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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q36-Q41):

NEW QUESTION # 36
A mobile gaming company wants to capture data from its gaming app. The company wants to make the data available to three internal consumers of the data. The data records are approximately 20 KB in size.
The company wants to achieve optimal throughput from each device that runs the gaming app. Additionally, the company wants to develop an application to process data streams. The stream-processing application must have dedicated throughput for each internal consumer.
Which solution will meet these requirements?

Answer: B

Explanation:
Problem Analysis:
Input Requirements: Gaming app generates approximately 20 KB data records, which must be ingested and made available to three internal consumers with dedicated throughput.
Key Requirements:
High throughput for ingestion from each device.
Dedicated processing bandwidth for each consumer.
Key Considerations:
Amazon Kinesis Data Streams supports high-throughput ingestion with PutRecords API for batch writes.
The Enhanced Fan-Out feature provides dedicated throughput to each consumer, avoiding bandwidth contention.
This solution avoids bottlenecks and ensures optimal throughput for the gaming application and consumers.
Solution Analysis:
Option A: Kinesis Data Streams + Enhanced Fan-Out
PutRecords API is designed for batch writes, improving ingestion performance.
Enhanced Fan-Out allows each consumer to process the stream independently with dedicated throughput.
Option B: Data Firehose + Dedicated Throughput Request
Firehose is not designed for real-time stream processing or fan-out. It delivers data to destinations like S3, Redshift, or OpenSearch, not multiple independent consumers.
Option C: Data Firehose + Enhanced Fan-Out
Firehose does not support enhanced fan-out. This option is invalid.
Option D: Kinesis Data Streams + EC2 Instances
Hosting stream-processing applications on EC2 increases operational overhead compared to native enhanced fan-out.
Final Recommendation:
Use Kinesis Data Streams with Enhanced Fan-Out for high-throughput ingestion and dedicated consumer bandwidth.
Kinesis Data Streams Enhanced Fan-Out
PutRecords API for Batch Writes


NEW QUESTION # 37
A technology company currently uses Amazon Kinesis Data Streams to collect log data in real time. The company wants to use Amazon Redshift for downstream real-time queries and to enrich the log data.
Which solution will ingest data into Amazon Redshift with the LEAST operational overhead?

Answer: A

Explanation:
The most efficient and low-operational-overhead solution for ingesting data into Amazon Redshift from Amazon Kinesis Data Streams is to use Amazon Redshift streaming ingestion. This feature allows Redshift to directly ingest streaming data from Kinesis Data Streams and process it in real-time.
Amazon Redshift Streaming Ingestion:
Redshift supports native streaming ingestion from Kinesis Data Streams, allowing real-time data to be queried using materialized views.
This solution reduces operational complexity because you don't need intermediary services like Amazon Kinesis Data Firehose or S3 for batch loading.
Reference:
Alternatives Considered:
A (Data Firehose to Redshift): This option is more suitable for batch processing but incurs additional operational overhead with the Firehose setup.
B (Firehose to S3): This involves an intermediate step, which adds complexity and delays the real-time requirement.
C (Managed Service for Apache Flink): This would work but introduces unnecessary complexity compared to Redshift's native streaming ingestion.
Amazon Redshift Streaming Ingestion from Kinesis
Materialized Views in Redshift


NEW QUESTION # 38
A healthcare company stores patient records in an on-premises MySQL database. The company creates an application to access the MySQL database. The company must enforce security protocols to protect the patient records. The company currently rotates database credentials every 30 days to minimize the risk of unauthorized access.
The company wants a solution that does not require the company to modify the application code for each credential rotation.
Which solution will meet this requirement with the least operational overhead?

Answer: A

Explanation:
The correct solution is C: AWS Secrets Manager.
* Why? AWS Secrets Manager is a fully managed service that helps you protect access to your applications, services, and IT resources without the upfront cost and complexity of managing your own hardware security module (HSM) infrastructure.
* It allows for automatic rotation of secrets without requiring changes to the application code, meeting the requirement of minimal operational overhead.
* Applications can securely retrieve credentials using Secrets Manager APIs, and the service integrates with AWS Identity and Access Management (IAM) to control access to secrets.
"You can use Secrets Manager to store credentials and to configure automatic rotation." Reference: AWS Certified Data Engineer - Associate Study Guide, Chapter 7 - Data Security and GovernanceAlso verified in AWS Documentation: https://docs.aws.amazon.com/secretsmanager/latest
/userguide/rotating-secrets.html


NEW QUESTION # 39
A company uses Amazon Redshift as a data warehouse solution. One of the datasets that the company stores in Amazon Redshift contains data for a vendor.
Recently, the vendor asked the company to transfer the vendor ' s data into the vendor ' s Amazon S3 bucket once each week.
Which solution will meet this requirement?

Answer: A

Explanation:
The Redshift UNLOAD command is specifically designed to export query results to Amazon S3, and AWS Glue can orchestrate this as part of a scheduled job. This is the cleanest and most appropriate approach for recurring weekly data transfers:
"Use the Redshift UNLOAD command with AWS Glue to export data to Amazon S3. This pattern enables routine exports of selected data to external locations."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This avoids complexities of Redshift Spectrum or unsupported use of COPY commands in Lambda.


NEW QUESTION # 40
A company is planning to use a provisioned Amazon EMR cluster that runs Apache Spark jobs to perform big data analysis. The company requires high reliability. A big data team must follow best practices for running cost-optimized and long-running workloads on Amazon EMR. The team must find a solution that will maintain the company's current level of performance.
Which combination of resources will meet these requirements MOST cost-effectively? (Choose two.)

Answer: B,D

Explanation:
The best combination of resources to meet the requirements of high reliability, cost-optimization, and performance for running Apache Spark jobs on Amazon EMR is to use Amazon S3 as a persistent data store and Graviton instances for core nodes and task nodes.
Amazon S3 is a highly durable, scalable, and secure object storage service that can store any amount of data for a variety of use cases, including big data analytics1. Amazon S3 is a better choice than HDFS as a persistent data store for Amazon EMR, as it decouples the storage from the compute layer, allowing for more flexibility and cost-efficiency. Amazon S3 also supports data encryption, versioning, lifecycle management, and cross-region replication1. Amazon EMR integrates seamlessly with Amazon S3, using EMR File System (EMRFS) to access data stored in Amazon S3 buckets2. EMRFS also supports consistent view, which enables Amazon EMR to provide read-after-write consistency for Amazon S3 objects that are accessed through EMRFS2.
Graviton instances are powered by Arm-based AWS Graviton2 processors that deliver up to 40% better price performance over comparable current generation x86-based instances3. Graviton instances are ideal for running workloads that are CPU-bound, memory-bound, or network-bound, such as big data analytics, web servers, and open-source databases3. Graviton instances are compatible with Amazon EMR, and can beused for both core nodes and task nodes. Core nodes are responsible for running the data processing frameworks, such as Apache Spark, and storing data in HDFS or the local file system. Task nodes are optional nodes that can be added to a cluster to increase the processing power and throughput. By using Graviton instances for both core nodes and task nodes, you can achieve higher performance and lower cost than using x86-based instances.
Using Spot Instances for all primary nodes is not a good option, as it can compromise the reliability and availability of the cluster. Spot Instances are spare EC2 instances that are available at up to 90% discount compared to On-Demand prices, but they can be interrupted by EC2 with a two-minute notice when EC2 needs the capacity back. Primary nodes are the nodes that run the cluster software, such as Hadoop, Spark, Hive, and Hue, and are essential for the cluster operation. If a primary node is interrupted by EC2, the cluster will fail or become unstable. Therefore, it is recommended to use On-Demand Instances or Reserved Instances for primary nodes, and use Spot Instances only for task nodes that can tolerate interruptions. References:
Amazon S3 - Cloud Object Storage
EMR File System (EMRFS)
AWS Graviton2 Processor-Powered Amazon EC2 Instances
[Plan and Configure EC2 Instances]
[Amazon EC2 Spot Instances]
[Best Practices for Amazon EMR]


NEW QUESTION # 41
......

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