BTW, DOWNLOAD part of Fast2test Data-Engineer-Associate dumps from Cloud Storage: https://drive.google.com/open?id=1k5N6Vc3YWtMB7zFe4lnM19wPZAcrUp9y
With the development of information and communications technology, we are now living in a globalized world. Data-Engineer-Associate information technology learning is correspondingly popular all over the world. Modern technology has changed the way how we live and work. In current situation, enterprises and institutions require their candidates not only to have great education background, but also acquired professional Data-Engineer-Associate Certification. Considering that, it is no doubt that an appropriate certification would help candidates achieve higher salaries and get promotion.
| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion and Transformation | 34% | - Implement data quality and validation - Transform and enrich data
|
| Data Store Management | 26% | - Design and implement data storage solutions
- Optimize storage performance and cost |
| Data Security and Governance | 18% | - Enforce compliance and data governance
- Encrypt data at rest and in transit |
| Data Operations and Support | 22% | - Monitor and troubleshoot data pipelines
- Automate operational tasks - Ensure reliability and scalability |
>> Data-Engineer-Associate Exam Pass Guide <<
Fast2test is one of the leading platforms that has been helping AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) exam candidates for many years. Over this long time period we have helped AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) exam candidates in their preparation. They got help from Fast2test Amazon Data-Engineer-Associate Practice Questions and easily got success in the final Amazon Data-Engineer-Associate certification exam. You can also trust Amazon Data-Engineer-Associate exam dumps and start preparation with complete peace of mind and satisfaction.
NEW QUESTION # 208
A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions.
The data engineer requires a less manual way to update the Lambda functions.
Which solution will meet this requirement?
Answer: B
Explanation:
Lambda layers are a way to share code and dependencies across multiple Lambda functions. By packaging the custom Python scripts into Lambda layers, the data engineer can update the scripts in one place and have them automatically applied to all the Lambda functions that use the layer. This reduces the manual effort and ensures consistency across the Lambda functions. The other options are either not feasible or not efficient. Storing a pointer to the custom Python scripts in the execution context object or in environment variables would require the Lambda functions to download the scripts from Amazon S3 every time they are invoked, which would increase latency and cost. Assigning the same alias to each Lambda function would not help with updating the Python scripts, as the alias only points to a specific version of the Lambda function code. Reference:
AWS Lambda layers
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.4: AWS Lambda
NEW QUESTION # 209
A car sales company maintains data about cars that are listed for sale in an area. 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?
Answer: C
Explanation:
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.
References:
* AWS Glue Documentation
* Amazon Athena Documentation
* Amazon QuickSight Documentation
* S3 Event Notifications and Lambda
NEW QUESTION # 210
A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.
The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.
Which solution will meet these requirements with the LOWEST latency?
Answer: C
Explanation:
This solution will meet the requirements with the lowest latency because it uses Amazon Managed Service for Apache Flink to process the sensor data in real time and write it to Amazon Timestream, a fast, scalable, and serverless time series database. Amazon Timestream is optimized for storing and analyzing time series data, such as sensor data, and can handle trillions of events per day with millisecond latency. By using AmazonTimestream as a source, you can create an Amazon QuickSight dashboard that displays a real-time view of operational efficiency on a large screen in the manufacturing facility. Amazon QuickSight is a fully managed business intelligence service that can connect to various data sources, including Amazon Timestream, and provide interactive visualizations and insights123.
The other options are not optimal for the following reasons:
A: Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard. This option is similar to option C, but it uses Grafana instead of Amazon QuickSight to create the dashboard.
Grafana is an open source visualization tool that can also connect to Amazon Timestream, but it requires additional steps to set up and configure, such as deploying a Grafana server on Amazon EC2, installing the Amazon Timestream plugin, and creating an IAM role for Grafana to access Timestream. These steps can increase the latency and complexity of the solution.
B: Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard. This option is not suitable for displaying a real-time view of operational efficiency, as it introduces unnecessary delays and costs in the data pipeline. First, the sensor data is written to an S3 bucket by Amazon Kinesis Data Firehose, which can have a buffering interval of up to 900 seconds. Then, the S3 bucket sends a notification to a Lambda function, which can incur additional invocation and execution time. Finally, the Lambda function publishes the data to Amazon Aurora, a relational database that is not optimized for time series data and can have higher storage and performance costs than Amazon Timestream .
D: Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
This option is also not suitable for displaying a real-time view of operational efficiency, as it uses AWS Glue bookmarks to read sensor data from the S3 bucket. AWS Glue bookmarks are a feature that helps AWS Glue jobs and crawlers keep track of the data that has already been processed, so that they can resume from where they left off. However, AWS Glue jobs and crawlers are not designed for real-time data processing, as they can have a minimum frequency of 5 minutes and a variable start-up time.
Moreover, this option also uses Grafana instead of Amazon QuickSight to create the dashboard, which can increase the latency and complexity of the solution .
References:
1: Amazon Managed Streaming for Apache Flink
2: Amazon Timestream
3: Amazon QuickSight
4: Analyze data in Amazon Timestream using Grafana
5: Amazon Kinesis Data Firehose
6: Amazon Aurora
7: AWS Glue Bookmarks
8: AWS Glue Job and Crawler Scheduling
NEW QUESTION # 211
A company stores historical customer data in an Amazon Redshift table. A column named Email contains null entries and values that are not email addresses. The quality of the Email column is critical for multiple downstream processes. A data engineer must create an AWS Glue Data Quality rule that fails when the percentage of valid email addresses in the Email column is less than 90%.
Which component of an AWS Glue Data Quality rule will meet these requirements?
Answer: A
Explanation:
Option C is correct because the requirement is to verify that at least 90% of values in the Email column are valid email addresses. In AWS Glue Data Quality, rules are written in DQDL, and ColumnValues is the rule component used to validate whether column values satisfy a condition or pattern. AWS Glue Data Quality documentation explains that DQDL is the language used to define rules, and it specifically notes that ColumnValues rules can evaluate column content and that NULL values do not pass during comparisons.
That is important here because the problem explicitly says the column contains null entries and invalid email values. A threshold of > 0.9 means the rule passes only when more than 90% of rows satisfy the email- validation condition.
Option A is incorrect because Uniqueness checks the percentage of values that are unique, not whether values are valid email addresses. AWS documents Uniqueness as measuring how many values occur exactly once, which is unrelated to email-format validity. Option D has the same issue because UniqueValueRatio addresses uniqueness characteristics, not format validation. Option B uses the right rule family but the wrong threshold because > 0.1 would require only 10% valid values. Therefore, ColumnValues with a threshold greater than 0.9 is the correct choice.
NEW QUESTION # 212
A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline.
Which AWS service or feature will meet these requirements MOST cost-effectively?
Answer: D
Explanation:
AWS Glue workflows are a cost-effective way to orchestrate complex ETL jobs that involve multiple crawlers, jobs, and triggers. AWS Glue workflows allow you to visually monitor the progress and dependencies of your ETL tasks, and automatically handle errors and retries. AWS Glue workflows also integrate with other AWS services, such as Amazon S3, Amazon Redshift, and AWS Lambda, among others, enabling you to leverage these services for your data processing workflows. AWS Glue workflows are serverless, meaning you only pay for the resources you use, and you don't have to manage any infrastructure.
AWS Step Functions, AWS Glue Studio, and Amazon MWAA are also possible options for orchestrating ETL pipelines, but they have some drawbacks compared to AWS Glue workflows. AWS Step Functions is a serverless function orchestrator that can handle different types of data processing, such as real-time, batch, and stream processing. However, AWS Step Functions requires you to write code to define your state machines, which can be complex and error-prone. AWS Step Functions also charges you for every state transition, which can add up quickly for large-scale ETL pipelines.
AWS Glue Studio is a graphical interface that allows you to create and run AWS Glue ETL jobs without writing code. AWS Glue Studio simplifies the process of building, debugging, and monitoring your ETL jobs, and provides a range of pre-built transformations and connectors. However, AWS Glue Studio does not support workflows, meaning you cannot orchestrate multiple ETL jobs or crawlers with dependencies and triggers. AWS Glue Studio also does not support streaming data sources or targets, which limits its use cases for real-time data processing.
Amazon MWAA is a fully managed service that makes it easy to run open-source versions of Apache Airflow on AWS and build workflows to run your ETL jobs and data pipelines. Amazon MWAA provides a familiar and flexible environment for data engineers who are familiar with Apache Airflow, and integrates with a range of AWS services such as Amazon EMR, AWS Glue, and AWS Step Functions. However, Amazon MWAA is not serverless, meaning you have to provision and pay for the resources you need, regardless of your usage. Amazon MWAA also requires you to write code to define your DAGs, which can be challenging and time-consuming for complex ETL pipelines. References:
* AWS Glue Workflows
* AWS Step Functions
* AWS Glue Studio
* Amazon MWAA
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
NEW QUESTION # 213
......
After undergoing a drastic change over these years, our Data-Engineer-Associate actual exam have been doing perfect job in coping with the exam. Up to now our Data-Engineer-Associate practice materials account for 60 percent of market share in this line for their efficiency and accuracy when dealing with the exam. With the best reputation in the market our Data-Engineer-Associate Training Materials can help you ward off all unnecessary and useless materials and spend all your limited time on practicing most helpful questions.
Reliable Data-Engineer-Associate Test Labs: https://www.fast2test.com/Data-Engineer-Associate-premium-file.html
P.S. Free 2026 Amazon Data-Engineer-Associate dumps are available on Google Drive shared by Fast2test: https://drive.google.com/open?id=1k5N6Vc3YWtMB7zFe4lnM19wPZAcrUp9y