Salesforce CRT-101 Exam Prep
Certification Preparation for Administrator (Page 40 )

• Service Cloud focuses on customer service and support.
• Knowledge: Used for a knowledge base of articles to assist agents and customers.
• Entitlements: Define service levels/SLA for handling cases.

• Campaigns and Opportunities/Quotes are typically part of Sales Cloud (marketing and sales functionalities, respectively).

• Two VMs
• Distributed across two separate availability zones

python 
def add_integers(a, b): 
    return a + b

• Uses def to define a function.
• Accepts two parameters, a and b.
• Uses return to send their sum back to the caller.
• Uses indentation to define the function body.

python 
result = add_integers(3, 4) 
result is 7

• Why: Table.Profile returns a per-column profile that includes statistics such as count, null count, distinct count, and standard deviation. It also provides min/max and average where applicable, which matches the requested metrics (Count, Average, Null count, Distinct count, Standard deviation).
• Syntax: Table.Profile(table as table, optional additionalAggregates as nullable list) as table
• How it helps: After running Table.Profile on your table, you can extract the per-column stats you need and display them in your semantic model or report.

• Answer: No.

• Why:

• If you need such an assessment while controlling costs, you’d generally pursue a paid MCS engagement or another option outside of the basic Premier entitlement.

• Azure Resource Manager templates: Use ARM templates to define the VM infrastructure and baseline configuration as code. This ensures all Windows Server 2016 VMs are created with a uniform setup, minimizing manual configuration.

• PowerShell Desired State Configuration (DSC) extension for Windows: DSC enforces a defined state across all servers and can automatically remediate drift. This keeps the VMs in the desired configuration with minimal ongoing admin effort.

• Authenticating websites with TLS/HTTPS
• Enabling certificate-based VPN authentication
• Signing or encrypting email
• Supporting device or user authentication

• VLAN tagging: Uses network-frame tags, such as 802.1Q; it does not rely on X.509 certificates.
• LDAP: May integrate with PKI in some environments, but X.509 certificates are not most commonly associated with LDAP itself.
• MFA: Certificates can be one authentication factor, but X.509 is fundamentally a PKI technology.

• Review iteration length: Changing duration does not resolve unclear requirements.
• Limit work in progress: Useful when excessive multitasking is the problem, but it does not directly clarify requirements.
• Review individual performance: The issue is a process and backlog-quality problem, not necessarily an individual performance problem.

Flat organisation

• Few levels of management
• A wide span of control—managers supervise many employees
• Shorter communication channels
• More employee autonomy and quicker decisions
• Fewer formal promotion levels

Tall organisation

• Many levels of management
• A narrow span of control—managers supervise relatively few employees
• A clear hierarchy and reporting structure
• More formal supervision and communication channels
• More obvious promotion and career stages

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.

• Drop violating frames
• Log or alert on the violation
• Shut down or restrict the port

• Load balancer: Distributes application traffic; it does not control switch MAC learning.
• IPS: May detect some network attacks but is not the primary control for switch MAC-table exhaustion.
• NGFW: Protects traffic crossing network boundaries, while MAC flooding occurs at the switching/LAN layer.

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.

1. Get Data ? Folder in Power BI Desktop.
2. In Power Query Editor, expand the Attributes column to expose metadata such as hidden status and other file attributes.
3. Remove the Content column (and any other data columns you do not need).

• Name
• Extension
• Date accessed
• Date modified
• Folder Path
• Attributes
• Content

• Why: If an application on EC2 needs to call other AWS services, using an IAM role (attached to the EC2 instance as an instance profile) provides temporary, automatically rotated credentials. This avoids embedding access keys in the application.
• How it works: Create a role with the minimum required permissions, attach it to the EC2 instance, and let the AWS SDK used by the application retrieve credentials from the instance metadata service. The credentials rotate automatically and are scoped to the policy you define.
• Why not other options:

• D. Provide public endpoints to store and retrieve data

• E. Manage the infrastructure layer and the operating system

• Classifying data
• Configuring access permissions, such as IAM policies
• Selecting or configuring available encryption options, depending on the requirement

• A. Configure prompt modifications to enforce tone, disclaimers, and refusal behavior at the system level.

• D. Configure Power Platform DLP policies to restrict unauthorized data connectors.

• B: Manual topic-by-topic disclaimer edits are specifically discouraged.
• C: Channel settings do not replace agent-level Responsible AI controls.
• E: Publishing before enabling safety filters violates the stated deployment sequence. Responsible AI filters should be enabled before publication, not afterward.

• Answer: No (B)

• SaaS (Software as a Service) delivers applications over the internet; you don’t manage the underlying infrastructure or OS.
• Deploying Azure virtual machines requires provisioning and managing virtual machines, OS, updates, and networking—this is classic IaaS (Infrastructure as a Service).
• PaaS is for managed runtime environments (apps hosted on a platform), not for VM deployment.

• Correct answer: A
• What this is saying: You need to write a Transact-SQL query that uses REGEXP_SUBSTR to extract the ID from MessageText, and you should apply STRING_ESCAPE(MessageText, 'json') before calling REGEXP_SUBSTR.

• MessageText may contain JSON characters (quotes, backslashes, etc.). Escaping the JSON content first ensures the string you pass to the regex is safe and won’t confuse the regex engine.
• The pattern you apply with REGEXP_SUBSTR would then extract the first substring that matches your ID pattern from the (escaped) text.

• Example (conceptual):

• The question implies REGEXP_SUBSTR is available in this environment; some SQL Server and Fabric contexts don’t expose it natively in all versions. If REGEXP_SUBSTR isn’t available, you’d need an alternative approach (e.g., using built-in regex-like capabilities or CLR/UDFs).
• If you need the ID specifically from the first paragraph, you might first isolate the first paragraph and then apply REGEXP_SUBSTR, or compose a single pattern that anchors to the first paragraph.

• Amazon S3 — Object storage, not a database.
• Amazon DynamoDB — Fully managed NoSQL key-value and document database.
• Amazon Redshift — Managed data warehouse designed for analytics, not primarily transactional MySQL workloads.

• Source: on-premises SQL Server Database1
• Destination: Fabric Warehouse Warehouse1
• Connectivity: on-premises data gateway

• Dataflow Gen1: Primarily for Power Query transformations and isn’t the best Fabric-native choice for this copy operation.
• KQL queryset: Queries data; it does not orchestrate copying from SQL Server.
• Notebook: Could perform custom code-based movement, but it is unnecessary and less direct than a pipeline.

• Enable the owner dimension

• Assign a standard costing inventory model

• Batch dimension tracks batches or lots, such as expiration or production batches. It does not identify inventory ownership.
• Moving average is a costing method, but it is not the required choice for this consignment configuration.

• As the project progresses, you gain information, refine requirements, and lock down baselines (scope, schedule, cost). This reduces overall risk and uncertainty because more is known and addressed.
• At the same time, changes become more costly over time. Later changes can affect already approved baselines, cause rework, impact the critical path, require more coordination, and incur penalties or delays, all of which raise the cost of making changes.

• 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.

• A. Extranet
• B. VLAN
• C. Intranet
• D. VPN

• 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.

1. Determine which host was infected first—the originating host.
2. Decide whether each other host is clean or infected based on communication and event logs.

• Compare timestamps and find the earliest suspicious event.
• Look for the host that initiated the malicious connection, file transfer, exploit, or command—not merely a host that later received traffic.
• Follow the timeline through the firewall:

• A host is likely infected if it both receives suspicious activity and then initiates related malicious traffic.
• A host may be clean if the firewall blocked the connection and there is no evidence of execution or follow-on communication.

• The stage has a default file format defined (e.g., when you create the stage with FILE_FORMAT = (...)), and Snowflake will use that default.

• You use a named file format attached to the stage, or you specify an inline format in the COPY command, e.g., COPY INTO my_table FROM @my_stage FILE_FORMAT = (TYPE = 'CSV');

• Use a stage default: omit FILE_FORMAT in COPY.
• Or rely on a named/inline format: specify FILE_FORMAT in COPY or set a format on the stage.

• Azure Cosmos DB is a globally distributed NoSQL database service. It stores and retrieves application data, but it does not provide the tools needed to build, test, and deploy predictive analytics models.
• Azure Machine Learning—including the historical Azure Machine Learning Studio—is designed for the machine-learning lifecycle:

• 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.

• Startup scripts run at computer startup, before any user logs on, making them ideal for machine-wide configuration on each new VM.
• They execute under the Local System account, which is appropriate for initial setup tasks.
• Linking a GPO to the OU ensures every new VM in that domain automatically receives the configuration—great for scalability.

• A) SetupComplete.cmd runs only once, right after Windows Setup, not on every boot, so it isn’t ideal for provisioning multiple VMs consistently.
• B) Logon scripts run per user, not per machine, so they aren’t reliable for initial VM configuration before a user signs in.
• D) Placing scripts in a VHD doesn’t automatically execute them.

• The mapping and resolution of endpoint identities (EIDs) to their location (RLOCs) in an SD-Access fabric is handled by the control plane using the LISP protocol.
• Why not the others:

• Answer: C (192.168.0.1-192.168.0.99, 192.168.0.101-192.168.0.254)

• Why: The interface is 192.168.0.100/24, so DHCP pools must be within the 192.168.0.0/24 subnet and cannot include the router’s IP (192.168.0.100), the network address (192.168.0.0), or the broadcast (192.168.0.255). Option C provides two ranges that exclude those addresses.

• Why the other options are invalid:

• Control plane: Managed by Databricks and contains the Databricks web application, workspace services, cluster management, and job orchestration.
• Data plane: Runs in your cloud account and contains compute resources such as:

• Worker node: Data plane compute.
• Driver node: Data plane compute.
• JDBC data source: Usually an external data system.
• Databricks Filesystem (DBFS): Provides storage access and is backed by cloud object storage rather than being entirely part of the control plane.

• The 99.99% availability SLA for VMs is achieved when you run VMs across two or more Availability Zones.
• Two VMs in a single Availability Zone do not meet the 99.99% target (their SLA is lower, e.g., around 99.95% or less depending on configuration).

• Deploy at least two VMs across two different Availability Zones (one VM in each zone, or both in two zones with a load balancer).
• This provides the required zone redundancy and the 99.99% SLA.

• Network Security Groups (NSGs) control network traffic to and from Azure resources.
• By applying NSGs to the web tier and the database tier (subnets or NICs), you can:

• NSGs provide the necessary granularity (source/destination, port, protocol) to enforce the desired traffic patterns between tiers.
• For easier management you can use separate subnets for each tier or, optionally, use Application Security Groups to simplify rule management.

• 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.

• Correct answer: UBA (A)

• Why: UBA (User Behavior Analytics) analyzes user activity, builds baselines of normal access patterns, and flags deviations (e.g., accessing sensitive projects not related to the user’s role). This insider-threat detection is exactly what the scenario requires.

• Why not the others:

• Coordinate and manage the assessment team
• Assign assessment activities
• Direct interviews, examinations, and tests
• Ensure the assessment follows the CAP

• Answer key: Lead Assessor — likely wrong
• CAP-based answer: C3PAO

• Ethical red-teaming is adversarial testing aimed at uncovering weaknesses and potential exploits in an AI system before deployment.
• A (improving accuracy) is not the primary?? of red-teaming.
• B (simulate model risk scenarios) is related but narrower; red-teaming centers on finding vulnerabilities and risk exposure, not just simulating risks.
• D (ensure compliance) is not the main goal of red-teaming.

• Basic: Self-help only; no technical support or design assessment.
• Developer/Standard: Technical support, but generally not the full proactive architecture guidance required here.
• Professional Direct: Includes proactive guidance and architecture recommendations.
• Premier: Also supports this, but is not the minimum-cost option.

• Goal: serving a static website globally with minimal latency.
• Why C works: CloudFront is a Content Delivery Network (CDN) that caches content at many edge locations around the world. When users request the site, CloudFront serves cached copies from the nearest edge location, greatly reducing latency. It also reduces load on your EC2 origin by handling many requests at the edge.
• Why the other options aren’t as good:

• IaaS: You manage the virtual machine’s operating system, configuration, applications, and data. Azure manages the physical infrastructure and virtualization platform.
• PaaS: You deploy applications without managing the underlying operating system or servers.
• SaaS: You consume a complete application managed by the provider, such as Microsoft 365.

• 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.

• Create a record-triggered Flow on Opportunity.
• Run it when the record is created or updated.
• Add entry criteria such as Probability > 50%.
• Use Create Records to create the related Sales Demo Request, populating its Opportunity lookup.

• Apex Trigger: Possible, but unnecessary custom code for a straightforward automation requirement.
• Workflow Rule: Cannot create a related custom record.
• Process Builder: Could create a record, but Flow is Salesforce’s preferred and more capable declarative automation tool.

• 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.

• Equipment and hardware
• Facilities
• Inventory
• Backup media
• Supplies stored at an alternate or remote location

• Personal belongings of staff: Usually not organizational assets.
• DR line-item revenues: Revenue is a financial impact, not a physical asset.
• Cloud-based applications: These are primarily logical or information-processing assets, even though they depend on physical infrastructure.

kusto 
Event | where EventType == "error"

• == is the KQL equality operator.
• -eq is a PowerShell operator, not KQL.
• is is not the correct equality operator in this context.

text 
Event | where EventType –eq "error"

• Event | search "error" — valid, broad search.
• search in (Event) "error" — valid, broad search.
• Event | where EventType == "error" — valid, targeted filter.

• Interactive development and exploration
• Small datasets
• Lightweight transformations or testing
• Avoiding the overhead and cost of multiple workers

• Automated reports: Jobs may benefit from job clusters or SQL warehouses.
• Working with SQL in Databricks SQL: Databricks SQL warehouses are the relevant compute option.
• Automatically scaling for larger data: A multi-node cluster with autoscaling is more suitable.
• Large amounts of data: Distributed worker nodes are generally needed.