Amazon Data-Engineer-Associateテスト難易度 & Data-Engineer-Associate出題内容

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更新するData-Engineer-Associateテスト難易度 & 合格スムーズData-Engineer-Associate出題内容 | 最新のData-Engineer-Associate日本語関連対策 AWS Certified Data Engineer - Associate (DEA-C01)

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) 認定 Data-Engineer-Associate 試験問題 (Q113-Q118):

質問 # 113
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?

A. Assign the same alias to each Lambda function. Call reach Lambda function by specifying the function's alias.
B. Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda functions.
C. Store a pointer to the custom Python scripts in the execution context object in a shared Amazon S3 bucket.
D. Store a pointer to the custom Python scripts in environment variables in a shared Amazon S3 bucket.

正解：B

解説：
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

質問 # 114
A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column.
Which solution will MOST speed up the Athena query performance?

A. Change the data format from .csvto JSON format. Apply Snappy compression.
B. Compress the .csv files by using gzjg compression.
C. Compress the .csv files by using Snappy compression.
D. Change the data format from .csvto Apache Parquet. Apply Snappy compression.

正解：D

解説：
Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. Athena supports various data formats, such as CSV, JSON, ORC, Avro, and Parquet. However, not all data formats are equally efficient for querying. Some data formats, such as CSV and JSON, are row-oriented, meaning that they store data as a sequence of records, each with the same fields. Row-oriented formats are suitable for loading and exporting data, but they are not optimal for analytical queries that often access only a subset of columns. Row-oriented formats also do not support compression or encoding techniques that can reduce the data size and improve the query performance.
On the other hand, some data formats, such as ORC and Parquet, are column-oriented, meaning that they store data as a collection of columns, each with a specific data type. Column-oriented formats are ideal for analytical queries that often filter, aggregate, or join data by columns. Column-oriented formats also support compression and encoding techniques that can reduce the data size and improve the query performance. For example, Parquet supports dictionary encoding, which replaces repeated values with numeric codes, and run-length encoding, which replaces consecutive identical values with a single value and a count. Parquet also supports various compression algorithms, such as Snappy, GZIP, and ZSTD, that can further reduce the data size and improve the query performance.
Therefore, changing the data format from CSV to Parquet and applying Snappy compression will most speed up the Athena query performance. Parquet is a column-oriented format that allows Athena to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. Snappy is a compression algorithm that reduces the data size without compromising the query speed, as it is splittable and does not require decompression before reading. This solution will also reduce the cost of Athena queries, as Athena charges based on the amount of data scanned from S3.
The other options are not as effective as changing the data format to Parquet and applying Snappy compression. Changing the data format from CSV to JSON and applying Snappy compression will not improve the query performance significantly, as JSON is also a row-oriented format that does not support columnar access or encoding techniques. Compressing the CSV files by using Snappy compression will reduce the data size, but it will not improve the query performance significantly, as CSV is still a row-oriented format that does not support columnar access or encoding techniques. Compressing the CSV files by using gzjg compression will reduce the data size, but it will degrade the query performance, as gzjg is not a splittable compression algorithm and requires decompression before reading. Reference:
Amazon Athena
Choosing the Right Data Format
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena

質問 # 115
A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances.
B. Change the volume type of the existing gp2 volumes to gp3. Enter new values for volume size, IOPS, and throughput.
C. Create new gp3 volumes. Gradually transfer the data to the new gp3 volumes. When the transfer is complete, mount the new gp3 volumes to the EC2 instances to replace the gp2 volumes.
D. Use AWS DataSync to create new gp3 volumes. Transfer the data from the original gp2 volumes to the new gp3 volumes.

正解：B

解説：
Changing the volume type of the existing gp2 volumes to gp3 is the easiest and fastest way to migrate to the new storage type without any downtime or data loss. You can use the AWS Management Console, the AWS CLI, or the Amazon EC2 API to modify the volume type, size, IOPS, and throughput of your gp2 volumes.
The modification takes effect immediately, and you can monitor the progress of the modification using CloudWatch. The other options are either more complex or require additional steps, such as creating snapshots, transferring data, or attaching new volumes, which can increase the operational overhead and the risk of errors. References:
* Migrating Amazon EBS volumes from gp2 to gp3 and save up to 20% on costs (Section: How to migrate from gp2 to gp3)
* Switching from gp2 Volumes to gp3 Volumes to Lower AWS EBS Costs (Section: How to Switch from GP2 Volumes to GP3 Volumes)
* Modifying the volume type, IOPS, or size of an EBS volume - Amazon Elastic Compute Cloud (Section: Modifying the volume type)

質問 # 116
A company uses Amazon RDS for MySQL as the database for a critical application. The database workload is mostly writes, with a small number of reads.
A data engineer notices that the CPU utilization of the DB instance is very high. The high CPU utilization is slowing down the application. The data engineer must reduce the CPU utilization of the DB Instance.
Which actions should the data engineer take to meet this requirement? (Choose two.)

A. Reboot the RDS DB instance once each week.
B. Modify the database schema to include additional tables and indexes.
C. Use the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilization. Optimize the problematic queries.
D. Implement caching to reduce the database query load.
E. Upgrade to a larger instance size.

正解：C、D

解説：
Amazon RDS is a fully managed service that provides relational databases in the cloud. Amazon RDS for MySQL is one of the supported database engines that you can use to run your applications. Amazon RDS provides various features and tools to monitor and optimize the performance of your DB instances, such as Performance Insights, Enhanced Monitoring, CloudWatch metrics and alarms, etc.
Using the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilization and optimizing the problematic queries will help reduce the CPU utilization of the DB instance. Performance Insights is a feature that allows you to analyze the load on your DB instance and determine what is causing performance issues. Performance Insights collects, analyzes, and displays database performance data using an interactive dashboard. You can use Performance Insights to identify the top SQL statements, hosts, users, or processes that are consuming the most CPU resources. You can also drill down into the details of each query and see the execution plan, wait events, locks, etc. By using Performance Insights, you can pinpoint the root cause of the high CPU utilization and optimize the queries accordingly. For example, you can rewrite the queries to make them more efficient, add or remove indexes, use prepared statements, etc.
Implementing caching to reduce the database query load will also help reduce the CPU utilization of the DB instance. Caching is a technique that allows you to store frequently accessed data in a fast and scalable storage layer, such as Amazon ElastiCache. By using caching, you can reduce the number of requests that hit your database, which in turn reduces the CPU load on your DB instance. Caching also improves the performance and availability of your application, as it reduces the latency and increases the throughput of your data access. You can use caching for various scenarios, such as storing session data, user preferences, application configuration, etc. You can also use caching for read-heavy workloads, such as displaying product details, recommendations, reviews, etc.
The other options are not as effective as using Performance Insights and caching. Modifying the database schema to include additional tables and indexes may or may not improve the CPU utilization, depending on the nature of the workload and the queries. Adding more tables and indexes may increase the complexity and overhead of the database, which may negatively affect the performance. Rebooting the RDS DB instance once each week will not reduce the CPU utilization, as it will not address the underlying cause of the high CPU load. Rebooting may also cause downtime and disruption to your application. Upgrading to a larger instance size may reduce the CPU utilization, but it will also increase the cost and complexity of your solution. Upgrading may also not be necessary if you can optimize the queries and reduce the database load by using caching. Reference:
Amazon RDS
Performance Insights
Amazon ElastiCache
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 3: Data Storage and Management, Section 3.1: Amazon RDS

質問 # 117
A company stores CSV files in an Amazon S3 bucket. A data engineer needs to process the data in the CSV files and store the processed data in a new S3 bucket.
The process needs to rename a column, remove specific columns, ignore the second row of each file, create a new column based on the values of the first row of the data, and filter the results by a numeric value of a column.
Which solution will meet these requirements with the LEAST development effort?

A. Use an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files.
B. Use AWS Glue Python jobs to read and transform the CSV files.
C. Use an AWS Glue custom crawler to read and transform the CSV files.
D. Use AWS Glue DataBrew recipes to read and transform the CSV files.

正解：D

質問 # 118
......

我々は不定期的に割引コードを提供することができます。受験生たちはData-Engineer-Associate試験を準備するとき、Data-Engineer-Associate参考書が必要です。だから、安い問題集はあなたにとって重要です。我々の安い問題集で、あなたは順調にData-Engineer-Associate試験に合格することができます。我々は受験生たちの合格を祈ります。

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我々の専門家はこの分野での最新動向に注意を払いますので、これらの変化をData-Engineer-Associateテスト練習にすぐに追加します、Data-Engineer-Associate学習教材の合格率が99％であることは間違いありません、JPTestKing AmazonのData-Engineer-Associate試験スタディガイドはあなたのキャリアの灯台になれます、多くの候補者は彼らが準備に多大な時間をかけるのに、失敗したことを反映しましたが、弊社の有効なData-Engineer-Associate資格問題集を購入した後、2～3日の試験準備だけで素晴らしいスコアを取られます、JPTestKing Data-Engineer-Associate出題内容は今まで数え切れないIT認定試験の受験者を助けて、皆さんから高い評判をもらいました、Amazon Data-Engineer-Associateテスト難易度 PDFバージョンは通常のファイルです。

問題集のメリットは以下のとおりです、復び山にのぼり給ふに、我々の専門家はこの分野での最新動向に注意を払いますので、これらの変化をData-Engineer-Associateテスト練習にすぐに追加します、Data-Engineer-Associate学習教材の合格率が99％であることは間違いありません。

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JPTestKing AmazonのData-Engineer-Associate試験スタディガイドはあなたのキャリアの灯台になれます、多くの候補者は彼らが準備に多大な時間をかけるのに、失敗したことを反映しましたが、弊社の有効なData-Engineer-Associate資格問題集を購入した後、2～3日の試験準備だけで素晴らしいスコアを取られます。

JPTestKingは今まで数え切Data-Engineer-AssociateれないIT認定試験の受験者を助けて、皆さんから高い評判をもらいました。