Amazon AWS-Certified-Machine-Learning-Specialty最新関連参考書: AWS Certified Machine Learning - Specialty - Xhs1991最新の更新

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Xhs1991電子機器の開発に伴い、Amazonパススルートレントの設計に多くの変更があります。最も印象的なバージョンは、APPオンラインバージョンです。通常、あらゆる種類のデジタルデバイスで使用できます。しかし、AWS-Certified-Machine-Learning-Specialtyオンラインではないときにオンラインバージョンを使用できるという特別な利点もあります。ネットワーク環境で初めて使用する場合は、どこからでもAmazon学習ガイドのオンラインバージョンを使用できます。ネットワーク接続なし。 AWS-Certified-Machine-Learning-Specialtyオンライン版はあなたにとって良い選択になると思います。また、このオンラインバージョンは実際のAWS Certified Machine Learning - Specialty試験環境をシミュレートできます。したがって、Amazonテストクイズを使用すると、試験に合格し、希望する証明書を取得できる可能性が高くなると思います。

被験者は定期的に計画を立て、自分の状況に応じて目標を設定し、研究を監視および評価することにより、学習者のプロフィールを充実させる必要があります。 AWS-Certified-Machine-Learning-Specialty試験の準備に役立つからです。試験に合格して関連する試験を受けるには、適切な学習プログラムを設定する必要があります。当社からAWS-Certified-Machine-Learning-Specialtyテストガイドを購入し、それを真剣に検討すると、最短時間でAWS-Certified-Machine-Learning-Specialty試験に合格するのに役立つ適切な学習プランが得られると考えています。

>> AWS-Certified-Machine-Learning-Specialty最新関連参考書 <<

AWS-Certified-Machine-Learning-Specialty試験対策、AWS-Certified-Machine-Learning-Specialty資格関連題

AWS-Certified-Machine-Learning-Specialty準備試験では、国内および海外の専門家と学者を取り入れた専門家のチームを集めて、関連する試験銀行の調査と設計を行い、受験者がAWS-Certified-Machine-Learning-Specialty試験に合格するのを支援します。ほとんどの専門家は長年プロの分野で勉強しており、AWS-Certified-Machine-Learning-Specialty練習問題で多くの経験を蓄積しています。当社は才能の選択にかなり慎重であり、夢のAWS-Certified-Machine-Learning-Specialty認定の取得を支援するために、専門知識とスキルを備えた従業員を常に雇用しています。

Amazon AWS Certified Machine Learning - Specialty 認定 AWS-Certified-Machine-Learning-Specialty 試験問題 (Q93-Q98):

質問 # 93
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)

A. Increase regularization.
B. Increase dropout.
C. Decrease regularization.
D. Decrease dropout.
E. Increase feature combinations.
F. Decrease feature combinations.

正解：B、C、E

質問 # 94
A data engineer needs to provide a team of data scientists with the appropriate dataset to run machine learning training jobs. The data will be stored in Amazon S3. The data engineer is obtaining the data from an Amazon Redshift database and is using join queries to extract a single tabular dataset. A portion of the schema is as follows:
...traction Timestamp (Timeslamp)
...JName(Varchar)
...JNo (Varchar)
Th data engineer must provide the data so that any row with a CardNo value of NULL is removed. Also, the TransactionTimestamp column must be separated into a TransactionDate column and a isactionTime column Finally, the CardName column must be renamed to NameOnCard.
The data will be extracted on a monthly basis and will be loaded into an S3 bucket. The solution must minimize the effort that is needed to set up infrastructure for the ingestion and transformation. The solution must be automated and must minimize the load on the Amazon Redshift cluster Which solution meets these requirements?

A. Set up an AWS Glue job that has the Amazon Redshift cluster as the source and the S3 bucket as the destination Use the built-in transforms Filter, Map. and RenameField to perform the required transformations. Schedule the job to run monthly.
B. Set up an Amazon EC2 instance with a SQL client tool, such as SQL Workbench/J. to query the data from the Amazon Redshift cluster directly. Export the resulting dataset into a We. Upload the file into the S3 bucket. Perform these tasks monthly.
C. Set up an Amazon EMR cluster Create an Apache Spark job to read the data from the Amazon Redshift cluster and transform the data. Load the data into the S3 bucket. Schedule the job to run monthly.
D. Use Amazon Redshift Spectrum to run a query that writes the data directly to the S3 bucket. Create an AWS Lambda function to run the query monthly

正解：A

解説：
The best solution for this scenario is to set up an AWS Glue job that has the Amazon Redshift cluster as the source and the S3 bucket as the destination, and use the built-in transforms Filter, Map, and RenameField to perform the required transformations. This solution has the following advantages:
* It minimizes the effort that is needed to set up infrastructure for the ingestion and transformation, as AWS Glue is a fully managed service that provides a serverless Apache Spark environment, a graphical interface to define data sources and targets, and a code generation feature to create and edit scripts1.
* It automates the extraction and transformation process, as AWS Glue can schedule the job to run monthly, and handle the connection, authentication, and configuration of the Amazon Redshift cluster and the S3 bucket2.
* It minimizes the load on the Amazon Redshift cluster, as AWS Glue can read the data from the cluster in parallel and use a JDBC connection that supports SSL encryption3.
* It performs the required transformations, as AWS Glue can use the built-in transforms Filter, Map, and RenameField to remove the rows with NULL values, split the timestamp column into date and time columns, and rename the card name column, respectively4.
The other solutions are not optimal or suitable, because they have the following drawbacks:
* A: Setting up an Amazon EMR cluster and creating an Apache Spark job to read the data from the Amazon Redshift cluster and transform the data is not the most efficient or convenient solution, as it requires more effort and resources to provision, configure, and manage the EMR cluster, and to write and maintain the Spark code5.
* B: Setting up an Amazon EC2 instance with a SQL client tool to query the data from the Amazon Redshift cluster directly and export the resulting dataset into a CSV file is not a scalable or reliable solution, as it depends on the availability and performance of the EC2 instance, and the manual execution and upload of the SQL queries and the CSV file6.
* D: Using Amazon Redshift Spectrum to run a query that writes the data directly to the S3 bucket and creating an AWS Lambda function to run the query monthly is not a feasible solution, as Amazon Redshift Spectrum does not support writing data to external tables or S3 buckets, only reading data from them7.
1: What Is AWS Glue? - AWS Glue
2: Populating the Data Catalog - AWS Glue
3: Best Practices When Using AWS Glue with Amazon Redshift - AWS Glue
4: Built-In Transforms - AWS Glue
5: What Is Amazon EMR? - Amazon EMR
6: Amazon EC2 - Amazon Web Services (AWS)
7: Using Amazon Redshift Spectrum to Query External Data - Amazon Redshift

質問 # 95
A retail company is ingesting purchasing records from its network of 20,000 stores to Amazon S3 by using Amazon Kinesis Data Firehose. The company uses a small, server-based application in each store to send the data to AWS over the internet. The company uses this data to train a machine learning model that is retrained each day. The company's data science team has identified existing attributes on these records that could be combined to create an improved model.
Which change will create the required transformed records with the LEAST operational overhead?

A. Create an AWS Lambda function that can transform the incoming records. Enable data transformation on the ingestion Kinesis Data Firehose delivery stream. Use the Lambda function as the invocation target.
B. Deploy an Amazon S3 File Gateway in the stores. Update the in-store software to deliver data to the S3 File Gateway. Use a scheduled daily AWS Glue job to transform the data that the S3 File Gateway delivers to Amazon S3.
C. Deploy an Amazon EMR cluster that runs Apache Spark and includes the transformation logic. Use Amazon EventBridge (Amazon CloudWatch Events) to schedule an AWS Lambda function to launch the cluster each day and transform the records that accumulate in Amazon S3. Deliver the transformed records to Amazon S3.
D. Launch a fleet of Amazon EC2 instances that include the transformation logic. Configure the EC2 instances with a daily cron job to transform the records that accumulate in Amazon S3. Deliver the transformed records to Amazon S3.

正解：A

解説：
The solution A will create the required transformed records with the least operational overhead because it uses AWS Lambda and Amazon Kinesis Data Firehose, which are fully managed services that can provide the desired functionality. The solution A involves the following steps:
* Create an AWS Lambda function that can transform the incoming records. AWS Lambda is a service that can run code without provisioning or managing servers. AWS Lambda can execute the transformation logic on the purchasing records and add the new attributes to the records1.
* Enable data transformation on the ingestion Kinesis Data Firehose delivery stream. Use the Lambda function as the invocation target. Amazon Kinesis Data Firehose is a service that can capture, transform, and load streaming data into AWS data stores. Amazon Kinesis Data Firehose can enable data transformation and invoke the Lambda function to process the incoming records before delivering them to Amazon S3. This can reduce the operational overhead of managing the transformation process and the data storage2.
The other options are not suitable because:
* Option B: Deploying an Amazon EMR cluster that runs Apache Spark and includes the transformation logic, using Amazon EventBridge (Amazon CloudWatch Events) to schedule an AWS Lambda function to launch the cluster each day and transform the records that accumulate in Amazon S3, and delivering the transformed records to Amazon S3 will incur more operational overhead than using AWS Lambda and Amazon Kinesis Data Firehose. The company will have to manage the Amazon EMR cluster, the Apache Spark application, the AWS Lambda function, and the Amazon EventBridge rule. Moreover, this solution will introduce a delay in the transformation process, as it will run only once a day3.
* Option C: Deploying an Amazon S3 File Gateway in the stores, updating the in-store software to deliver data to the S3 File Gateway, and using a scheduled daily AWS Glue job to transform the data that the S3 File Gateway delivers to Amazon S3 will incur more operational overhead than using AWS Lambda and Amazon Kinesis Data Firehose. The company will have to manage the S3 File Gateway, the in-store software, and the AWS Glue job. Moreover, this solution will introduce a delay in the transformation process, as it will run only once a day4.
* Option D: Launching a fleet of Amazon EC2 instances that include the transformation logic, configuring the EC2 instances with a daily cron job to transform the records that accumulate in Amazon S3, and delivering the transformed records to Amazon S3 will incur more operational overhead than using AWS Lambda and Amazon Kinesis Data Firehose. The company will have to manage the EC2 instances, the transformation code, and the cron job. Moreover, this solution will introduce a delay in the transformation process, as it will run only once a day5.
1: AWS Lambda
2: Amazon Kinesis Data Firehose
3: Amazon EMR
4: Amazon S3 File Gateway
5: Amazon EC2

質問 # 96
A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week.
Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3.
Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.)

A. Only 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.
B. The sales data is missing zero entries for item sales. Request that item sales data from the source database include zero entries to enable building the model.
C. The sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.
D. Detecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.
E. Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.

正解：B、E

解説：
The factors that will adversely impact the performance of the forecast model are:
* Sales data is aggregated by week. This will reduce the granularity and resolution of the data, and make it harder to capture the daily patterns and variations in sales volume. The data scientist should request daily sales data from the source database to enable building a daily model, which will be more accurate and useful for the prediction task.
* Sales data is missing zero entries for item sales. This will introduce bias and incompleteness in the data, and make it difficult to account for the items that have no demand or are out of stock. The data scientist should request that item sales data from the source database include zero entries to enable building the model, which will be more robust and realistic.
The other options are not valid because:
* Detecting seasonality for the majority of stores will not be an issue, as sales data is highly consistent from week to week. Requesting categorical data to relate new stores with similar stores that have more historical data may not improve the model performance significantly, and may introduce unnecessary complexity and noise.
* The sales data does not need to have more variance, as it reflects the actual demand and behavior of the customers. Requesting external sales data from other industries will not improve the model's ability to generalize, but may introduce irrelevant and misleading information.
* Only 100 MB of sales data is not a problem, as it is sufficient to train a forecast model with Amazon S3 and Amazon Forecast. Requesting 10 years of sales data will not provide much benefit, as it may contain outdated and obsolete information that does not reflect the current market trends and customer preferences.
Amazon Forecast
Forecasting: Principles and Practice

質問 # 97
A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset.
The company requires at least 85% accuracy to make use of the model.
After an exhaustive grid search, the optimal hyperparameters produced the following:
68% accuracy on the training set
67% accuracy on the validation set
What can the machine learning specialist do to improve the system's accuracy?

A. Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.
B. Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.
C. Add more data to the training set and retrain the model using transfer learning to reduce the bias.
D. Train a new model using the current neural network architecture.

正解：C

解説：
The problem described in the question is a case of underfitting, where the neural network model performs poorly on both the training and validation sets. This means that the model has not learned the features of the data well enough and has high bias. To solve this issue, the machine learning specialist should consider the following change:
Add more data to the training set and retrain the model using transfer learning to reduce the bias: Adding more data to the training set can help the model learn more patterns and variations in the data and improve its performance. Transfer learning can also help the model leverage the knowledge from the pre-trained network and adapt it to the new data. This can reduce the bias and increase the accuracy of the model.
References:
Transfer learning for TensorFlow image classification models in Amazon SageMaker Transfer learning for custom labels using a TensorFlow container and "bring your own algorithm" in Amazon SageMaker Machine Learning Concepts - AWS Training and Certification

質問 # 98
......

Amazon特別で適切に設計されたAWS-Certified-Machine-Learning-Specialty試験資料を所有しているだけでなく、想像を超えた幅広いサービスを提供できるため、人気があります。最初は、信頼できる制作チームがあり、AWS-Certified-Machine-Learning-Specialty学習ガイドは何百人もの専門家によって改訂されています。つまり、シラバスと最新の変更に応じて、オーダーメイドのAWS-Certified-Machine-Learning-Specialty学習教材を受け取ることができます。理論とブレークスルーの開発。間違いなく、当社のAWS-Certified-Machine-Learning-Specialty練習トレントは最新の情報に対応しています。

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有効的なAmazon AWS-Certified-Machine-Learning-Specialty最新関連参考書 & 合格スムーズAWS-Certified-Machine-Learning-Specialty試験対策 | 実際的なAWS-Certified-Machine-Learning-Specialty資格関連題

Xhs1991がそばのいてあげたら、全ての難問が解決できまAWS-Certified-Machine-Learning-Specialty難易度受験料す、時間は完全に利用されています、知識は、将来価値のある報酬を提供できる無形資産と定義されているため、neverめないでください。

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Amazon AWS-Certified-Machine-Learning-Specialty最新関連参考書: AWS Certified Machine Learning - Specialty - Xhs1991最新の更新

AWS-Certified-Machine-Learning-Specialty試験対策、AWS-Certified-Machine-Learning-Specialty資格関連題

Amazon AWS Certified Machine Learning - Specialty 認定 AWS-Certified-Machine-Learning-Specialty 試験問題 (Q93-Q98):

有効的なAmazon AWS-Certified-Machine-Learning-Specialty最新関連参考書 & 合格スムーズAWS-Certified-Machine-Learning-Specialty試験対策 | 実際的なAWS-Certified-Machine-Learning-Specialty資格関連題

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