P.S. Free 2026 Amazon Data-Engineer-Associate dumps are available on Google Drive shared by Exams4Collection: https://drive.google.com/open?id=1Nk5blJCDmjOxmvm5SECpWCJefo8vO_dn
Our company is no exception, and you can be assured to buy our Data-Engineer-Associate exam prep. Our company has been focusing on the protection of customer privacy all the time. We can make sure that we must protect the privacy of all customers who have bought our Data-Engineer-Associate test questions. If you decide to use our Data-Engineer-Associate test torrent, we are assured that we recognize the importance of protecting your privacy and safeguarding the confidentiality of the information you provide to us. We hope you will use our Data-Engineer-Associate Exam Prep with a happy mood, and you don’t need to worry about your information will be leaked out.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion and Transformation | 34% | - Ingest data from various sources
|
| Topic 2: Data Security and Governance | 18% | - Enforce compliance and data governance
- Protect sensitive data - Implement access control and authentication
|
| Topic 3: Data Operations and Support | 22% | - Automate operational tasks - Ensure reliability and scalability - Monitor and troubleshoot data pipelines
|
| Topic 4: Data Store Management | 26% | - Optimize storage performance and cost - Design and implement data storage solutions
|
>> Data-Engineer-Associate Exam Fees <<
Among all substantial practice materials with similar themes, our Data-Engineer-Associate practice materials win a majority of credibility for promising customers who are willing to make progress in this line. With excellent quality at attractive price, our Data-Engineer-Associate practice materials get high demand of orders in this fierce market with passing rate up to 98 to 100 percent all these years. We shall highly appreciate your acceptance of our Data-Engineer-Associate practice materials and your decision will lead you to bright future with highly useful certificates. We have handled professional Data-Engineer-Associate practice materials for over ten years. Our experts have many years’ experience in this particular line of business, together with meticulous and professional attitude towards jobs.
NEW QUESTION # 271
A company has multiple applications that use datasets that are stored in an Amazon S3 bucket. The company has an ecommerce application that generates a dataset that contains personally identifiable information (PII).
The company has an internal analytics application that does not require access to the PII.
To comply with regulations, the company must not share PII unnecessarily. A data engineer needs to implement a solution that with redact PII dynamically, based on the needs of each application that accesses the dataset.
Which solution will meet the requirements with the LEAST operational overhead?
Answer: D
Explanation:
Option B is the best solution to meet the requirements with the least operational overhead because S3 Object Lambda is a feature that allows you to add your own code to process data retrieved from S3 before returning it to an application. S3 Object Lambda works with S3 GET requests and can modify both the object metadata and the object data. By using S3 Object Lambda, you can implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data. This way, you can avoid creating and maintaining multiple copies of the dataset with different levels of redaction.
Option A is not a good solution because it involves creating and managing multiple copies of the dataset with different levels of redaction for each application. This option adds complexity and storage cost to the data protection process and requires additional resources and configuration. Moreover, S3 bucket policies cannot enforce fine-grained data access control at the row and column level, so they are not sufficient to redact PII.
Option C is not a good solution because it involves using AWS Glue to transform the data for each application. AWS Glue is a fully managed service that can extract, transform, and load (ETL) data from various sources to various destinations, including S3. AWS Glue can also convert data to different formats, such as Parquet, which is a columnar storage format that is optimized for analytics. However, in this scenario, using AWS Glue to redact PII is not the best option because it requires creating and maintaining multiple copies of the dataset with different levels of redaction for each application. This option also adds extra time and cost to the data protection process and requires additional resources and configuration.
Option D is not a good solution because it involves creating and configuring an API Gateway endpoint that has custom authorizers. API Gateway is a service that allows you to create, publish, maintain, monitor, and secure APIs at any scale. API Gateway can also integrate with other AWS services, such as Lambda, to provide custom logic for processing requests. However, in this scenario, using API Gateway to redact PII is not the best option because it requires writing and maintaining custom code and configuration for the API endpoint, the custom authorizers, and the REST API call. This option also adds complexity and latency to the data protection process and requires additional resources and configuration.
References:
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
* Introducing Amazon S3 Object Lambda - Use Your Code to Process Data as It Is Being Retrieved from S3
* Using Bucket Policies and User Policies - Amazon Simple Storage Service
* AWS Glue Documentation
* What is Amazon API Gateway? - Amazon API Gateway
NEW QUESTION # 272
A data engineer is using AWS Glue to build an extract, transform, and load (ETL) pipeline that processes streaming data from sensors. The pipeline sends the data to an Amazon S3 bucket in near real-time. The data engineer also needs to perform transformations and join the incoming data with metadata that is stored in an Amazon RDS for PostgreSQL database. The data engineer must write the results back to a second S3 bucket in Apache Parquet format.
Which solution will meet these requirements?
Answer: B
Explanation:
* AWS Glue streaming jobs are purpose-built for low-latency, near real-time ETL pipelines.
* Glue Studio allows visual job authoring and supports complex transformations, including joins with Amazon RDS databases, through JDBC connectors.
* Data can be written directly to S3 in Apache Parquet format using built-in AWS Glue support.
Options B and C are more suitable for batch or development/testing. Option D is for custom batch jobs, not real-time processing.
"You can use AWS Glue streaming jobs with Glue Studio to process streaming data, perform transformations, join with RDS data sources, and write results in formats like Parquet to S3." Reference: AWS Glue Studio and Streaming Jobs - AWS Documentation
NEW QUESTION # 273
A company stores employee data in Amazon Redshift A table named Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key. Which queries will MOST increase the speed of a query by using a compound sort key of the table? (Select TWO.)
Answer: A,E
Explanation:
In Amazon Redshift, a compound sort key is designed to optimize the performance of queries that use filtering and join conditions on the columns in the sort key. A compound sort key orders the data based on the first column, followed by the second, and so on. In the scenario given, the compound sort key consists of Region ID, Department ID, and Role ID. Therefore, queries that filter on the leading columns of the sort key are more likely to benefit from this order.
Option B: "Select * from Employee where Region ID='North America' and Department ID=20;" This query will perform well because it uses both the Region ID and Department ID, which are the first two columns of the compound sort key. The order of the columns in the WHERE clause matches the order in the sort key, thus allowing the query to scan fewer rows and improve performance.
Option C: "Select * from Employee where Department ID=20 and Region ID='North America';" This query also benefits from the compound sort key because it includes both Region ID and Department ID, which are the first two columns in the sort key. Although the order in the WHERE clause does not match exactly, Amazon Redshift will still leverage the sort key to reduce the amount of data scanned, improving query speed.
Options A, D, and E are less optimal because they do not utilize the sort key as effectively:
Option A only filters by the Region ID, which may still use the sort key but does not take full advantage of the compound nature.
Option D uses only Role ID, the last column in the compound sort key, which will not benefit much from sorting since it is the third key in the sort order.
Option E filters on Region ID and Role ID but skips the Department ID column, making it less efficient for the compound sort key.
Reference:
Amazon Redshift Documentation - Sorting Data
AWS Certified Data Analytics Study Guide
AWS Certification - Data Engineer Associate Exam Guide
NEW QUESTION # 274
A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file.
Which Step Functions state should the data engineer use to meet these requirements?
Answer: D
Explanation:
Option C is the correct answer because the Map state is designed to process a collection of data in parallel by applying the same transformation to each element. The Map state can invoke a nested workflow for each element, which can be another state machine or a Lambda function. The Map state will wait until all the parallel executions are completed before moving to the next state.
Option A is incorrect because the Parallel state is used to execute multiple branches of logic concurrently, not to process a collection of data. The Parallel state can have different branches with different logic and states, whereas the Map state has only one branch that is applied to each element of the collection.
Option B is incorrect because the Choice state is used to make decisions based on a comparison of a value to a set of rules. The Choice state does not process any data or invoke any nested workflows.
Option D is incorrect because the Wait state is used to delay the state machine from continuing for a specified time. The Wait state does not process any data or invoke any nested workflows.
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Orchestration, Section 5.3: AWS Step Functions, Pages 131-132 Building Batch Data Analytics Solutions on AWS, Module 5: Data Orchestration, Lesson 5.2: AWS Step Functions, Pages 9-10 AWS Documentation Overview, AWS Step Functions Developer Guide, Step Functions Concepts, State Types, Map State, Pages 1-3
NEW QUESTION # 275
A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.
The company stores all order data at a single path within the S3 bucket. The data has more than 100 columns. The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.
The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded. A data engineer must resolve the performance issues for the queries.
Which combination of steps will meet this requirement with LEAST developmental effort? (Select TWO.)
Answer: A,C
Explanation:
The performance issue in Amazon Redshift Spectrum queries arises due to the nature of CSV files, which are row-based storage formats. Spectrum is more optimized for columnar formats, which significantly improve performance by reducing the amount of data scanned. Also, partitioning data based on relevant columns like order date can further reduce the amount of data scanned, as queries can focus only on the necessary partitions.
A . Configure the third-party application to create the files in a columnar format:
Columnar formats (like Parquet or ORC) store data in a way that is optimized for analytical queries because they allow queries to scan only the columns required, rather than scanning all columns in a row-based format like CSV.
Amazon Redshift Spectrum works much more efficiently with columnar formats, reducing the amount of data that needs to be scanned, which improves query performance.
Reference:
C . Partition the order data in the S3 bucket based on order date:
Partitioning the data on columns like order date allows Redshift Spectrum to skip scanning unnecessary partitions, leading to improved query performance.
By organizing data into partitions, you minimize the number of files Spectrum has to read, further optimizing performance.
Alternatives Considered:
B (Develop an AWS Glue ETL job): While consolidating files can improve performance by reducing the number of small files (which can be inefficient to process), it adds additional ETL complexity. Switching to a columnar format (Option A) and partitioning (Option C) provides more significant performance improvements with less development effort.
D and E (JSON-related options): Using JSON format or the SUPER type in Redshift introduces complexity and isn't as efficient as the proposed solutions, especially since JSON is not a columnar format.
Amazon Redshift Spectrum Documentation
Columnar Formats and Data Partitioning in S3
NEW QUESTION # 276
......
The AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) exam questions are the real, valid, and updated Data-Engineer-Associate Exam Questions that are specifically designed for quick and complete Data-Engineer-Associate exam preparation. With Exams4Collection AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) practice test questions you can start Amazon Data-Engineer-Associate exam preparation immediately.
Study Data-Engineer-Associate Demo: https://www.exams4collection.com/Data-Engineer-Associate-latest-braindumps.html
P.S. Free 2026 Amazon Data-Engineer-Associate dumps are available on Google Drive shared by Exams4Collection: https://drive.google.com/open?id=1Nk5blJCDmjOxmvm5SECpWCJefo8vO_dn