BONUS!!! Download part of PracticeTorrent Data-Engineer-Associate dumps for free: https://drive.google.com/open?id=1Xhq4Z12yPSKPwRaXzdffFtXy_K9fu_EE
Pass the AWS Certified Data Engineer - Associate (DEA-C01) Data-Engineer-Associate certification exam which is a challenging task. To make Data-Engineer-Associate exam success journey simple, quick, and smart, you have to prepare well and show a firm commitment to passing this exam. The real, updated, and error-free AWS Certified Data Engineer - Associate (DEA-C01) Data-Engineer-Associate Exam Dumps are available over the PracticeTorrent.
| Section | Weight | Objectives |
|---|---|---|
| Data Operations and Support | 22% | - Ensure reliability and scalability - Monitor and troubleshoot data pipelines
- Automate operational tasks |
| Data Security and Governance | 18% | - Enforce compliance and data governance
- Encrypt data at rest and in transit |
| Data Store Management | 26% | - Design and implement data storage solutions
- Optimize storage performance and cost |
| Data Ingestion and Transformation | 34% | - Implement data quality and validation - Transform and enrich data
|
>> Data-Engineer-Associate Reliable Exam Sample <<
The AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) product can be easily accessed just after purchasing it from PracticeTorrent. You can receive free Amazon Dumps updates for up to 1 year after buying material. The 24/7 support system is also available for you, which helps you every time you get stuck somewhere. Many students have studied from the PracticeTorrent Amazon Data-Engineer-Associate practice material and rated it positively because they have passed the AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) certification exam on the first try.
NEW QUESTION # 125
An application uses an AWS Lambda function that is configured with managed runtimes. The Lambda function successfully writes logs to the default Amazon CloudWatch Logs log group. A data engineer wants to modify the logging behavior to show only ERROR level logs for application logs and WARN level logs for system logs.
Which solution will meet these requirements?
Answer: B
Explanation:
Option C is correct because AWS Lambda's advanced logging controls support separate application log level and system log level filtering, but AWS documentation states that for Lambda to filter application logs according to their log level, the function must use JSON formatted logs. AWS also documents that in the advanced logging configuration you can choose a log level such as ERROR for application logs and WARN for system logs. Therefore, configuring the function to use JSON log format is the necessary step that enables the required log-level filtering behavior.
Option A is irrelevant because the function already writes logs successfully. Option B alone is insufficient because changing code-level logging does not configure Lambda's separate system log filtering behavior, and Lambda's application log filtering relies on structured JSON logs. Option D changes the log destination but does not implement the required filtering levels. The official Lambda documentation makes clear that JSON log format is the enabling configuration for this feature, so that is the correct answer.
NEW QUESTION # 126
A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views.
Which solution will meet this requirement with the LEAST effort?
Answer: B
Explanation:
The query editor v2 in Amazon Redshift is a web-based tool that allows users to run SQL queries and scripts on Amazon Redshift clusters. The query editor v2 supports creating and managing materialized views, which are precomputed results of a query that can improve the performance of subsequent queries. The query editor v2 also supports scheduling queries to run at specified intervals, which can be used to refresh materialized views automatically. This solution requires the least effort, as it does not involve any additional services, coding, or configuration. The other solutions are more complex and require more operational overhead. Apache Airflow is an open-source platform for orchestrating workflows, which can be used to refresh materialized views, but it requires setting up and managing an Airflow environment, creating DAGs (directed acyclic graphs) to define the workflows, and integrating with Amazon Redshift. AWS Lambda is a serverless compute service that can run code in response to events, which can be used to refresh materialized views, but it requires creating and deploying Lambda functions, defining UDFs within Amazon Redshift, and triggering the functions using events or schedules. AWS Glue is a fully managed ETL service that can run jobs to transform and load data, which can be used to refresh materialized views, but it requires creating and configuring Glue jobs, defining Glue workflows to orchestrate the jobs, and scheduling the workflows using triggers. Reference:
Query editor V2
Working with materialized views
Scheduling queries
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]
NEW QUESTION # 127
A company uses Amazon Redshift to store order transactions from the current day. The company has an orders table that contains the previous order data. The company also has a staging table that contains new or updated order records. The company needs to remove stale records from the orders table and insert the most recent data in the orders table from the staging table. Several downstream applications need the orders table to display up-to-date information.
Which solution will meet these requirements?
Answer: B
Explanation:
Option D is correct because Amazon Redshift stored procedures are designed to encapsulate a sequence of SQL statements and business logic inside the database. AWS documentation states that stored procedures are commonly used for data transformation, data validation, and business-specific logic, and that they can combine multiple SQL steps into one procedure. AWS also documents the standard Redshift pattern for deleting stale rows and inserting fresh rows from a staging table, which is exactly the requirement here.
Keeping the operation inside Redshift is the most direct way to maintain an up-to-date orders table for downstream consumers.
Option A is incorrect because Redshift Spectrum is for querying external data in S3, not for performing this in- place Redshift table-maintenance pattern. Option B adds unnecessary unload and reload steps, creating delay and operational complexity. Option C is also unsuitable because Athena federated queries are not the right mechanism for transactional maintenance of Redshift tables. The correct DEA-C01-style answer is to use Redshift-native procedural SQL to delete stale rows and insert current rows from staging.
NEW QUESTION # 128
A gaming company uses Amazon Kinesis Data Streams to collect clickstream data. The company uses Amazon Kinesis Data Firehose delivery streams to store the data in JSON format in Amazon S3. Data scientists at the company use Amazon Athena to query the most recent data to obtain business insights.
The company wants to reduce Athena costs but does not want to recreate the data pipeline.
Which solution will meet these requirements with the LEAST management effort?
Answer: D
Explanation:
Step 1: Understanding the Problem
The company collectsclickstream datavia Amazon Kinesis Data Streams and stores it inJSON formatin Amazon S3 using Kinesis Data Firehose. They useAmazon Athenato query the data, but they want toreduce Athena costswhile maintaining the same data pipeline.
Since Athena charges based on the amount of data scanned during queries, reducing the data size (by converting JSON to a more efficient format likeApache Parquet) is a key solution to lowering costs.
Step 2: Why Option A is Correct
* Option Aprovides a straightforward way to reduce costs withminimal management overhead:
* Changing the Firehose output format to Parquet: Parquet is a columnar data format, which is more compact and efficient than JSON for Athena queries. It significantly reduces the amount of data scanned, which in turn reduces Athena query costs.
* Custom S3 Object Prefix (YYYYMMDD): Adding a date-based prefix helps in partitioning the data, which further improves query efficiency in Athena by limiting the data scanned to only relevant partitions.
* AWS Glue ETL Job for Existing Data: To handle existing data stored in JSON format, a one- time AWS Glue ETL job can combine small JSON files, convert them to Parquet, and apply the YYYYMMDD prefix. This ensures consistency in the S3 bucket structure and allows Athena to efficiently query historical data.
* ALTER TABLE ADD PARTITION: This command updates Athena's table metadata to reflect the new partitions, ensuring that future queries target only the required data.
Step 3: Why Other Options Are Not Ideal
* Option B (Apache Spark on EMR)introduces higher management effort by requiring the setup of Apache Spark jobsand anAmazon EMR cluster. While it achieves the goal of converting JSON to Parquet, it involves running and maintaining an EMR cluster, which adds operational complexity.
* Option C (Kinesis and Apache Flink)is a more complex solution involvingApache Flink, which adds a real-time streaming layer to aggregate data. Although Flink is a powerful tool for stream processing, it adds unnecessary overhead in this scenario since the company already uses Kinesis Data Firehose for batch delivery to S3.
* Option D (AWS Lambda with Firehose)suggests usingAWS Lambdato convert records in real time.
While Lambda can work in some cases, it's generally not the best tool for handling large-scale data transformations like JSON-to-Parquet conversion due to potential scaling and invocation limitations.
Additionally, running parallel Glue jobs further complicates the setup.
Step 4: How Option A Minimizes Costs
* By usingApache Parquet, Athena queries become more efficient, as Athena will scan significantly less data, directly reducing query costs.
* Firehosenatively supports Parquet as an output format, so enabling this conversion in Firehose requires minimal effort. Once set, new data will automatically be stored in Parquet format in S3, without requiring any custom coding or ongoing management.
* TheAWS Glue ETL jobfor historical data ensures that existing JSON files are also converted to Parquet format, ensuring consistency across the data stored in S3.
Conclusion:
Option A meets the requirement toreduce Athena costswithout recreating the data pipeline, using Firehose's native support forApache Parquetand a simple one-timeAWS Glue ETL jobfor existing data. This approach involvesminimal management effortcompared to the other solutions.
NEW QUESTION # 129
A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options.
The company's current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS.
Which extract, transform, and load (ETL) service will meet these requirements?
Answer: C
Explanation:
AWS Glue is a fully managed serverless ETL service that can handle petabytes of data in seconds. AWS Glue can run Apache Spark and Apache Flink jobs without requiring any infrastructure provisioning or management. AWS Glue can also integrate with Apache Pig, Apache Oozie, and Apache Hbase using AWS Glue Data Catalog and AWS Glue workflows. AWS Glue can reduce the overall operational overhead by automating the data discovery, data preparation, and data loading processes. AWS Glue can also optimize the cost and performance of ETL jobs by using AWS Glue Job Bookmarking, AWS Glue Crawlers, and AWS Glue Schema Registry. References:
* AWS Glue
* AWS Glue Data Catalog
* AWS Glue Workflows
* [AWS Glue Job Bookmarking]
* [AWS Glue Crawlers]
* [AWS Glue Schema Registry]
* [AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]
NEW QUESTION # 130
......
If you want to pass Data-Engineer-Associate exams easily and obtain certifications in shortest time, the best way is to purchase the best high-quality Data-Engineer-Associate exam preparation materials. That's what we do. Our Data-Engineer-Associate training materials are famous for the high pass rate in this field, if you choose our products we are sure that you will 100% clear Data-Engineer-Associate Exams. If you are still headache about how to pass exam certainly, our Data-Engineer-Associate practice test questions will be your best choice. Don’t hesitate again and just choose us!
Test Data-Engineer-Associate Sample Online: https://www.practicetorrent.com/Data-Engineer-Associate-practice-exam-torrent.html
P.S. Free 2026 Amazon Data-Engineer-Associate dumps are available on Google Drive shared by PracticeTorrent: https://drive.google.com/open?id=1Xhq4Z12yPSKPwRaXzdffFtXy_K9fu_EE