Open the auxiliary access

Firefly Open Source Community

   Login   |   Register   |
New_Topic
Firefly Open Source Community › Embedded Board › AIO-3288C› もしあなたはまだAmazonのMLA-C01試験に合格するのため ...
New Topic
Print Previous Topic Next Topic

[General] もしあなたはまだAmazonのMLA-C01試験に合格するのために悩まれば

rickmar824

131

Credits

0

Prestige

0

Contribution

registered members

Rank: 2

Credits
131

【General】 もしあなたはまだAmazonのMLA-C01試験に合格するのために悩まれば

Posted at 12 hour before      View：20 | Replies：0        Print      Only Author   [Copy Link] 1#
2026年JPTestKingの最新MLA-C01 PDFダンプおよびMLA-C01試験エンジンの無料共有：https://drive.google.com/open?id=1cb9Ih-bd9C9B5ahq2xQuxu0KM29vhAw_
ほとんどの時間インターネットにアクセスできない場合、どこかに行く必要がある場合はオフライン状態ですが、MLA-C01試験のために学習したい場合。心配しないでください、私たちの製品はあなたの問題を解決するのに役立ちます。最新のMLA-C01試験トレントは、能力を強化し、試験に合格し、認定を取得するのに非常に役立つと確信しています。嫌がらせから抜け出すために、MLA-C01学習教材は高品質で高い合格率を備えています。だから、今すぐ行動しましょう！ MLA-C01クイズ準備を使用してください。
JPTestKing現在、MLA-C01証明書は、特定の分野で職務を遂行する能力があり、優れた労働能力を高めていることを証明できるため、求職者にとってますます重要になっています。 MLA-C01認定試験に合格すると、より良い仕事を見つけて高い給料を得ることができます。この目標により、最高のMLA-C01試験トレントをクライアントに提供し、MLA-C01練習エンジンを購入すると、クライアントがMLA-C01試験に簡単に合格できるようにします。
MLA-C01資格トレーニング、MLA-C01日本語参考商品を購入するとき、信頼できる会社を選ぶことができます。我々JPTestKingはAmazonのMLA-C01試験の最高の通過率を保証してAmazonのMLA-C01ソフトの無料のデモと一年間の無料更新を承諾します。あなたに安心させるために、我々はあなたがAmazonのMLA-C01試験に失敗したら全額で返金するのを保証します。JPTestKingはあなたのAmazonのMLA-C01試験を準備する間あなたの最もよい友達です。
Amazon MLA-C01 認定試験の出題範囲：
トピック出題範囲
トピック 1
  • 機械学習（ML）のためのデータ準備：この試験セクションでは、フォレンジックデータアナリストのスキルを評価し、機械学習用のデータの収集、保存、準備について扱います。様々なデータ形式、取り込み方法、そしてデータの処理と変換に使用されるAWSツールの理解に重点が置かれます。受験者は、不正分析のコンテキストにおいて高品質なデータセットを準備するために不可欠な、特徴量のクリーニングとエンジニアリング、データの整合性の確保、そしてバイアスやコンプライアンスの問題への対処を行うことが求められます。
トピック 2
  • 機械学習モデル開発：この試験セクションでは、不正検査官のスキルを測定し、不正検出などのビジネス課題を解決するための機械学習モデルの選択とトレーニングについて学びます。アルゴリズムの選択、組み込みモデルまたはカスタムモデルの使用、パラメータの調整、標準指標によるパフォーマンス評価などが含まれます。この分野では、過剰適合を回避するためのモデルの改良と、継続的な調査と監査証跡をサポートするためのバージョン管理の維持に重点が置かれています。
トピック 3
  • MLワークフローのデプロイメントとオーケストレーション：このセクションでは、フォレンジックデータアナリストのスキルを測定し、機械学習モデルの本番環境へのデプロイメントに焦点を当てます。適切なインフラストラクチャの選択、コンテナの管理、スケーリングの自動化、CI
  • CDパイプラインを介したワークフローのオーケストレーションなど、幅広い分野を網羅しています。受験者は、実世界の不正検出システムにおいて、一貫したデプロイメントと効率的な再トレーニングサイクルをサポートする環境を構築し、スクリプトを作成できる必要があります。
トピック 4
  • 機械学習ソリューションの監視、保守、セキュリティ：この試験セクションでは、不正検査官のスキルを測定し、機械学習モデルの監視、インフラストラクチャコストの管理、セキュリティのベストプラクティスの適用能力を評価します。モデルパフォーマンスの追跡設定、ドリフトの検出、ログ記録とアラートのためのAWSツールの使用などが含まれます。受験者は、アクセス制御の設定、環境の監査、金融不正検出などの機密データ環境におけるコンプライアンスの維持についてもテストされます。

Amazon AWS Certified Machine Learning Engineer - Associate 認定 MLA-C01 試験問題 (Q147-Q152):質問 # 147
A company is using Amazon SageMaker to develop ML models. The company stores sensitive training data in an Amazon S3 bucket. The model training must have network isolation from the internet.
Which solution will meet this requirement?
  • A. Run the SageMaker training jobs in public subnets that have an attached security group. In the security group, use inbound rules to limit traffic from the internet. Encrypt SageMaker instance storage by using server-side encryption with AWS KMS keys (SSE-KMS).
  • B. Run the SageMaker training jobs in private subnets. Create an S3 gateway VPC endpoint. Route traffic for training through the S3 gateway VPC endpoint.
  • C. Encrypt traffic to Amazon S3 by using a bucket policy that includes a value of True for the aws:SecureTransport condition key. Use default at-rest encryption for Amazon S3. Encrypt SageMaker instance storage by using server-side encryption with AWS KMS keys (SSE-KMS).
  • D. Run the SageMaker training jobs in private subnets. Create a NAT gateway. Route traffic for training through the NAT gateway.
正解：B

質問 # 148
A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes.
Which solution on AWS will detect duplicates in the dataset with the LEAST code development?
  • A. Use Amazon QuickSight ML Insights to build a custom deduplication model.
  • B. Use Amazon SageMaker Data Wrangler to pre-process and detect duplicates.
  • C. Use the AWS Glue FindMatches transform to detect duplicates.
  • D. Use Amazon Mechanical Turk jobs to detect duplicates.
正解：C

質問 # 149
A digital media entertainment company needs real-time video content moderation to ensure compliance during live streaming events.
Which solution will meet these requirements with the LEAST operational overhead?
  • A. Use Amazon SageMaker AI to extract and analyze the metadata from the videos' image frames.
  • B. Use Amazon Rekognition and AWS Lambda to extract and analyze the metadata from the videos' image frames.
  • C. Use Amazon Transcribe and Amazon Comprehend to extract and analyze the metadata from the videos' image frames.
  • D. Use Amazon Rekognition and a large language model (LLM) hosted on Amazon Bedrock to extract and analyze the metadata from the videos' image frames.
正解：B
解説：
For real-time video content moderation with minimal operational overhead, AWS documentation recommends using fully managed, purpose-built AI services. Amazon Rekognition provides real-time video analysis capabilities, including content moderation, unsafe content detection, and label recognition for live video streams.
By integrating Rekognition with AWS Lambda, the company can automatically process video frames, extract moderation metadata, and take immediate action (such as flagging or stopping a stream) without managing servers, models, or infrastructure. This serverless architecture scales automatically and minimizes operational complexity.
Option B introduces unnecessary complexity. While Amazon Bedrock LLMs are powerful, they are not required for image-based moderation tasks that Rekognition already handles natively.
Option C is incorrect because using Amazon SageMaker would require model training, endpoint management, and scaling, significantly increasing operational overhead.
Option D is incorrect because Amazon Transcribe and Amazon Comprehend are designed for audio and text analysis, not image or video frame moderation.
Therefore, Amazon Rekognition with AWS Lambda is the most efficient, scalable, and low-maintenance solution for real-time video moderation during live streaming events.

質問 # 150
A travel company wants to create an ML model to recommend the next airport destination for its users. The company has collected millions of data records about user location, recent search history on the company's website, and 2,000 available airports. The data has several categorical features with a target column that is expected to have a high-dimensional sparse matrix.
The company needs to use Amazon SageMaker AI built-in algorithms for the model. An ML engineer converts the categorical features by using one-hot encoding.
Which algorithm should the ML engineer implement to meet these requirements?
  • A. Use the CatBoost algorithm to recommend the next airport destination.
  • B. Use the k-means algorithm to cluster users into groups and map each group to the next airport destination.
  • C. Use the Factorization Machines algorithm to recommend the next airport destination.
  • D. Use the DeepAR forecasting algorithm to recommend the next airport destination.
正解：C
解説：
This problem describes a recommendation system with millions of records, many categorical variables, and a high-dimensional sparse feature space created by one-hot encoding. AWS documentation explicitly recommends Amazon SageMaker Factorization Machines (FM) for such use cases.
Factorization Machines are designed to handle sparse datasets efficiently and to model interactions between categorical features without explicitly enumerating all feature combinations. This capability makes FM particularly well-suited for recommendation problems such as predicting user-item interactions, including destination recommendations.
With 2,000 possible airport destinations, the target space is large and sparse. One-hot encoding further increases sparsity. Factorization Machines address this challenge by learning latent factors that capture relationships between features, even when many feature combinations are rarely observed.
Option A (CatBoost) is not an Amazon SageMaker built-in algorithm and therefore does not meet the requirement. Option B (DeepAR) is a time-series forecasting algorithm, not intended for recommendation or classification problems. Option D (k-means) is an unsupervised clustering algorithm and cannot directly predict a specific destination label.
AWS documentation explicitly lists recommendation systems and click prediction as primary use cases for the SageMaker Factorization Machines algorithm.
Therefore, Option C is the correct and AWS-verified choice.

質問 # 151
A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.
Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
* An S3 event notification invokes the pipeline when new data is uploaded.
* S3 Lifecycle rule invokes the pipeline when new data is uploaded.
* SageMaker retrains the model by using the data in the S3 bucket.
* The pipeline deploys the model to a SageMaker endpoint.
* The pipeline deploys the model to SageMaker Model Registry.

正解：
解説：

Explanation:
Step 1: An S3 event notification invokes the pipeline when new data is uploaded.Step 2: SageMaker retrains the model by using the data in the S3 bucket.Step 3: The pipeline deploys the model to a SageMaker endpoint.

* Step 1: An S3 Event Notification Invokes the Pipeline When New Data is Uploaded
* Why?The CI/CD pipeline should be triggered automatically whenever new training data is uploaded to Amazon S3. S3 event notifications can be configured to send events to AWS services like Lambda, which can then invoke AWS CodePipeline.
* How?Configure the S3 bucket to send event notifications (e.g., s3:ObjectCreated:*) to AWS Lambda, which in turn triggers the CodePipeline.
* Step 2: SageMaker Retrains the Model by Using the Data in the S3 Bucket
* Why?The uploaded data is used to retrain the ML model to incorporate new information and maintain performance. This step is critical to updating the model with fresh data.
* How?Define a SageMaker training step in the CI/CD pipeline, which reads the training data from the S3 bucket and retrains the model.
* Step 3: The Pipeline Deploys the Model to a SageMaker Endpoint
* Why?Once retrained, the updated model must be deployed to a SageMaker endpoint to make it available for real-time inference.
* How?Add a deployment step in the CI/CD pipeline, which automates the creation or update of the SageMaker endpoint with the retrained model.
Order Summary:
* An S3 event notification invokes the pipeline when new data is uploaded.
* SageMaker retrains the model by using the data in the S3 bucket.
* The pipeline deploys the model to a SageMaker endpoint.
This configuration ensures an automated, efficient, and scalable CI/CD pipeline for continuous retraining and deployment of the ML model in Amazon SageMaker.

質問 # 152
......
利用可能な時間、現在の知識レベルなどの状況に基づいて、MLA-C01学習教材は適切な計画と学習教材を作成します。利用可能な場合はMLA-C01テストの質問を使用して、各使用の効率を確認することができます。これは非常に良い効果があります。あなた自身や他の何かについて心配する必要はありません。 MLA-C01学習教材を使用すると、いつでも学習できます。また、MLA-C01ラーニングガイドを使用すると、最小限の時間と労力でMLA-C01試験に合格できます。
MLA-C01資格トレーニング: https://www.jptestking.com/MLA-C01-exam.html
2026年JPTestKingの最新MLA-C01 PDFダンプおよびMLA-C01試験エンジンの無料共有：https://drive.google.com/open?id=1cb9Ih-bd9C9B5ahq2xQuxu0KM29vhAw_
https://www.itbraindumps.com
Reply

Use props Report

Return to List
High mode
B Color Image Link Quote Code Smilies
You need to log in before you can reply Login | Register

This forum Credits Rules

  •  

Service Hotline : (+86)18688117175
Contact us
E-mail : global@t-firefly.com
Copyright © 2014-2022, FIREFLY TECHNOLOGY CO.,LTD. All Rightts Reserved. 粤ICP备14022046号-2
Quick Reply Back to top Back to list