さらに、Fast2test Data-Engineer-Associateダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1yEyxyGT57H993rT-CQZWMDuHtx9SHLfC
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| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion and Transformation | 34% | - Ingest data from various sources
- Transform and enrich data
|
| Data Operations and Support | 22% | - Automate operational tasks - Monitor and troubleshoot data pipelines
- Backup, restore, and disaster recovery |
| Data Store Management | 26% | - Optimize storage performance and cost - Manage data lifecycle and storage tiers - Design and implement data storage solutions
|
| Data Security and Governance | 18% | - Implement access control and authentication
- Enforce compliance and data governance
|
>> Data-Engineer-Associateテスト模擬問題集 <<
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質問 # 185
A company ingests data from multiple data sources and stores the data in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake.
The company needs to identify matching records even when the records do not have a common unique identifier.
Which solution will meet this requirement?
正解：C
解説：
FindMatches is a transform available in AWS Lake Formation that uses ML to discover duplicate records or related records that might not have a common unique identifier.
It can be integrated into an AWS Glue ETL job to perform deduplication or matching tasks.
FindMatches is highly effective in scenarios where records do not share a key, such as customer records from different sources that need to be merged or reconciled.
Explanation:
The problem described requires identifying matching records even when there is no unique identifier. AWS Lake Formation FindMatches is designed for this purpose. It uses machine learning (ML) to deduplicate and find matching records in datasets that do not share a common identifier.
Reference:
Alternatives Considered:
A (Amazon Made pattern matching): Amazon Made is not a service in AWS, and pattern matching typically refers to regular expressions, which are not suitable for deduplication without a common identifier.
B (AWS Glue PySpark Filter class): PySpark's Filter class can help refine datasets, but it does not offer the ML-based matching capabilities required to find matches between records without unique identifiers.
C (Partition tables on a unique identifier): Partitioning requires a unique identifier, which the question states is unavailable.
AWS Glue Documentation on Lake Formation FindMatches
FindMatches in AWS Lake Formation
質問 # 186
A company uses AWS Glue jobs to implement several data pipelines. The pipelines are critical to the company.
The company needs to implement a monitoring mechanism that will alert stakeholders if the pipelines fail.
Which solution will meet these requirements with the LEAST operational overhead?
正解：C
解説：
Creating an EventBridge rule that triggers a Lambda function on AWS Glue job failure events and then sends notifications via Amazon SNS is the most direct and operationally efficient method:
"Practice Quiz 10: A data engineer must monitor the data pipeline... Which solution will meet these requirements?
A: Inspect the job run monitoring section of the AWS Glue console.
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Although this reference directly supports using AWS Glue's monitoring features via EventBridge, it implies that solutions likeA-which directly use EventBridge failure events for automation-are more optimal and less complex than constructing custom logs and metrics.
質問 # 187
A company is using an AWS Transfer Family server to migrate data from an on-premises environment to AWS. Company policy mandates the use of TLS 1.2 or above to encrypt the data in transit.
Which solution will meet these requirements?
正解：D
質問 # 188
A company stores petabytes of data in thousands of Amazon S3 buckets in the S3 Standard storage class. The data supports analytics workloads that have unpredictable and variable data access patterns.
The company does not access some data for months. However, the company must be able to retrieve all data within milliseconds. The company needs to optimize S3 storage costs.
Which solution will meet these requirements with the LEAST operational overhead?
正解：B
解説：
S3 Intelligent-Tiering is a storage class that automatically moves objects between four access tiers based on the changing access patterns. The default access tier consists of two tiers: Frequent Access and Infrequent Access. Objects in the Frequent Access tier have the same performance and availability as S3 Standard, while objects in the Infrequent Access tier have the same performance and availability as S3 Standard-IA. S3 Intelligent-Tiering monitors the access patterns of each object and moves them between the tiers accordingly, without any operational overhead or retrieval fees. This solution can optimize S3 storage costs for data with unpredictable and variable access patterns, while ensuring millisecond latency for data retrieval. The other solutions are not optimal or relevant for this requirement. Using S3 Storage Lens standard metrics and activity metrics can provide insights into the storage usage and access patterns, but they do not automate the data movement between storage classes. Creating S3 Lifecycle policies for the S3 buckets can move objects to more cost-optimized storage classes, but they require manual configuration and maintenance, and they may incur retrieval fees for data that is accessed unexpectedly. Activating the Deep Archive Access tier for S3 Intelligent-Tiering can further reduce the storage costs for data that is rarely accessed, but it also increases the retrieval time to 12 hours, which does not meet the requirement of millisecond latency. References:
S3 Intelligent-Tiering
S3 Storage Lens
S3 Lifecycle policies
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]
質問 # 189
A company stores data in a data lake that is in Amazon S3. Some data that the company stores in the data lake contains personally identifiable information (PII). Multiple user groups need to access the raw data. The company must ensure that user groups can access only the PII that they require.
Which solution will meet these requirements with the LEAST effort?
正解：A
解説：
Amazon Athena is a serverless, interactive query service that enables you to analyze data in Amazon S3 using standard SQL. AWS Lake Formation is a service that helps you build, secure, and manage data lakes on AWS. You can use AWS Lake Formation to create data filters that define the level of access for different IAM roles based on the columns, rows, or tags of the data. By using Amazon Athena to query the data and AWS Lake Formation to create data filters, the company can meet the requirements of ensuring that user groups can access only the PII that they require with the least effort. The solution is to use Amazon Athena to query the data in the data lake that is in Amazon S3. Then, set up AWS Lake Formation and create data filters to establish levels of access for the company's IAM roles. For example, a data filter can allow a user group to access only the columns that contain the PII that they need, such as name and email address, and deny access to the columns that contain the PII that they do not need, such as phone number and social security number.
Finally, assign each user to the IAM role that matches the user's PII access requirements. This way, the user groups can access the data in the data lake securely and efficiently. The other options are either not feasible or not optimal. Using Amazon QuickSight to access the data (option B) would require the company to pay for the QuickSight service and to configure the column-level security features for each user. Building a custom query builder UI that will run Athena queries in the background to access the data (option C) would require the company to develop and maintain the UI and to integrate it with Amazon Cognito. Creating IAM roles that have different levels of granular access (option D) would require the company to manage multiple IAM roles and policies and to ensure that they are aligned with the data schema. References:
* Amazon Athena
* AWS Lake Formation
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Analysis and Visualization, Section 4.3: Amazon Athena
質問 # 190
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テストの準備に多くの時間を費やし、それでも何度も失敗するのは馬鹿げていますか？ 一部の受験者は、Amazon Data-Engineer-Associate試験ダンプ問題で簡単に試験に合格しますか？ 試験に合格し、認定を取得することが目標である場合、Data-Engineer-Associate試験ダンプは、目標を簡単に達成するのに役立ちます。選択してみませんか？ Data-Engineer-Associate試験ダンプ問題を含むテストの前にわずか数十のお金と20〜35時間の有効な準備で、確実に試験をクリアできます。 では、なぜあなたは無駄な努力をするのに多くの時間を無駄にしているのですか？
Data-Engineer-Associate日本語版テキスト内容: https://jp.fast2test.com/Data-Engineer-Associate-premium-file.html
さらに、Fast2test Data-Engineer-Associateダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1yEyxyGT57H993rT-CQZWMDuHtx9SHLfC