BONUS!!! Download part of BootcampPDF Data-Engineer-Associate dumps for free: https://drive.google.com/open?id=12h7JD3t4C9qb5ymtl9Dqrtexdn7j_usX
By propagating all necessary points of knowledge available for you, our Data-Engineer-Associate practice materials helped over 98 percent of former exam candidates gained successful outcomes as a result. Our Data-Engineer-Associate practice materials have accuracy rate in proximity to 98 and over percent for your reference. Up to now we classify them as three versions. They are pdf, software and the most convenient one app. Each of them has their respective feature and advantage including new information that you need to know to pass the test.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion and Transformation | 34% | - Implement data quality and validation - Ingest data from various sources
|
| Topic 2: Data Operations and Support | 22% | - Automate operational tasks - Backup, restore, and disaster recovery - Monitor and troubleshoot data pipelines
|
| Topic 3: Data Security and Governance | 18% | - Implement access control and authentication
- Enforce compliance and data governance
|
| Topic 4: Data Store Management | 26% | - Design and implement data storage solutions
- Optimize storage performance and cost |
>> Latest Data-Engineer-Associate Exam Simulator <<
Review the products offered by us by downloading their free demos and compare them with the Data-Engineer-Associate study material offered in online course free and vendors' files. You will find our products the better than our competitors such as exam collection and others. The excellent quality of our Data-Engineer-Associate content, their relevance with the actual exam needs and their interactive and simple format will prove them superior and quite pertinent to your needs and requirements.
NEW QUESTION # 242
A data engineer needs to optimize the performance of a data pipeline that handles retail orders. Data about the orders is ingested daily into an Amazon S3 bucket.
The data engineer runs queries once each week to extract metrics from the orders data based on the order date for multiple date ranges. The data engineer needs an optimization solution that ensures the query performance will not degrade when the volume of data increases.
Answer: D
Explanation:
For query workloads on S3 data that depend on date-based filters, partitioning by order date optimizes performance and cost because Athena reads only the relevant partitions.
Athena scales automatically and doesn't degrade with increasing data size when partitions are managed efficiently.
"Partitioning data in Amazon S3 based on query predicates such as order date improves Athena query performance and reduces scanned data volume."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This is the most cost-effective and scalable option for date-based queries.
NEW QUESTION # 243
A media company uses software as a service (SaaS) applications to gather data by using third-party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data.
Which AWS service or feature will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications and AWS services like Amazon S3 and Amazon Redshift. Amazon AppFlow supports many SaaS applications as data sources and targets, and allows you to configure data flows with a few clicks. Amazon AppFlow also provides features such as data transformation, filtering, validation, and encryption to prepare and protect your data. Amazon AppFlow meets the requirements of the media company with the least operational overhead, as it eliminates the need to write code, manage infrastructure, or monitor data pipelines. Reference:
Amazon AppFlow
Amazon AppFlow | SaaS Integrations List
Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions
NEW QUESTION # 244
A company currently uses a provisioned Amazon EMR cluster that includes general purpose Amazon EC2 instances. The EMR cluster uses EMR managed scaling between one to five task nodes for the company's long-running Apache Spark extract, transform, and load (ETL) job. The company runs the ETL job every day.
When the company runs the ETL job, the EMR cluster quickly scales up to five nodes. The EMR cluster often reaches maximum CPU usage, but the memory usage remains under 30%.
The company wants to modify the EMR cluster configuration to reduce the EMR costs to run the daily ETL job.
Which solution will meet these requirements MOST cost-effectively?
Answer: D
Explanation:
The company's Apache Spark ETL job on Amazon EMR uses high CPU but low memory, meaning that compute-optimized EC2 instances would be the most cost-effective choice. These instances are designed for high-performance compute applications, where CPU usage is high, but memory needs are minimal, which is exactly the case here.
Compute Optimized Instances:
Compute-optimized instances, such as the C5 series, provide a higher ratio of CPU to memory, which is more suitable for jobs with high CPU usage and relatively low memory consumption.
Switching from general-purpose EC2 instances to compute-optimized instances can reduce costs while improving performance, as these instances are optimized for workloads like Spark jobs that perform a lot of computation.
Reference:
Managed Scaling: The EMR cluster's scaling is currently managed between 1 and 5 nodes, so changing the instance type will leverage the current scaling strategy but optimize it for the workload.
Alternatives Considered:
A (Increase task nodes to 10): Increasing the number of task nodes would increase costs without necessarily improving performance. Since memory usage is low, the bottleneck is more likely the CPU, which compute-optimized instances can handle better.
B (Memory optimized instances): Memory-optimized instances are not suitable since the current job is CPU-bound, and memory usage remains low (under 30%).
D (Reduce scaling cooldown): This could marginally improve scaling speed but does not address the need for cost optimization and improved CPU performance.
Amazon EMR Cluster Optimization
Compute Optimized EC2 Instances
NEW QUESTION # 245
A media company wants to build a real-time analytics pipeline to process customer activity events across the company ' s website and mobile app. The company wants to build a solution to ingest millions of events with minimum latency. The solution must be scalable and durable enough so that no data is lost.
Which solution will meet these requirements in the MOST cost-effective way?
Answer: D
Explanation:
Option A is correct because Amazon Kinesis Data Streams is designed to collect and process large streams of data records in real time and is optimized for rapid, continuous data intake and aggregation. AWS states that Kinesis Data Streams is a fully managed, serverless data streaming service for ingesting streaming data at any scale and highlights event sources such as clickstream and application activity data. AWS also notes that Kinesis can pair with AWS Lambda for low-latency event processing. This directly matches the requirement to ingest millions of customer activity events with minimum latency in a scalable and durable way.
Option B is batch processing every 10 minutes, so it does not satisfy the real-time requirement. Option C depends on files landing in S3 first, which is also not the lowest-latency ingest path. Option D can support streaming, but a managed Kafka cluster with self-managed consumers introduces more operational overhead and is generally less cost-effective here than a native Kinesis + Lambda pipeline for an AWS-centric event- ingestion workload. Therefore, Kinesis Data Streams + Lambda + Redshift is the best answer.
NEW QUESTION # 246
A company stores time-series data that is collected from streaming services in an Amazon S3 bucket. The company must ensure that only workloads that are deployed within the company's VPC can access the data.
Which solution will meet this requirement?
Answer: B
Explanation:
The best practice to restrict Amazon S3 access to specific VPCs is to use a bucket policy with a StringEquals or StringLike condition on aws:SourceVpc. This ensures only requests from a specified VPC are allowed.
* IAM policies (option C) control who can access the resource but are not suitable alone to restrict by VPC.
* Security Groups and NACLs (options B and D) do not apply to Amazon S3 because it is a global service and not VPC-bound.
"You can restrict access to your S3 bucket so that only requests coming from a specific VPC endpoint are allowed." Source: AWS Documentation - Amazon S3 Bucket Policies for VPC Endpoints
NEW QUESTION # 247
......
Our Amazon Data-Engineer-Associate test braindump materials is popular based on that too. As we all know the passing rate for exams is low, the wise choice for candidates will select valid Amazon Data-Engineer-Associate test braindump materials to make you pass exam surely and fast. Our Amazon Data-Engineer-Associate test simulations will help you twice the result with half the effort.
New Data-Engineer-Associate Test Syllabus: https://www.bootcamppdf.com/Data-Engineer-Associate_exam-dumps.html
P.S. Free & New Data-Engineer-Associate dumps are available on Google Drive shared by BootcampPDF: https://drive.google.com/open?id=12h7JD3t4C9qb5ymtl9Dqrtexdn7j_usX