to build, test, train, and deploy machine-learning models, including predictive analytics.
• Azure Cosmos DB is a globally distributed NoSQL database. It can store data used by an AI solution, but it is not the primary service for developing and deploying predictive models.
• Therefore, the appropriate recommendation is Azure Machine Learning Studio, as shown in Question 15.

• Driver node: Coordinates Spark jobs and runs in the compute environment.
• Worker node: Executes tasks and processes data.
• Databricks Filesystem (DBFS): Provides storage access for data used by compute resources.

• 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.

• IKE (Internet Key Exchange) negotiates the IPsec security association, including:

• ESP (Encapsulating Security Payload) protects the data after negotiation by providing encryption and optionally authentication.
• AH provides authentication and integrity but does not encrypt traffic.
• TLS is used for protocols such as HTTPS, not for negotiating IPsec parameters.

• 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.

• The table is large.
• Queries frequently filter or join on particular columns.
• The table’s natural data order does not support good micro-partition pruning.
• Clustering improves performance enough to justify its maintenance cost.

1. Install a Cassandra-compatible ODBC driver.
2. Configure a data source name (DSN), if required.
3. In Power BI Desktop, select Get Data > ODBC.
4. Choose the configured DSN and connect.

• Microsoft SQL Server database: Connects to SQL Server, not Cassandra.
• OLE DB: Requires a compatible OLE DB provider; this is not the standard choice for Cassandra in Power BI.
• OData: Requires Cassandra data to be exposed through an OData service, which is not the normal direct connection method.

• AWS Network Firewall can inspect and block traffic across a VPC when traffic is routed through the firewall. A Drop action rule can deny traffic from the suspicious source IP.
• GuardDuty findings are sent to Security Hub.
• A Lambda function can be invoked when Security Hub receives a new GuardDuty finding, extract the source IP, and update the Network Firewall policy with a blocking rule.

• GuardDuty threat lists: Used to identify or increase the severity of findings; they do not directly block VPC traffic.
• AWS WAF: Protects supported web applications, such as CloudFront or Application Load Balancers. It does not provide general VPC-wide traffic blocking.
• GuardDuty suppression rules: Suppress or hide findings. They do not block the suspicious traffic.

• 50% memory utilization baseline: The environment can be provisioned with excess capacity, leaving room for growth and workload spikes.
• Storage scalability: Cloud storage can generally be expanded as needed without redesigning the physical platform.
• Single circuit failure resilience: A properly designed IaaS provider uses redundant power circuits and infrastructure, so failure of one circuit should not take the platform offline.

• A — Dual PDUs: Helps with power redundancy but does not provide storage scalability.
• C — Network load balancing: Provides network-path resilience, not memory or storage scalability, and does not address a power-circuit failure.
• D — Multiple NAS devices: May improve storage capacity, but does not inherently provide power resilience or the required compute-capacity baseline.

• Review the risk register.
• Check the documented risk response strategy, such as using a backup resource, reallocating work, or adjusting sequencing.
• Implement the planned response promptly.

• B: Moving the task may affect the critical path and should not be done without first evaluating the planned response and impacts.
• C: Excluding a required task would change scope and business requirements without proper analysis or approval.
• D: Updating lessons learned and the risk log may be appropriate later, but it does not address the immediate problem. First, execute the response.

• 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.

• True positive: The scan reports a vulnerability that really exists.
• False positive: The scan reports a vulnerability that does not exist.
• True negative: The scan correctly reports no vulnerability.
• False negative: The scan fails to detect a vulnerability that is actually present.

• Red team: Offensive security; simulates attackers and validates exploitability.
• Blue team: Defensive security; monitors, detects, and responds to attacks.
• Purple team: Helps red and blue teams collaborate and improve defenses; not usually the primary testing role.
• White team: Oversees and coordinates the rules, scope, and authorization of an exercise.

• Tokens: Individual units of text processed by a model, such as words, subwords, or characters.
• Models: Systems that learn patterns and generate predictions or outputs.
• Binaries: Data represented using the binary number system, not semantic representations.

• Render thousands of animation frames
• Generate product images in multiple sizes
• Apply filters or transformations to a large image collection
• Produce visual effects for a video

• Separate development, test, and production environments
• Building components in unmanaged solutions during development
• Organizing solutions by functional area
• Exporting and deploying managed solutions to production
• Validating changes before production deployment
• Preventing developers from overwriting one another’s production changes

• Allow the team to identify the causes of delays.
• Encourage improvements to processes, tools, and collaboration.
• Help remove impediments without dictating solutions.
• Protect quality and maintain a sustainable pace.

• A: An impediment log may help track issues, but merely recording them does not resolve the root causes.
• B: Directing teams toward solutions conflicts with the agile principle of a self-managing team.
• C: Adding impediments to the product backlog may delay their resolution and incorrectly treats them as product features.

• A single summarized value, such as total annual sales
• Progress toward a defined target or goal
• Whether performance is below, near, or above the target

• Table: Displays detailed records or summarized rows, but not goal progress visually.
• Stacked Bar: Compares multiple categories and their parts.
• Donut: Shows how a total is divided among categories, not progress toward a target.

• 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.

• No servers or database infrastructure to provision.
• Uses standard SQL.
• Supports formats such as JSON, CSV, Parquet, and ORC.
• You typically define the table structure with AWS Glue Data Catalog or Athena’s own schema definitions.
• Charges are generally based on the amount of data scanned, so partitioning and columnar formats can reduce cost.
• It does not copy the data into a separate database; it reads the files in S3 when you run a query.

• Process Builder: Automates actions based on record changes but does not create interactive forms.
• Approval Process: Routes records for approval; it is not designed to collect information through a wizard.
• Outbound Message: Sends information to an external system; it does not provide a user interface.

• The Azure DevOps (Boards only) connector is designed specifically for Azure DevOps Analytics views.
• It authenticates directly to Azure DevOps and lets you select an existing Analytics view.
• Filters defined in the Analytics view are applied by Azure DevOps before Power BI retrieves the data, reducing the amount of data transferred.

• Azure DevOps Server (Boards only) is for on-premises Azure DevOps Server.
• OData Feed can consume OData endpoints, but it is not the dedicated Azure DevOps Analytics-view connector.
• OData queries are not the intended dedicated connector for this requirement.

• Streams — track change data for tables or views.
• External tables — reference data stored in an external cloud storage location.

• Role — account-level security object.
• Warehouse — account-level compute resource.
• User — account-level security object.
• Share — account-level object used to share data.

• Fine-tuning requires a properly formatted training dataset.
• For classification or instruction-style fine-tuning, examples are supplied in JSONL format.
• Each line represents one training example, typically containing the input prompt and the desired completion or label.
• The file must then be uploaded or made available to the Microsoft Foundry fine-tuning workflow.

• A: Prompt Flow is useful for prompt experimentation, but it is not required to start fine-tuning.
• B: You do not need to deploy the base model to an online endpoint before fine-tuning.
• C: Tracing supports observability and evaluation, but it does not prepare the fine-tuning job or dataset.

• Profitability and financial performance
• Long-term business viability
• Responsible use of resources
• Delivering returns to shareholders

• Social concerns people, communities, labour standards and human rights.
• Environmental concerns impacts on the planet, such as emissions and resource use.
• Legal is important for compliance, but it is not one of the three Triple Bottom Line pillars.

• Products ? OrderHistory: Products is 1, OrderHistory is many (1:*)

• Date ? OrderHistory: Date is 1, OrderHistory is many (1:*)

• A — Technical review by business owner: A business owner may approve the business impact, but a technical review is normally performed by technical personnel.
• C — Cost-benefit analysis after implementation: Cost-benefit analysis should occur before implementation to support the approval decision.
• D — Business continuity testing: This can be appropriate for major changes, but it is primarily part of business continuity and disaster recovery planning—not a universal change-management activity.

• Matching hashes: The software likely has not changed.
• Different hashes: The software was modified or corrupted.

• Encryption: Protects confidentiality by making data unreadable; it does not primarily detect changes.
• Baselines: Define an approved system or configuration state, but they are not the specific integrity mechanism here.
• Tokenization: Replaces sensitive data with substitute values.

• Enforce organizational rules, such as allowed regions or required tags
• Audit resources for compliance
• Deny noncompliant deployments
• Aggregate compliance results across subscriptions

• Ramen is a Linux worm associated with Red Hat Linux 6.2 and 7.0 systems.
• It spreads by scanning randomly generated IP addresses using tools such as synscan.
• It exploits vulnerable network services, including web-related services, and may leave malicious files or altered web content.
• The tar.gz file is also a clue because it is a common Linux archive/package format.

• Code Red: Primarily targeted Microsoft IIS servers running Windows.
• LoveLetter: Spread mainly through email attachments.
• Nimda: A Windows-based worm that spread through email, web servers, and network shares.

1. CALCULATE
2. AND
3. FILTER

DAX 
Transaction Category = 
CALCULATE( 
    MAX('Transaction Size'[Size]), 
    FILTER( 
        'Transaction Size', 
        AND( 
            Sales[Sales Amount] >= 'Transaction Size'[Minimum], 
            Sales[Sales Amount] <= 'Transaction Size'[Maximum] 
        ) 
    ) 
)

• FILTER examines the rows in the Transaction Size table.
• AND ensures the sales amount falls between the category’s minimum and maximum values.
• CALCULATE evaluates the result in the modified filter context and returns the matching category.