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Download Amazon AWS Certified Solutions Architect - Associate (SAA-C03) Exam Exam Dumps
NEW QUESTION 50
A company plans to develop a custom messaging service that will also be used to train their AI for an automatic response feature which they plan to implement in the future. Based on their research and tests, the service can receive up to thousands of messages a day, and all of these data are to be sent to Amazon EMR for further processing. It is crucial that none of the messages are lost, no duplicates are produced, and that they are processed in EMR in the same order as their arrival.
Which of the following options can satisfy the given requirement?
- A. Create an Amazon Kinesis Data Stream to collect the messages.
- B. Create a pipeline using AWS Data Pipeline to handle the messages.
- C. Set up a default Amazon SQS queue to handle the messages.
- D. Set up an Amazon SNS Topic to handle the messages.
Answer: A
Explanation:
Two important requirements that the chosen AWS service should fulfill is that data should not go missing, is durable, and streams data in the sequence of arrival. Kinesis can do the job just fine because of its architecture. A Kinesis data stream is a set of shards that has a sequence of data records, and each data record has a sequence number that is assigned by Kinesis Data Streams. Kinesis can also easily handle the high volume of messages being sent to the service.
Amazon Kinesis Data Streams enables real-time processing of streaming big data. It provides ordering of records, as well as the ability to read and/or replay records in the same order to multiple Amazon Kinesis Applications. The Amazon Kinesis Client Library (KCL) delivers all records for a given partition key to the same record processor, making it easier to build multiple applications reading from the same Amazon Kinesis data stream (for example, to perform counting, aggregation, and filtering).
Setting up a default Amazon SQS queue to handle the messages is incorrect because although SQS is a valid messaging service, it is not suitable for scenarios where you need to process the data based on the order they were received. Take note that a default queue in SQS is just a standard queue and not a FIFO (First-In-First-Out) queue. In addition, SQS does not guarantee that no duplicates will be sent.
Setting up an Amazon SNS Topic to handle the messages is incorrect because SNS is a pub-sub messaging service in AWS. SNS might not be capable of handling such a large volume of messages being received and sent at a time. It does not also guarantee that the data will be transmitted in the same order they were received.
Creating a pipeline using AWS Data Pipeline to handle the messages is incorrect because this is primarily used as a cloud-based data workflow service that helps you process and move data between different AWS services and on-premises data sources. It is not suitable for collecting data from distributed sources such as users, IoT devices, or clickstreams. References:
https://docs.aws.amazon.com/streams/latest/dev/introduction.html
For additional information, read the When should I use Amazon Kinesis Data Streams, and when should I use Amazon SQS? section of the Kinesis Data Stream FAQ:
https://aws.amazon.com/kinesis/data-streams/faqs/
Check out this Amazon Kinesis Cheat Sheet:
https://tutorialsdojo.com/amazon-kinesis/
NEW QUESTION 51
A solutions architect is formulating a strategy for a startup that needs to transfer 50 TB of on- premises data to Amazon S3. The startup has a slow network transfer speed between its data center and AWS which causes a bottleneck for data migration.
Which of the following should the solutions architect implement?
- A. Deploy an AWS Migration Hub Discovery agent in the on-premises data center.
- B. Request an Import Job to Amazon S3 using a Snowball device in the AWS Snowball Console.
- C. Enable Amazon S3 Transfer Acceleration on the target S3 bucket.
- D. Integrate AWS Storage Gateway File Gateway with the on-premises data center.
Answer: B
Explanation:
AWS Snowball uses secure, rugged devices so you can bring AWS computing and storage capabilities to your edge environments, and transfer data into and out of AWS. The service delivers you Snowball Edge devices with storage and optional Amazon EC2 and AWS IOT Greengrass compute in shippable, hardened, secure cases. With AWS Snowball, you bring cloud capabilities for machine learning, data analytics, processing, and storage to your edge for migrations, short-term data collection, or even long- term deployments. AWS Snowball devices work with or without the internet, do not require a dedicated IT operator, and are designed to be used in remote environments.
Hence, the correct answer is: Request an Import Job to Amazon S3 using a Snowball device in the AWS Snowball Console.
The option that says: Deploy an AWS Migration Hub Discovery agent in the on-premises data center is incorrect. The AWS Migration Hub service is just a central service that provides a single location to track the progress of application migrations across multiple AWS and partner solutions.
The option that says: Enable Amazon S3 Transfer Acceleration on the target S3 bucket is incorrect because this S3 feature is not suitable for large-scale data migration. Enabling this feature won't always guarantee faster data transfer as it's only beneficial for long-distance transfer to and from your Amazon S3 buckets.
The option that says: Integrate AWS Storage Gateway File Gateway with the on-premises data center is incorrect because this service is mostly used for building hybrid cloud solutions where you still need on- premises access to unlimited cloud storage. Based on the scenario, this service is not the best option because you would still rely on the existing low bandwidth internet connection. References:
https://aws.amazon.com/snowball
https://aws.amazon.com/blogs/storage/making-it-even-simpler-to-create-and-manage-your-aws-snow-fa mily-jobs/ Check out this AWS Snowball Cheat Sheet:
https://tutorialsdojo.com/aws-snowball/
AWS Snow Family Overview:
https://www.youtube.com/watch?v=9Ar-51Ip53Q
NEW QUESTION 52
A company needs to deploy at least 2 EC2 instances to support the normal workloads of its application and automatically scale up to 6 EC2 instances to handle the peak load. The architecture must be highly available and fault-tolerant as it is processing mission-critical workloads.
As the Solutions Architect of the company, what should you do to meet the above requirement?
- A. Create an Auto Scaling group of EC2 instances and set the minimum capacity to 4 and the maximum capacity to 6. Deploy 2 instances in Availability Zone A and another 2 instances in Availability Zone B.
- B. Create an Auto Scaling group of EC2 instances and set the minimum capacity to 2 and the maximum capacity to 6. Use 2 Availability Zones and deploy 1 instance for each AZ.
- C. Create an Auto Scaling group of EC2 instances and set the minimum capacity to 2 and the maximum capacity to 4. Deploy 2 instances in Availability Zone A and 2 instances in Availability Zone B.
- D. Create an Auto Scaling group of EC2 instances and set the minimum capacity to 2 and the maximum capacity to 6. Deploy 4 instances in Availability Zone A.
Answer: A
Explanation:
Amazon EC2 Auto Scaling helps ensure that you have the correct number of Amazon EC2 instances available to handle the load for your application. You create collections of EC2 instances, called Auto Scaling groups. You can specify the minimum number of instances in each Auto Scaling group, and Amazon EC2 Auto Scaling ensures that your group never goes below this size. You can also specify the maximum number of instances in each Auto Scaling group, and Amazon EC2 Auto Scaling ensures that your group never goes above this size.
To achieve highly available and fault-tolerant architecture for your applications, you must deploy all your instances in different Availability Zones. This will help you isolate your resources if an outage occurs.
Take note that to achieve fault tolerance, you need to have redundant resources in place to avoid any system degradation in the event of a server fault or an Availability Zone outage. Having a fault-tolerant architecture entails an extra cost in running additional resources than what is usually needed. This is to ensure that the mission-critical workloads are processed.
Since the scenario requires at least 2 instances to handle regular traffic, you should have 2 instances running all the time even if an AZ outage occurred. You can use an Auto Scaling Group to automatically scale your compute resources across two or more Availability Zones. You have to specify the minimum capacity to 4 instances and the maximum capacity to 6 instances. If each AZ has 2 instances running, even if an AZ fails, your system will still run a minimum of 2 instances.
Hence, the correct answer in this scenario is: Create an Auto Scaling group of EC2 instances and set the minimum capacity to 4 and the maximum capacity to 6. Deploy 2 instances in Availability Zone A and another 2 instances in Availability Zone B.
The option that says: Create an Auto Scaling group of EC2 instances and set the minimum capacity to 2 and the maximum capacity to 6. Deploy 4 instances in Availability Zone A is incorrect because the instances are only deployed in a single Availability Zone. It cannot protect your applications and data from datacenter or AZ failures.
The option that says: Create an Auto Scaling group of EC2 instances and set the minimum capacity to 2 and the maximum capacity to 6. Use 2 Availability Zones and deploy 1 instance for each AZ is incorrect.
It is required to have 2 instances running all the time. If an AZ outage happened, ASG will launch a new instance on the unaffected AZ. This provisioning does not happen instantly, which means that for a certain period of time, there will only be 1 running instance left.
The option that says: Create an Auto Scaling group of EC2 instances and set the minimum capacity to 2 and the maximum capacity to 4. Deploy 2 instances in Availability Zone A and 2 instances in Availability Zone B is incorrect. Although this fulfills the requirement of at least 2 EC2 instances and high availability, the maximum capacity setting is wrong. It should be set to 6 to properly handle the peak load. If an AZ outage occurs and the system is at its peak load, the number of running instances in this setup will only be 4 instead of 6 and this will affect the performance of your application. References:
https://docs.aws.amazon.com/autoscaling/ec2/userguide/what-is-amazon-ec2-auto-scaling.html
https://docs.aws.amazon.com/documentdb/latest/developerguide/regions-and-azs.html Check out this AWS Auto Scaling Cheat Sheet:
https://tutorialsdojo.com/aws-auto-scaling/
NEW QUESTION 53
An application is loading hundreds of JSON documents into an Amazon S3 bucket every hour which is registered in AWS Lake Formation as a data catalog. The Data Analytics team uses Amazon Athena to run analyses on these data, but due to the volume, most queries take a long time to complete.
What change should be made to improve the query performance while ensuring data security?
- A. Compress the data into GZIP format before storing it in the S3 bucket. Apply an IAM policy with aws:SourceArn and aws:SourceAccount global condition context keys in Lake Formation that prevents cross-service confused deputy problems and other security issues.
- B. Apply minification on the data and implement the Lake Formation tag-based access control (LF- TBAC) authorization strategy to ensure security.
- C. Convert the JSON documents into CSV format. Provide fine-grained named resource access control to specific databases or tables in AWS Lake Formation.
- D. Transform the JSON data into Apache Parque format. Ensure that the user has an lakeformation:GetDataAccess IAM permission for underlying data access control.
Answer: D
Explanation:
Amazon Athena supports a wide variety of data formats like CSV, TSV, JSON, or Textfiles and also supports open-source columnar formats such as Apache ORC and Apache Parquet. Athena also supports compressed data in Snappy, Zlib, LZO, and GZIP formats. By compressing, partitioning, and using columnar formats you can improve performance and reduce your costs.
Parquet and ORC file formats both support predicate pushdown (also called predicate filtering). Parquet and ORC both have blocks of data that represent column values. Each block holds statistics for the block, such as max/min values. When a query is being executed, these statistics determine whether the block should be read or skipped.
Athena charges you by the amount of data scanned per query. You can save on costs and get better performance if you partition the data, compress data, or convert it to columnar formats such as Apache Parquet.
Apache Parquet is an open-source columnar storage format that is 2x faster to unload and takes up 6x less storage in Amazon S3 as compared to other text formats. One can COPY Apache Parquet and Apache ORC file formats from Amazon S3 to your Amazon Redshift cluster. Using AWS Glue, one can configure and run a job to transform CSV data to Parquet. Parquet is a columnar format that is well suited for AWS analytics services like Amazon Athena and Amazon Redshift Spectrum.
When an integrated AWS service requests access to data in an Amazon S3 location that is access- controlled by AWS Lake Formation, Lake Formation supplies temporary credentials to access the data.
To enable Lake Formation to control access to underlying data at an Amazon S3 location, you register that location with Lake Formation.
To enable Lake Formation principals to read and write underlying data with access controlled by Lake Formation permissions:
- The Amazon S3 locations that contain the data must be registered with Lake Formation.
- Principals who create Data Catalog tables that point to underlying data locations must have data location permissions.
- Principals who read and write underlying data must have Lake Formation data access permissions on the Data Catalog tables that point to the underlying data locations.
- Principals who read and write underlying data must have the lakeformation:GetDataAccess IAM permission.
Thus, the correct answer is: Transform the JSON data into Apache Parquet format. Ensure that the user has an lakeformation:GetDataAccess IAM permission for underlying data access control.
The option that says: Convert the JSON documents into CSV format. Provide fine-grained named resource access control to specific databases or tables in AWS Lake Formation is incorrect because Athena queries performed against row-based formats like CSV are slower than columnar file formats like Apache Parquet.
The option that says:Apply minification on the data and implement the Lake Formation tag-based access control (LF-TBAC) authorization strategy using IAM Tags to ensure security is incorrect. Although minifying the JSON file might reduce its overall file size, there won't be a significant difference in terms of querying performance. LF-TBAC is a type of an attribute-based access control (ABAC) that defines permissions based on certain attributes, such as tags in AWS. LF-TBAC uses LF-Tags to grant Lake Formation permissions and not regular IAM Tags.
The option that says: Compress the data into GZIP format before storing in the S3 bucket. Apply an IAM policy with aws:SourceArn and aws:SourceAccount global condition context keys in Lake Formation that prevents cross-service confused deputy problems and other security issues. is incorrect. Compressing the files prior to storing them in Amazon S3 will only save storage costs. As for query performance, it won't have much improvement. In addition, using an IAM Policy to prevent cross-service confused deputy issues is not warranted in this scenario. Having an lakeformation:GetDataAccess IAM permission for underlying data access control should suffice.
References: https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/
https://docs.aws.amazon.com/lake-formation/latest/dg/access-control-underlying-data.html
https://docs.aws.amazon.com/lake-formation/latest/dg/TBAC-overview.html Check out this Amazon Athena Cheat Sheet: https://tutorialsdojo.com/amazon-athena/
NEW QUESTION 54
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