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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q133-Q138):

NEW QUESTION # 133
A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

  • A. Create a NAT gateway within the corporate VPC.
  • B. Create VPC peering with Amazon VPC hosting Amazon SageMaker.
  • C. Route Amazon SageMaker traffic through an on-premises network.
  • D. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.

Answer: D

Explanation:
Explanation
To enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances, the company should create Amazon SageMaker VPC interface endpoints within the corporate VPC. A VPC interface endpoint is a gateway that enables private connections between the VPC and supported AWS services without requiring an internet gateway, a NAT device, a VPN connection, or an AWS Direct Connect connection. The instances in the VPC do not need to connect to the public internet in order to communicate with the Amazon SageMaker service. The VPC interface endpoint connects the VPC directly to the Amazon SageMaker service using AWS PrivateLink, which ensures that the traffic between the VPC and the service does not leave the AWS network1.
References:
1: Connect to SageMaker Within your VPC - Amazon SageMaker


NEW QUESTION # 134
A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

  • A. Random Cut Forest (RCF)
  • B. K-means
  • C. Seq2seq
  • D. XGBoost

Answer: D

Explanation:
Explanation
XGBoost is a built-in Amazon SageMaker machine learning algorithm that should be used for modeling the credit card fraud detection problem. XGBoost is an algorithm that implements a scalable and distributed gradient boosting framework, which is a popular and effective technique for supervised learning problems.
Gradient boosting is a method of combining multiple weak learners, such as decision trees, into a strong learner, by iteratively fitting new models to the residual errors of the previous models and adding them to the ensemble. XGBoost can handle various types of data, such as numerical, categorical, or text, and can perform both regression and classification tasks. XGBoost also supports various features and optimizations, such as regularization, missing value handling, parallelization, and cross-validation, that can improve the performance and efficiency of the algorithm.
XGBoost is suitable for the credit card fraud detection problem for the following reasons:
The problem is a binary classification problem, where the goal is to predict whether a transaction is fraudulent or not, based on the information from new transactions. XGBoost can perform binary classification by using a logistic regression objective function and outputting the probability of the positive class (fraudulent) for each transaction.
The problem involves a large and imbalanced dataset of historical data labeled as fraudulent. XGBoost can handle large-scale and imbalanced data by using distributed and parallel computing, as well as techniques such as weighted sampling, class weighting, or stratified sampling, to balance the classes and reduce the bias towards the majority class (non-fraudulent).
The problem requires a high accuracy and precision for detecting fraudulent transactions, as well as a low false positive rate for avoiding false alarms. XGBoost can achieve high accuracy and precision by using gradient boosting, which can learn complex and non-linear patterns from the data and reduce the variance and overfitting of the model. XGBoost can also achieve a low false positive rate by using regularization, which can reduce the complexity and noise of the model and prevent it from fitting spurious signals in the data.
The other options are not as suitable as XGBoost for the credit card fraud detection problem for the following reasons:
Seq2seq: Seq2seq is an algorithm that implements a sequence-to-sequence model, which is a type of neural network model that can map an input sequence to an output sequence. Seq2seq is mainly used for natural language processing tasks, such as machine translation, text summarization, or dialogue generation. Seq2seq is not suitable for the credit card fraud detection problem, because the problem is not a sequence-to-sequence task, but a binary classification task. The input and output of the problem are not sequences of words or tokens, but vectors of features and labels.
K-means: K-means is an algorithm that implements a clustering technique, which is a type of unsupervised learning method that can group similar data points into clusters. K-means is mainly used for exploratory data analysis, dimensionality reduction, or anomaly detection. K-means is not suitable for the credit card fraud detection problem, because the problem is not a clustering task, but a classification task. The problem requires using the labeled data to train a model that can predict the labels of new data, not finding the optimal number of clusters or the cluster memberships of the data.
Random Cut Forest (RCF): RCF is an algorithm that implements an anomaly detection technique, which is a type of unsupervised learning method that can identify data points that deviate from the normal behavior or distribution of the data. RCF is mainly used for detecting outliers, frauds, or faults in the data. RCF is not suitable for the credit card fraud detection problem, because the problem is not an anomaly detection task, but a classification task. The problem requires using the labeled data to train a model that can predict the labels of new data, not finding the anomaly scores or the anomalous data points in the data.
References:
XGBoost Algorithm
Use XGBoost for Binary Classification with Amazon SageMaker
Seq2seq Algorithm
K-means Algorithm
[Random Cut Forest Algorithm]


NEW QUESTION # 135
A finance company needs to forecast the price of a commodity. The company has compiled a dataset of historical daily prices. A data scientist must train various forecasting models on 80% of the dataset and must validate the efficacy of those models on the remaining 20% of the dataset.
What should the data scientist split the dataset into a training dataset and a validation dataset to compare model performance?

  • A. Starting from the earliest date in the dataset. pick eight data points for the training dataset and two data points for the validation dataset. Repeat this stratified sampling until no data points remain.
  • B. Sample data points randomly without replacement so that 80% of the data points are in the training dataset. Assign all the remaining data points to the validation dataset.
  • C. Pick a date so that 80% to the data points precede the date Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.
  • D. Pick a date so that 80% of the data points occur after the date. Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.

Answer: C

Explanation:
A Comprehensive Explanation: The best way to split the dataset into a training dataset and a validation dataset is to pick a date so that 80% of the data points precede the date and assign that group of data points as the training dataset. This method preserves the temporal order of the data and ensures that the validation dataset reflects the most recent trends and patterns in the commodity price. This is important for forecasting models that rely on time series analysis and sequential data. The other methods would either introduce bias or lose information by ignoring the temporal structure of the data.
References:
Time Series Forecasting - Amazon SageMaker
Time Series Splitting - scikit-learn
Time Series Forecasting - Towards Data Science


NEW QUESTION # 136
A company that runs an online library is implementing a chatbot using Amazon Lex to provide book recommendations based on category. This intent is fulfilled by an AWS Lambda function that queries an Amazon DynamoDB table for a list of book titles, given a particular category. For testing, there are only three categories implemented as the custom slot types: "comedy," "adventure," and "documentary." A machine learning (ML) specialist notices that sometimes the request cannot be fulfilled because Amazon Lex cannot understand the category spoken by users with utterances such as "funny," "fun," and "humor." The ML specialist needs to fix the problem without changing the Lambda code or data in DynamoDB.
How should the ML specialist fix the problem?

  • A. Add the unrecognized words as synonyms in the custom slot type.
  • B. Add the unrecognized words in the enumeration values list as new values in the slot type.
  • C. Use the AMAZON.SearchQuery built-in slot types for custom searches in the database.
  • D. Create a new custom slot type, add the unrecognized words to this slot type as enumeration values, and use this slot type for the slot.

Answer: A

Explanation:
The best way to fix the problem without changing the Lambda code or data in DynamoDB is to add the unrecognized words as synonyms in the custom slot type. This way, Amazon Lex can resolve the synonyms to the corresponding slot values and pass them to the Lambda function. For example, if the slot type has a value "comedy" with synonyms "funny", "fun", and "humor", then any of these words entered by the user will be resolved to "comedy" and the Lambda function can query the DynamoDB table for the book titles in that category. Adding synonyms to the custom slot type can be done easily using the Amazon Lex console or API, and does not require any code changes.
The other options are not correct because:
Option A: Adding the unrecognized words in the enumeration values list as new values in the slot type would not fix the problem, because the Lambda function and the DynamoDB table are not aware of these new values. The Lambda function would not be able to query the DynamoDB table for the book titles in the new categories, and the request would still fail. Moreover, adding new values to the slot type would increase the complexity and maintenance of the chatbot, as the Lambda function and the DynamoDB table would have to be updated accordingly.
Option B: Creating a new custom slot type, adding the unrecognized words to this slot type as enumeration values, and using this slot type for the slot would also not fix the problem, for the same reasons as option A. The Lambda function and the DynamoDB table would not be able to handle the new slot type and its values, and the request would still fail. Furthermore, creating a new slot type would require more effort and time than adding synonyms to the existing slot type.
Option C: Using the AMAZON.SearchQuery built-in slot types for custom searches in the database is not a suitable approach for this use case. The AMAZON.SearchQuery slot type is used to capture free-form user input that corresponds to a search query. However, this slot type does not perform any validation or resolution of the user input, and passes the raw input to the Lambda function. This means that the Lambda function would have to handle the logic of parsing and matching the user input to the DynamoDB table, which would require changing the Lambda code and adding more complexity to the solution.
References:
Custom slot type - Amazon Lex
Using Synonyms - Amazon Lex
Built-in Slot Types - Amazon Lex


NEW QUESTION # 137
A Machine Learning Specialist is training a model to identify the make and model of vehicles in images The Specialist wants to use transfer learning and an existing model trained on images of general objects The Specialist collated a large custom dataset of pictures containing different vehicle makes and models

  • A. Initialize the model with random weights in all layers including the last fully connected layer
  • B. Initialize the model with random weights in all layers and replace the last fully connected layer
  • C. Initialize the model with pre-trained weights in all layers including the last fully connected layer
  • D. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

Answer: C


NEW QUESTION # 138
......

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Cisco 300-440 Exam Syllabus Topics:

TopicDetails
Topic 1
  • SD-WAN Cloud Connectivity: Questions about configuration of SD-WAN-based cloud connectivity using Cisco infrastructure appear in this topic. Furthermore, it discusses configuration of Cisco SD-WAN OnRamp, configuration for connecting to a SaaS cloud provider, and configuration of Cisco SD-WAN policies to address traffic.
Topic 2
  • Design: Questions about cloud-native security policies for AWS, Azure, and Google Cloud appear in this topic. It also recommends connectivity models that ensure high availability, resiliency, SLAs, and reliability. Furthermore, the topic delves into connectivity models based on network architecture requirements. The topic further discusses factors including bandwidth, QoS, dedicated vs shared connections and multi-homing.
Topic 3
  • Architecture Models: In this topic different aspects of connectivity to cloud providers are discussed. It focuses on AWS, Azure, and Google Cloud. Moreover, the topic explains private connectivity to leading cloud providers and connectivity options for Software as a Service (SaaS) cloud providers.
Topic 4
  • Operation: The topic delves into diagnosis of IPsec-based secure cloud connectivity between an on-premises native Cloud endpoints and Cisco IOS XE router. It also explains the diagnosis of routing issues on Cisco IOS XE routers, and diagnosis of Cisco SD-WAN policy issues, focusing on all the traffic.
Topic 5
  • IPsec Cloud Connectivity: The configuration of IPsec-based secure cloud connectivity is one of the focal points of this topic. Additionally, it delves into configuration of IPsec-based secure cloud connectivity between an on-premises Cisco IOS XE router and native Azure, AWS, and Google Cloud endpoints. Lastly, the topic discusses configuration of routing on Cisco IOS XE routers.

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Cisco Designing and Implementing Cloud Connectivity Sample Questions (Q30-Q35):

NEW QUESTION # 30
Refer to the exhibit.

While troubleshooting an IPsec connection between a Cisco WAN edge router and an Amazon Web Services (AWS) endpoint, a network engineer observes that the security association status is active, but no traffic flows between the devices What is the problem?

  • A. wrong ISAKMP policy
  • B. identity mismatch
  • C. IKE version mismatch
  • D. wrong encryption

Answer: B

Explanation:
An identity mismatch occurs when the local and remote identities configured on the IPsec peers do not match.
This can prevent the establishment of an IPsec tunnel or cause traffic to be dropped by the IPsec policy. In this case, the network engineer should verify that the local and remote identities configured on the Cisco WAN edge router and the AWS endpoint match the values expected by each peer. The identities can be an IP address, a fully qualified domain name (FQDN), or a distinguished name (DN). The identities are exchanged during the IKE phase 1 negotiation and are used to authenticate the peers. If the identities do not match, the peers will reject the IKE proposal and the IPsec tunnel will not be established or will be torn down.
References :=
Configure IOS-XE Site-to-Site VPN Connection to Amazon Web Services, Topic: Troubleshooting Designing and Implementing Cloud Connectivity (ENCC) v1.0, Module 3: Implementing Cloud Connectivity, Lesson 2: Implementing Cisco SD-WAN Cloud OnRamp for IaaS, Topic:
Troubleshooting Cisco SD-WAN Cloud OnRamp for IaaS
Cisco IOS Security Configuration Guide, Release 15M&T, Chapter: Configuring IPsec Network Security, Topic: Configuring IPsec Identity and Peer Addressing


NEW QUESTION # 31
A company with multiple branch offices wants a connectivity model to meet its network architecture requirements. The company focuses on ensuring low latency and efficient routing for its critical business applications. Which connectivity model meets these requirements?

  • A. point-to-point topology using dedicated leased lines and static routing
  • B. hub-and-spoke topology with SD-WAN technology, using dynamic routing and OSPF as the routing protocol
  • C. fully meshed topology with SD-WAN technology, using dynamic routing and BGP as the routing protocol
  • D. star topology with internet-based VPN connections and static routing

Answer: C

Explanation:
A fully meshed topology with SD-WAN technology, using dynamic routing and BGP as the routing protocol, meets the requirements of the company because it provides the following benefits:
It allows direct and secure connectivity between any two branch offices, without the need for a central hub or intermediary devices12. This reduces the latency and improves the performance of the critical business applications.
It leverages SD-WAN technology to optimize the traffic flow and application quality of service (QoS) across the WAN13. SD-WAN can dynamically select the best path for each application based on the network conditions and policies13. SD-WAN can also provide redundancy, security, and visibility for the WAN13.
It uses dynamic routing and BGP as the routing protocol to exchange routing information and establish connectivity between the branch offices14. BGP is a scalable and flexible protocol that can support multiple address families, such as IPv4 and IPv6, and multiple routing policies, such as local preference and route filtering14. BGP can also enable seamless integration with the cloud service providers (CSPs) and internet service providers (ISPs)14.
References :=
1: Designing and Implementing Cloud Connectivity (ENCC, Track 1 of 5) (Cisco U. login required)
2: Cisco SD-WAN Design Guide


NEW QUESTION # 32
Which architecture model establishes internet-based connectivity between on-premises networks and AWS cloud resources?

  • A. That employs AWS Direct Connect for a dedicated network connection and uses private IP addresses tor secure communication.
  • B. That relies on AWS Elastic Load Balancing (ELB) for traffic distribution and uses SSL/TLS encryption for secure data transmission.
  • C. That uses Amazon CloudFrontfor caching and distributing content globally and uses HTTPS for secure data transfer.
  • D. That establishes an iPsec VPN tunnel with Internet Key Exchange (IKE) for secure key negotiation and encrypted data transmission

Answer: D

Explanation:
The architecture model that establishes internet-based connectivity between on-premises networks and AWS cloud resources is the one that establishes an iPsec VPN tunnel with Internet Key Exchange (IKE) for secure key negotiation and encrypted data transmission. This model is also known as the VPN CloudHub model12. It allows multiple remote sites to connect to the same virtual private gateway in AWS, creating a hub-and-spoke topology1. The VPN CloudHub model provides the following benefits12:
It enables secure communication between remote sites and AWS over the public internet, using encryption and authentication protocols such as IPsec and IKE.
It supports dynamic routing protocols such as BGP, which can automatically adjust the routing tables based on the availability and performance of the VPN tunnels.
It allows for redundancy and load balancing across multiple VPN tunnels, increasing the reliability and throughput of the connectivity.
It simplifies the management and configuration of the VPN connections, as each remote site only needs to establish one VPN tunnel to the virtual private gateway in AWS, rather than multiple tunnels to different VPCs or regions.
The other options are not correct because they do not establish internet-based connectivity between on-premises networks and AWS cloud resources. Option B relies on AWS Elastic Load Balancing (ELB) for traffic distribution and uses SSL/TLS encryption for secure data transmission. However, ELB is a service that distributes incoming traffic across multiple targets within a VPC, not across different networks3. Option C employs AWS Direct Connect for a dedicated network connection and uses private IP addresses for secure communication. However, AWS Direct Connect is a service that establishes a private connection between on-premises networks and AWS, bypassing the public internet4. Option D uses Amazon CloudFront for caching and distributing content globally and uses HTTPS for secure data transfer. However, Amazon CloudFront is a service that delivers static and dynamic web content to end users, not to on-premises networks5.
References:
1: Designing and Implementing Cloud Connectivity (ENCC, Track 1 of 5)
2: Cisco ASA Site-to-Site VPN
3: What Is Elastic Load Balancing?
4: What is AWS Direct Connect?


NEW QUESTION # 33
Which approach does a centralized internet gateway use to provide connectivity to SaaS applications?

  • A. A dedicated, private connection is established between the on-premises infrastructure and the SaaS provider data center using colocation services.
  • B. Internet traffic from the on-premises infrastructure is routed through a centralized gateway that provides access controls for SaaS applications.
  • C. VPN connections are used to provide secure access to SaaS applications from the on-premises infrastructure.
  • D. A cloud-based proxy server routes traffic from the on-premises infrastructure to the SaaS provider data center.

Answer: B

Explanation:
A centralized internet gateway is a network design that routes all internet-bound traffic from the on-premises infrastructure through a single point of egress, typically located at the data center or a regional hub1. This approach allows the enterprise to apply consistent security policies and access controls for SaaS applications, as well as optimize the bandwidth utilization and performance of the WAN links2. A centralized internet gateway can use various technologies to provide connectivity to SaaS applications, such as proxy servers, firewalls, web filters, and WAN optimizers3. However, a cloud-based proxy server (option A) is not a part of the centralized internet gateway, but rather a separate service that can be used to route traffic from the on-premises infrastructure to the SaaS provider data center4. VPN connections (option C) and dedicated, private connections (option D) are also not related to the centralized internet gateway, but rather alternative ways of providing secure and reliable access to SaaS applications from the on-premises infrastructure5. Therefore, the correct answer is option B, which describes the basic function of a centralized internet gateway. References := 1: Designing and Implementing Cloud Connectivity (ENCC) v1.0, Module 1:
Cloud Connectivity Overview, Lesson 1: Cloud Connectivity Concepts, Topic: Centralized Internet Gateway 2: Cloud OnRamp for SaaS, Cisco IOS XE Catalyst SD-WAN Release 17.3.1a and Later, Topic:
Centralized Internet Gateway 3: Architect and optimize your internet traffic with Azure routing preference, Microsoft Azure Blog, Topic: Routing via the premium Microsoft global network 4: What is SaaS? Softwareas a Service, Microsoft Azure, Topic: How SaaS works 5: How an application gateway works, Microsoft Learn, Topic: Application gateway components


NEW QUESTION # 34
An engineer must enable the OMP advertisement of BGP routes for a specific VRF instance on a Cisco IOS XE SD-WAN device. What should be configured after the global address-family ipv4 is configured?

  • A. Disable bgp advertisement.
  • B. Set the VRF-specific route advertisements.
  • C. Enter sdwan mode.
  • D. Enable bgp advertisement.

Answer: D

Explanation:
To enable the OMP advertisement of BGP routes for a specific VRF instance on a Cisco IOS XE SD-WAN device, the engineer must first configure the global address-family ipv4 and then enable bgp advertisement under the vrf definition. This will allow the device to advertise the BGP routes learned from the cloud provider to the OMP control plane, which will then distribute them to the other SD-WAN devices in the overlay network1 References := 1: Designing and Implementing Cloud Connectivity (ENCC) v1.0, Module 3: Implementing Cloud Connectivity, Lesson 3: Configuring IPsec VPN from Cisco IOS XE to AWS, Topic: Configuring BGP on the Cisco IOS XE Device, Page 3-24.


NEW QUESTION # 35
......

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Microsoft Power Platform Developer Sample Questions (Q285-Q290):

NEW QUESTION # 285
A company uses Dynamics 365 Sales and the Microsoft Online Services portal.
The multi-select OptionSet field data type is not supported in the portal.
You need to copy the selected field value to the text field.
How should you configure the Organization service request? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/dotnet/api/microsoft.xrm.sdk.messages.retrieveattributerequest.retrieveasifpublished?view=dynamics-general-ce-9
https://docs.microsoft.com/en-us/dotnet/api/microsoft.xrm.sdk.metadata.attributemetadata?view=dynamics-general-ce-9


NEW QUESTION # 286
You need to select a process to create each function.
Which process should you use? To answer, drag the appropriate processes to the correct functions. Each process may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://us.hitachi-solutions.com/blog/dynamics-365-workflow-vs-microsoft-flow/


NEW QUESTION # 287
You are creating a canvas app that realtors use to identify neighbors for properties that are for sale. The OnStart property includes the following code:

The app is running slower than expected.
You need to resolve the issue.
What should you do?

  • A.
  • B.
  • C.

Answer: C


NEW QUESTION # 288
A travel company plans to track the address of places their clients visit in an entity named Destination. Client information is captured as contact records. Client records include links to the places that clients visit.
The company must be able to link multiple rating records to the new address record.
You find a custom Rating entity that is incomplete.
You need to expand the Rating entity to include contact, address, and rating information in one place.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

  • A. Create a 1:N relationship between the Destination entity and the Rating entity.
  • B. Create a mapping for the Destination - Rating relationship.
  • C. Create a 1:N relationship between the Address system entity and the Rating entity.
  • D. Create a mapping for the Contact - Rating relationship.
  • E. Create a 1:N relationship between the Contact system entity and the Rating entity.
  • F. Create a 1:N relationship between the Contact system entity and the Address system entity named Destination.

Answer: B,C,F

Explanation:
Reference:
https://docs.microsoft.com/en-us/powerapps/maker/common-data-service/map-entity-fields


NEW QUESTION # 289
You need to address the user interface issues.
What should you do? To answer, drag the appropriate actions to the correct issues. Each action may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://support.microsoft.com/en-us/help/4552163/ribbon-troubleshooting-guide
https://neilparkhurst.com/2015/10/19/adding-buttons-to-ribbons/


NEW QUESTION # 290
......

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Amazon AWS Certified Solutions Architect - Associate (SAA-C02) Sample Questions (Q549-Q554):

NEW QUESTION # 549
MySQL installations default to port _____.

  • A. 0
  • B. 1
  • C. 2
  • D. 3

Answer: D


NEW QUESTION # 550
An application running on an Amazon EC2 instance needs to securely access tiles on an Amazon Elastic File System (Amazon I tile system. The EFS tiles are stored using encryption at rest. Which solution for accessing the tiles is MOST secure?

  • A. Store the encryption key in an Amazon S3 bucket and use 1AM roles to grant the EC2 instance access permission
  • B. Store the encryption key in the code of the application
  • C. Enable TLS when mounting Amazon EFS
  • D. Enable AWS Key Management Service (AWS KMS) when mounting Amazon EFS

Answer: B


NEW QUESTION # 551
A Company wants to design a web application on stateless web servers in an Auto Scaling group.
How can sensitive user data be handled to ensure a stateless design based on best practices.?

  • A. Use the ephemeral storage on the instances to store session state and Amazon DynamoDB for user data
  • B. Use cookies with the web server to store user data and session state on the client
  • C. Use Amazon SQS to make the workloads stateless and track execution history
  • D. Use Amazon DynamoDB for user data and cookies on the client to store session state

Answer: A


NEW QUESTION # 552
A company has a new mobile app. Anywhere in the world, users can see local news on topics they choose.
Users also can post photos and videos from inside the app.
Users access content often in the first minutes after the content is posted. New content quickly replaces older content, and then the older content disappears. The local nature of the news means that users consume 90% of the content within the AWS Region where it is uploaded.
Which solution will optimize the user experience by providing the LOWEST latency for content uploads?

  • A. Upload and store content in Amazon S3 in the Region that is closest to the user. Use multiple distributions of Amazon CloudFront.
  • B. Upload and store content in Amazon S3. Use S3 Transfer Acceleration for the uploads.
  • C. Upload content to Amazon EC2 instances in the Region that is closest to the user. Copy the data to Amazon S3.
  • D. Upload and store content in Amazon S3. Use Amazon CloudFront for the uploads.

Answer: B

Explanation:
The most suitable solution for optimizing the user experience by providing the lowest latency for content uploads is to upload and store content in Amazon S3 and use S3 Transfer Acceleration for the uploads. This solution will enable the company to leverage the AWS global network and edge locations to speed up the data transfer between the users and the S3 buckets.
Amazon S3 is a storage service that provides scalable, durable, and highly available object storage for any type of data. Amazon S3 allows users to store and retrieve data from anywhere on the web, and offers various features such as encryption, versioning, lifecycle management, and replication1.
S3 Transfer Acceleration is a feature of Amazon S3 that helps users transfer data to and from S3 buckets more quickly. S3 Transfer Acceleration works by using optimized network paths and Amazon's backbone network to accelerate data transfer speeds. Users can enable S3 Transfer Acceleration for their buckets and use a distinct URL to access them, such as <bucket>.s3-accelerate.amazonaws.com2.
The other options are not correct because they either do not provide the lowest latency or are not suitable for the use case. Uploading and storing content in Amazon S3 and using Amazon CloudFront for the uploads is not correct because this solution is not designed for optimizing uploads, but rather for optimizing downloads.
Amazon CloudFront is a content delivery network (CDN) that helps users distribute their content globally with low latency and high transfer speeds. CloudFront works by caching the content at edge locations around the world, so that users can access it quickly and easily from anywhere3. Uploading content to Amazon EC2 instances in the Region that is closest to the user and copying the data to Amazon S3 is not correct because this solution adds unnecessary complexity and cost to the process. Amazon EC2 is a computing service that provides scalable and secure virtual servers in the cloud. Users can launch, stop, or terminate EC2 instances as needed, and choose from various instance types, operating systems, and configurations4. Uploading and storing content in Amazon S3 in the Region that is closest to the user and using multiple distributions of Amazon CloudFront is not correct because this solution is not cost-effective or efficient for the use case. As mentioned above, Amazon CloudFront is a CDN that helps users distribute their content globally with low latency and high transfer speeds. However, creating multiple CloudFront distributions for each Region would incur additional charges and management overhead, and would not be necessary since 90% of the content is consumed within the same Region where it is uploaded3.
References:
What Is Amazon Simple Storage Service? - Amazon Simple Storage Service
Amazon S3 Transfer Acceleration - Amazon Simple Storage Service
What Is Amazon CloudFront? - Amazon CloudFront
What Is Amazon EC2? - Amazon Elastic Compute Cloud


NEW QUESTION # 553
Which of the following rules must be added to a mount target security group to access Amazon Elastic File System (EFS) from an on-premises server?

  • A. Configure an NFS proxy between Amazon EFS and the on-premises server to route traffic.
  • B. Permit secure traffic to the Kerberos port 88 from the on-premises server.
  • C. Set up a Point-To-Point Tunneling Protocol Server (PPTP) to allow secure connection.
  • D. Allow inbound traffic to the Network File System (NFS) port (2049) from the on-premises server.

Answer: D

Explanation:
By mounting an Amazon EFS file system on an on-premises server, on-premises data can be migrated into the AWS Cloud. Any one of the mount targets in your VPC can be used as long as the subnet of the mount target is reachable by using the AWS Direct Connect connection. To access Amazon EFS from an on- premises server, a rule must be added to the mount target security group to allow inbound traffic to the NFS port (2049) from the on-premises server.
Reference:
http://docs.aws.amazon.com/efs/latest/ug/how-it-works.html


NEW QUESTION # 554
......

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Adobe AD0-E717 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Identify when to call support *Yaml files and limitations (DIY vs Support tickets)
  • Identify the basics of category management and products management
Topic 2
  • Apply changes to existing product types and create new ones
  • Describe branching using the Adobe Commerce Cloud CLI tool
Topic 3
  • Describe cloud project files, permission, and structure
  • Identify ways to connect to cloud services? (My SQL, Redis, tunnel:info)
Topic 4
  • Identify Adobe commerce Cloud Plan capabilities
  • Demonstrate the ability to manage Indexes and customize price output
Topic 5
  • Recognize basic knowledge of cloud user management and onboarding UI
  • Explain how multi-source inventory impacts stock (program level)
Topic 6
  • Given a scenario, describe basic checkout modifications
  • Describe models, resource models, and collections

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Adobe Commerce Developer Professional Sample Questions (Q69-Q74):

NEW QUESTION # 69
Under which section should the soft dependency for a module be listed in app/code/<Vendor>/<Module>/composer.json file?

  • A. }
  • B. suggest*: {
  • C. }
    optional": {
  • D. }
    soft": {

Answer: D

Explanation:
Explanation
The soft dependency for a module should be listed in thesoftsection of
theapp/code/<Vendor>/<Module>/composer.jsonfile.
{
"name": "Vendor/Module",
"description": "This is a sample module",
"type": "magento2-module",
"version": "1.0.0",
"require": {
"php": "~7.3.0",
"magento/framework": "

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