DATA-ENGINEER-ASSOCIATE VALID TEST PDF - DATA-ENGINEER-ASSOCIATE VALID EXAM TESTKING

Data-Engineer-Associate Valid Test Pdf - Data-Engineer-Associate Valid Exam Testking

Data-Engineer-Associate Valid Test Pdf - Data-Engineer-Associate Valid Exam Testking

Blog Article

Tags: Data-Engineer-Associate Valid Test Pdf, Data-Engineer-Associate Valid Exam Testking, Data-Engineer-Associate Certification Exam Dumps, Data-Engineer-Associate Materials, Valid Test Data-Engineer-Associate Tutorial

We understand your enthusiasm of effective practice materials, because they are the most hopeful tools help us gain more knowledge with the least time to achieve success, and we have been in your shoes. Our Data-Engineer-Associate exam questions can help you achieve that dreams easily. Whatever you want to master about this exam, our experts have compiled into them for your reference. A growing number of exam candidates are choosing our Data-Engineer-Associate Exam Questions, why are you still hesitating? As long as you have make up your mind, our AWS Certified Data Engineer - Associate (DEA-C01) study question is available in five minutes, so just begin your review now! This could be a pinnacle in your life.

Dumps4PDF is famous for our company made these exam questions with accountability. We understand you can have more chances getting higher salary or acceptance instead of preparing for the Data-Engineer-Associate exam. Our Data-Engineer-Associate practice materials are made by our responsible company which means you can gain many other benefits as well. We offer free demos of our Data-Engineer-Associate Exam Questions for your reference, and send you the new updates of our Data-Engineer-Associate study guide if our experts make them freely. All we do and the promises made are in your perspective.

>> Data-Engineer-Associate Valid Test Pdf <<

Data-Engineer-Associate Valid Exam Testking - Data-Engineer-Associate Certification Exam Dumps

Life is short for each of us, and time is precious to us. Therefore, modern society is more and more pursuing efficient life, and our Data-Engineer-Associate exam materials are the product of this era, which conforms to the development trend of the whole era. It seems that we have been in a state of study and examination since we can remember, and we have experienced countless tests, including the qualification examinations we now face. In the process of job hunting, we are always asked what are the achievements and what certificates have we obtained? Therefore, we get the test Amazon certification and obtain the qualification certificate to become a quantitative standard, and our Data-Engineer-Associate learning guide can help you to prove yourself the fastest in a very short period of time.

Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q62-Q67):

NEW QUESTION # 62
A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Set up Java Database Connectivity (JDBC) connections to Amazon Redshift.
  • B. Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.
  • C. Use the Amazon Redshift Data API.
  • D. Establish WebSocket connections to Amazon Redshift.

Answer: C

Explanation:
The Amazon Redshift Data API is a built-in feature that allows you to run SQL queries on Amazon Redshift data with web services-based applications, such as AWS Lambda, Amazon SageMaker notebooks, and AWS Cloud9. The Data API does not require a persistent connection to your database, and it provides a secure HTTP endpoint and integration with AWS SDKs. You can use the endpoint to run SQL statements without managing connections. The Data API also supports both Amazon Redshift provisioned clusters and Redshift Serverless workgroups. The Data API is the best solution for running real-time queries on the financial data from within the trading application, as it has the least operational overhead compared to the other options.
Option A is not the best solution, as establishing WebSocket connections to Amazon Redshift would require more configuration and maintenance than using the Data API. WebSocket connections are also not supported by Amazon Redshift clusters or serverless workgroups.
Option C is not the best solution, as setting up JDBC connections to Amazon Redshift would also require more configuration and maintenance than using the Data API. JDBC connections are also not supported by Redshift Serverless workgroups.
Option D is not the best solution, as storing frequently accessed data in Amazon S3 and using Amazon S3 Select to run the queries would introduce additional latency and complexity than using the Data API. Amazon S3 Select is also not optimized for real-time queries, as it scans the entire object before returning the results.
References:
Using the Amazon Redshift Data API
Calling the Data API
Amazon Redshift Data API Reference
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide


NEW QUESTION # 63
A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.
  • B. Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.
  • C. Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.
  • D. Confiqure an AWS Lambda function to load data from the S3 bucket into a pandas dataframe- Write a SQL SELECT statement on the dataframe to query the required column.

Answer: C

Explanation:
Option B is the best solution to meet the requirements with the least operational overhead because S3 Select is a feature that allows you to retrieve only a subset of data from an S3 object by using simple SQL expressions.
S3 Select works on objects stored in CSV, JSON, or Parquet format. By using S3 Select, you can avoid the need to download and process the entire S3 object, which reduces the amount of data transferred and the computation time. S3 Select is also easy to use and does not require any additional services or resources.
Option A is not a good solution because it involves writing custom code and configuring an AWS Lambda function to load data from the S3 bucket into a pandas dataframe and query the required column. This option adds complexity and latency to the data retrieval process and requires additional resources and configuration.Moreover, AWS Lambda has limitations on the execution time, memory, and concurrency, which may affect the performance and reliability of the data retrieval process.
Option C is not a good solution because it involves creating and running an AWS Glue DataBrew project to consume the S3 objects and query the required column. AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. However, in this scenario, the data is already in Parquet format, which is a columnar storage format that is optimized for analytics.
Therefore, there is no need to use AWS Glue DataBrew to prepare the data. Moreover, AWS Glue DataBrew adds extra time and cost to the data retrieval process and requires additional resources and configuration.
Option D is not a good solution because it involves running an AWS Glue crawler on the S3 objects and using a SQL SELECT statement in Amazon Athena to query the required column. An AWS Glue crawler is a service that can scan data sources and create metadata tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores information about the data sources, such as schema, format, and location.
Amazon Athena is a serverless interactive query service that allows you to analyze data in S3 using standard SQL. However, in this scenario, the schema and format of the data are already known and fixed, so there is no need to run a crawler to discover them. Moreover, running a crawler and using Amazon Athena adds extra time and cost to the data retrieval process and requires additional services and configuration.
References:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
S3 Select and Glacier Select - Amazon Simple Storage Service
AWS Lambda - FAQs
What Is AWS Glue DataBrew? - AWS Glue DataBrew
Populating the AWS Glue Data Catalog - AWS Glue
What is Amazon Athena? - Amazon Athena


NEW QUESTION # 64
A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3.
The company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically.
  • B. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically.
  • C. Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog.
  • D. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog.

Answer: C

Explanation:
This solution will meet the requirements with the least operational overhead because it uses the AWS Glue Data Catalog as the central metadata repository for data sources that run in the AWS Cloud. The AWS Glue Data Catalog is a fully managed service that provides a unified view of your data assets across AWS and on- premises data sources. It stores the metadata of your data in tables, partitions, and columns, and enables you to access and query your data using various AWS services, such as Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum. You can use AWS Glue crawlers to connect to multiple data stores, such as Amazon RDS, Amazon Redshift, and Amazon S3, and to update the Data Catalog with metadata changes.
AWS Glue crawlers can automatically discover the schema and partition structure of your data, and create or update the corresponding tables in the Data Catalog. You can schedule the crawlers to run periodically to update the metadata catalog, and configure them to detect changes to the source metadata, such as new columns, tables, or partitions12.
The other options are not optimal for the following reasons:
* A. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically. This option is not recommended, as it would require more operational overhead to create and manage an Amazon Aurora database as the data catalog, and to write and maintain AWS Lambda functions to gather and update the metadata information from multiple sources. Moreover, this option would not leverage the benefits of the AWS Glue Data Catalog, such as data cataloging, data transformation, and data governance.
* C. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically. This option is also not recommended, as it would require more operational overhead to create and manage an Amazon DynamoDB table as the data catalog, and to write and maintain AWS Lambda functions to gather and update the metadata information from multiple sources. Moreover, this option would not leverage the benefits of the AWS Glue Data Catalog, such as data cataloging, data transformation, and data governance.
* D. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog. This option is not optimal, as it would require more manual effort to extract the schema for Amazon RDS and Amazon Redshift sources, and to build the Data Catalog. This option would not take advantage of the AWS Glue crawlers' ability to automatically discover the schema and partition structure of your data from various data sources, and to create or update the corresponding tables in the Data Catalog.
References:
* 1: AWS Glue Data Catalog
* 2: AWS Glue Crawlers
* : Amazon Aurora
* : AWS Lambda
* : Amazon DynamoDB


NEW QUESTION # 65
A data engineer uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run data pipelines in an AWS account. A workflow recently failed to run. The data engineer needs to use Apache Airflow logs to diagnose the failure of the workflow. Which log type should the data engineer use to diagnose the cause of the failure?

  • A. YourEnvironmentName-Scheduler
  • B. YourEnvironmentName-Task
  • C. YourEnvironmentName-WebServer
  • D. YourEnvironmentName-DAGProcessing

Answer: B

Explanation:
In Amazon Managed Workflows for Apache Airflow (MWAA), the type of log that is most useful for diagnosing workflow (DAG) failures is the Task logs. These logs provide detailed information on the execution of each task within the DAG, including error messages, exceptions, and other critical details necessary for diagnosing failures.
* Option D: YourEnvironmentName-TaskTask logs capture the output from the execution of each task within a workflow (DAG), which is crucial for understanding what went wrong when a DAG fails.
These logs contain detailed execution information, including errors and stack traces, making them the best source for debugging.
Other options (WebServer, Scheduler, and DAGProcessing logs) provide general environment-level logs or logs related to scheduling and DAG parsing, but they do not provide the granular task-level execution details needed for diagnosing workflow failures.
References:
* Amazon MWAA Logging and Monitoring
* Apache Airflow Task Logs


NEW QUESTION # 66
A company uses an Amazon Redshift provisioned cluster as its database. The Redshift cluster has five reserved ra3.4xlarge nodes and uses key distribution.
A data engineer notices that one of the nodes frequently has a CPU load over 90%. SQL Queries that run on the node are queued. The other four nodes usually have a CPU load under 15% during daily operations.
The data engineer wants to maintain the current number of compute nodes. The data engineer also wants to balance the load more evenly across all five compute nodes.
Which solution will meet these requirements?

  • A. Change the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.
  • B. Change the distribution key to the table column that has the largest dimension.
  • C. Upgrade the reserved node from ra3.4xlarqe to ra3.16xlarqe.
  • D. Change the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement.

Answer: B

Explanation:
Changing the distribution key to the table column that has the largest dimension will help to balance the load more evenly across all five compute nodes. The distribution key determines how the rows of a table are distributed among the slices of the cluster. If the distribution key is not chosen wisely, it can cause data skew, meaning some slices will have more data than others, resulting in uneven CPU load and query performance.
By choosing the table column that has the largest dimension, meaning the column that has the most distinct values, as the distribution key, the data engineer can ensure that the rows are distributed more uniformly across the slices, reducing data skew and improving query performance.
The other options are not solutions that will meet the requirements. Option A, changing the sort key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load. The sort key determines the order in which the rows of a table are stored on disk, which can improve the performance of range-restricted queries, but not the load balancing. Option C, upgrading the reserved node from ra3.4xlarge to ra3.16xlarge, will not maintain the current number of compute nodes, as it will increase the cost and the capacity of the cluster. Option D, changing the primary key to be the data column that is most often used in a WHERE clause of the SQL SELECT statement, will not affect the data distribution or the CPU load either. The primary key is a constraint that enforces the uniqueness of the rows in a table, but it does not influence the data layout or the query optimization. References:
Choosing a data distribution style
Choosing a data sort key
Working with primary keys


NEW QUESTION # 67
......

To avail of all these Amazon Data-Engineer-Associate certification exam benefits you need to enroll in Amazon Data-Engineer-Associate certification exam and pass it with good scores. Are you ready for this? If your answer is right then you do not need to go anywhere. Just download Amazon Data-Engineer-Associate Dumps questions and start preparing today.

Data-Engineer-Associate Valid Exam Testking: https://www.dumps4pdf.com/Data-Engineer-Associate-valid-braindumps.html

With the assistance of Data-Engineer-Associate test engine, you can not only save time and energy in the Data-Engineer-Associate pass test, but also get high score in the real exam, Amazon Data-Engineer-Associate Valid Test Pdf Free 3 month Product Updates for Customers for all new questions and answers in PDF and APP, After you took the test, you will find about 85% real questions appear in our Data-Engineer-Associate examcollection braindumps, The moment you money has been transferred into our account, and our system will send our Amazon Data-Engineer-Associate training materials to your mail boxes so that you can download them directly.

Analyze all content with task analysis, All inheritance in Java is public inheritance, With the assistance of Data-Engineer-Associate Test Engine, you can not only save time and energy in the Data-Engineer-Associate pass test, but also get high score in the real exam.

Amazon Data-Engineer-Associate Questions PDF To Unlock Your Career [2025]

Free 3 month Product Updates for Customers for all new questions and answers in PDF and APP, After you took the test, you will find about 85% real questions appear in our Data-Engineer-Associate examcollection braindumps.

The moment you money has been transferred into our account, and our system will send our Amazon Data-Engineer-Associate training materials to your mail boxes so that you can download them directly.

In this case, suggest you to ask Data-Engineer-Associate our on-line for the discount code to enjoy more benefit for you.

Report this page