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최신 AWS Certified Data Engineer Data-Engineer-Associate 무료샘플문제 (Q227-Q232):

질문 # 227
A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary.
The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to useData Quality Definition Language (DQDL)rules in an AWS Glue Data Quality job.
Which rule will meet these requirements?

정답：A

설명：
TheDatasetMatchrule in DQDL checks for full value equivalence between mapped fields. A value of1.0 indicates a100% match. The correct syntax and metric for an exact match scenario are:
"Use DatasetMatch when comparing mapped fields between two datasets. The comparison score of 1.0 confirms a perfect match."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Options with "100" use incorrect syntax since DQDL usesfloating-point scores(e.g., 1.0, 0.95), not percentages.


질문 # 228
A company stores data from an application in an Amazon DynamoDB table that operates in provisioned capacity mode. The workloads of the application have predictable throughput load on a regular schedule.
Every Monday, there is an immediate increase in activity early in the morning. The application has very low usage during weekends.
The company must ensure that the application performs consistently during peak usage times.
Which solution will meet these requirements in the MOST cost-effective way?

정답：B

설명：
Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. DynamoDB offers two capacity modes for throughput capacity:
provisioned and on-demand. In provisioned capacity mode, you specify the number of read and write capacity units per second that you expect your application to require. DynamoDB reserves the resources to meet your throughput needs with consistent performance. In on-demand capacity mode, you pay per request and DynamoDB scales the resources up and down automatically based on the actual workload. On-demand capacity mode is suitable for unpredictable workloads that can vary significantly over time1.
The solution that meets the requirements in the most cost-effective way is to use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times and lower capacity during off-peak times. This solution has the following advantages:
* It allows you to optimize the cost and performance of your DynamoDB table by adjusting the provisioned capacity according to your predictable workload patterns. You can use scheduled scaling to specify the date and time for the scaling actions, and the new minimum and maximum capacity limits. For example, you can schedule higher capacity for every Monday morning and lower capacity for weekends2.
* It enables you to take advantage of the lower cost per unit of provisioned capacity mode compared to on-demand capacity mode. Provisioned capacity mode charges a flat hourly rate for the capacity you reserve, regardless of how much you use. On-demand capacity mode charges for each read and write request you consume, with no minimum capacity required. For predictable workloads, provisioned capacity mode can be more cost-effective than on-demand capacity mode1.
* It ensures that your application performs consistently during peak usage times by having enough capacity to handle the increased load. You can also use auto scaling to automatically adjust the provisioned capacity based on the actual utilization of your table, and set a target utilization percentage for your table or global secondary index. This way, you can avoid under-provisioning or over- provisioning your table2.
Option A is incorrect because it suggests increasing the provisioned capacity to the maximum capacity that is currently present during peak load times. This solution has the following disadvantages:
* It wastes money by paying for unused capacity during off-peak times. If you provision the same high capacity for all times, regardless of the actual workload, you are over-provisioning your table and paying for resources that you don't need1.
* It does not account for possible changes in the workload patterns over time. If your peak load times increase or decrease in the future, you may need to manually adjust the provisioned capacity to match the new demand. This adds operational overhead and complexity to your application2.
Option B is incorrect because it suggests dividing the table into two tables and provisioning each table with half of the provisioned capacity of the original table. This solution has the following disadvantages:
* It complicates the data model and the application logic by splitting the data into two separate tables.
You need to ensure that the queries are evenly distributed across both tables, and that the data is consistent and synchronized between them. This adds extra development and maintenance effort to your application3.
* It does not solve the problem of adjusting the provisioned capacity according to the workload patterns.
You still need to manually or automatically scale the capacity of each table based on the actual utilization and demand. This may result in under-provisioning or over-provisioning your tables2.
Option D is incorrect because it suggests changing the capacity mode from provisioned to on-demand. This solution has the following disadvantages:
* It may incur higher costs than provisioned capacity mode for predictable workloads. On-demand capacity mode charges for each read and write request you consume, with no minimum capacity required. For predictable workloads, provisioned capacity mode can be more cost-effective than on- demand capacity mode, as you can reserve the capacity you need at a lower rate1.
* It may not provide consistent performance during peak usage times, as on-demand capacity mode may take some time to scale up the resources to meet the sudden increase in demand. On-demand capacity mode uses adaptive capacity to handle bursts of traffic, but it may not be able to handle very large spikes or sustained high throughput. In such cases, you may experience throttling or increased latency.
:
1: Choosing the right DynamoDB capacity mode - Amazon DynamoDB
2: Managing throughput capacity automatically with DynamoDB auto scaling - Amazon DynamoDB
3: Best practices for designing and using partition keys effectively - Amazon DynamoDB
[4]: On-demand mode guidelines - Amazon DynamoDB
[5]: How to optimize Amazon DynamoDB costs - AWS Database Blog
[6]: DynamoDB adaptive capacity: How it works and how it helps - AWS Database Blog
[7]: Amazon DynamoDB pricing - Amazon Web Services (AWS)


질문 # 229
A data engineer needs to debug an AWS Glue job that reads from Amazon S3 and writes to Amazon Redshift.
The data engineer enabled the bookmark feature for the AWS Glue job. The data engineer has set the maximum concurrency for the AWS Glue job to 1.
The AWS Glue job is successfully writing the output to Amazon Redshift. However, the Amazon S3 files that were loaded during previous runs of the AWS Glue job are being reprocessed by subsequent runs.
What is the likely reason the AWS Glue job is reprocessing the files?

정답：C

설명：
The issue described is that the AWS Glue job is reprocessing files from previous runs despite the bookmark feature being enabled. Bookmarks in AWS Glue allow jobs to keep track of which files or data have already been processed to avoid reprocessing. The most likely reason for reprocessing the files is missing S3 permissions, specifically s3 s3 is a permission required by AWS Glue when bookmarks are enabled to ensure Glue can retrieve metadata from the files in S3, which is necessary for the bookmark mechanism to function correctly. Without this permission, Glue cannot track which files have been processed, resulting in reprocessing during subsequent runs.
Concurrency settings (Option B) and the version of AWS Glue (Option C) do not affect the bookmark behavior. Similarly, the lack of a commit statement (Option D) is not applicable in this context, as Glue handles commits internally when interacting with Redshift and S3.
Thus, the root cause is likely related to insufficient permissions on the S3 bucket, specifically s3
, which is required for bookmarks to work as expected.
References:
AWS Glue Job Bookmarks Documentation
AWS Glue Permissions for Bookmarks


질문 # 230
A company is using Amazon Redshift to build a data warehouse solution. The company is loading hundreds of tiles into a tact table that is in a Redshift cluster.
The company wants the data warehouse solution to achieve the greatest possible throughput. The solution must use cluster resources optimally when the company loads data into the tact table.
Which solution will meet these requirements?

정답：C

설명：
To achieve the highest throughput and efficiently use cluster resources while loading data into an Amazon Redshift cluster, the optimal approach is to use a single COPY command that ingests data in parallel.
* Option D: Use a single COPY command to load the data into the Redshift cluster.The COPY command is designed to load data from multiple files in parallel into a Redshift table, using all the cluster nodes to optimize the load process. Redshift is optimized for parallel processing, and a single COPY command can load multiple files at once, maximizing throughput.
Options A, B, and C either involve unnecessary complexity or inefficient approaches, such as using multiple COPY commands or INSERT statements, which are not optimized for bulk loading.
References:
* Amazon Redshift COPY Command Documentation


질문 # 231
A retail company stores data from a product lifecycle management (PLM) application in an on-premises MySQL database. The PLM application frequently updates the database when transactions occur.
The company wants to gather insights from the PLM application in near real time. The company wants to integrate the insights with other business datasets and to analyze the combined dataset by using an Amazon Redshift data warehouse.
The company has already established an AWS Direct Connect connection between the on-premises infrastructure and AWS.
Which solution will meet these requirements with the LEAST development effort?

정답：B

설명：
Problem Analysis:
The company needs near real-time replication of MySQL updates to Amazon Redshift.
Minimal development effort is required for this solution.
Key Considerations:
AWS DMS provides a full load + CDC (Change Data Capture) mode for continuous replication of database changes.
DMS integrates natively with both MySQL and Redshift, simplifying setup.
Solution Analysis:
Option A: AWS Glue Job
Glue is batch-oriented and does not support near real-time replication.
Option B: DMS with Full Load + CDC
Efficiently handles initial database load and continuous updates.
Requires minimal setup and operational overhead.
Option C: AppFlow SDK
AppFlow is not designed for database replication. Custom connectors increase development effort.
Option D: DataSync
DataSync is for file synchronization and not suitable for database updates.
Final Recommendation:
Use AWS DMS in full load + CDC mode for continuous replication.
AWS Database Migration Service Documentation
Setting Up DMS with Redshift


질문 # 232
......

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