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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Data-Engineer-Associate Prüfungsfragen mit Lösungen (Q60-Q65):
60. Frage
A company wants to migrate a data warehouse from Teradata to Amazon Redshift. Which solution will meet this requirement with the LEAST operational effort?
Antwort: C
61. Frage
A car sales company maintains data about cars that are listed for sale in an are a. The company receives data about new car listings from vendors who upload the data daily as compressed files into Amazon S3. The compressed files are up to 5 KB in size. The company wants to see the most up-to-date listings as soon as the data is uploaded to Amazon S3.
A data engineer must automate and orchestrate the data processing workflow of the listings to feed a dashboard. The data engineer must also provide the ability to perform one-time queries and analytical reporting. The query solution must be scalable.
Which solution will meet these requirements MOST cost-effectively?
Antwort: A
Begründung:
For processing the incoming car listings in a cost-effective, scalable, and automated way, the ideal approach involves using AWS Glue for data processing, AWS Lambda with S3 Event Notifications for orchestration, Amazon Athena for one-time queries and analytical reporting, and Amazon QuickSight for visualization on the dashboard. Let's break this down:
AWS Glue: This is a fully managed ETL (Extract, Transform, Load) service that automatically processes the incoming data files. Glue is serverless and supports diverse data sources, including Amazon S3 and Redshift.
AWS Lambda and S3 Event Notifications: Using Lambda and S3 Event Notifications allows near real-time triggering of processing workflows as soon as new data is uploaded into S3. This approach is event-driven, ensuring that the listings are processed as soon as they are uploaded, reducing the latency for data processing.
Amazon Athena: A serverless, pay-per-query service that allows interactive queries directly against data in S3 using standard SQL. It is ideal for the requirement of one-time queries and analytical reporting without the need for provisioning or managing servers.
Amazon QuickSight: A business intelligence tool that integrates with a wide range of AWS data sources, including Athena, and is used for creating interactive dashboards. It scales well and provides real-time insights for the car listings.
This solution (Option D) is the most cost-effective, because both Glue and Athena are serverless and priced based on usage, reducing costs when compared to provisioning EMR clusters in the other options. Moreover, using Lambda for orchestration is more cost-effective than AWS Step Functions due to its lightweight nature.
Reference:
AWS Glue Documentation
Amazon Athena Documentation
Amazon QuickSight Documentation
S3 Event Notifications and Lambda
62. Frage
A company plans to use Amazon Kinesis Data Firehose to store data in Amazon S3. The source data consists of 2 MB csv files. The company must convert the .csv files to JSON format. The company must store the files in Apache Parquet format.
Which solution will meet these requirements with the LEAST development effort?
Antwort: C
Begründung:
The company wants to use Amazon Kinesis Data Firehose to transform CSV files into JSON format and store the files in Apache Parquet format with the least development effort.
Option B: Use Kinesis Data Firehose to convert the CSV files to JSON and to store the files in Parquet format.
Kinesis Data Firehose supports data format conversion natively, including converting incoming CSV data to JSON format and storing the resulting files in Parquet format in Amazon S3. This solution requires the least development effort because it uses built-in transformation features of Kinesis Data Firehose.
Other options (A, C, D) involve invoking AWS Lambda functions, which would introduce additional complexity and development effort compared to Kinesis Data Firehose's native format conversion capabilities.
Reference:
Amazon Kinesis Data Firehose Documentation
63. Frage
A company is developing an application that runs on Amazon EC2 instances. Currently, the data that the application generates is temporary. However, the company needs to persist the data, even if the EC2 instances are terminated.
A data engineer must launch new EC2 instances from an Amazon Machine Image (AMI) and configure the instances to preserve the data.
Which solution will meet this requirement?
Antwort: C
Begründung:
Amazon EC2 instances can use two types of storage volumes: instance store volumes and Amazon EBS volumes. Instance store volumes are ephemeral, meaning they are only attached to the instance for the duration of its life cycle. If the instance is stopped, terminated, or fails, the data on the instance store volume is lost.
Amazon EBS volumes are persistent, meaning they can be detached from the instance and attached to another instance, and the data on the volume is preserved. To meet the requirement of persisting the data even if the EC2 instances are terminated, the data engineer must use Amazon EBS volumes to store the application data.
The solution is to launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume, which is the default option for most AMIs. Then, the data engineer must attach an Amazon EBS volume to each instance and configure the application to write the data to the EBS volume. This way, the data will be saved on the EBS volume and can be accessed by another instance if needed. The data engineer can apply the default settings to the EC2 instances, as there is no need to modify the instance type, security group, or IAM role for this solution. The other options are either not feasible or not optimal. Launching new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data (option A) or by using an AMI that is backed by a root Amazon EBS volume that contains the application data (option B) would not work, as the data on the AMI would be outdated and overwritten by the new instances. Attaching an additional EC2 instance store volume to contain the application data (option D)would not work, as the data on the instance store volume would be lost if the instance is terminated. References:
Amazon EC2 Instance Store
Amazon EBS Volumes
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.1: Amazon EC2
64. Frage
A banking company uses an application to collect large volumes of transactional data. The company uses Amazon Kinesis Data Streams for real-time analytics. The company's application uses the PutRecord action to send data to Kinesis Data Streams.
A data engineer has observed network outages during certain times of day. The data engineer wants to configure exactly-once delivery for the entire processing pipeline.
Which solution will meet this requirement?
Antwort: B
Begründung:
For exactly-once delivery and processing in Amazon Kinesis Data Streams, the best approach is to design the application so that it handlesidempotency. By embedding aunique IDin each record, the application can identify and remove duplicate records during processing.
* Exactly-Once Processing:
* Kinesis Data Streams does not natively support exactly-once processing. Therefore,idempotency should be designed into the application, ensuring that each record has a unique identifier so that the same event is processed only once, even if it is ingested multiple times.
* This pattern is widely used for achieving exactly-once semantics in distributed systems.
Reference:Building Idempotent Applications with Kinesis
Alternatives Considered:
B (Checkpoint configuration): While updating the checkpoint configuration can help with some aspects of duplicate processing, it is not a full solution for exactly-once delivery.
C (Design data source): Ensuring events are not ingested multiple times is ideal, but network outages can make this difficult, and it doesn't guarantee exactly-once delivery.
D (Using EMR): While using EMR with Flink or Spark could work, it introduces unnecessary complexity compared to handling idempotency at the application level.
References:
Amazon Kinesis Best Practices for Exactly-Once Processing
Achieving Idempotency with Amazon Kinesis
65. Frage
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