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AWS-Certified-Machine-Learning-Specialty認證考試，AWS-Certified-Machine-Learning-Specialty認證

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AWS-Certified-Machine-Learning-Specialty 是一個占有一定比重的認證科目。由於人數太少，加上需求太大，導致擁有 AWS-Certified-Machine-Learning-Specialty 認證的人成為薪酬最高的資訊技術專業認證人員。由於技能是本身擁有的，加上在全球範圍內的幾乎所有國家都有類似需求。所以，Amazon 的 AWS-Certified-Machine-Learning-Specialty 認證為網路工程師打開了通往全球各地的大門。如果您通過了AWS-Certified-Machine-Learning-Specialty 的考試，將證明你的專業技能和貢獻，展示你的知識與能力。如果你被認證為一個 AWS-Certified-Machine-Learning-Specialty 網路公司的專家，你就會成為這個領域中最有知識的專家之一。

為了有資格參加AWS認證的機器學習 - 專業認證考試，考生必須至少有一年的AWS機器學習模型開發和部署經驗。他們還必須對AWS服務，如Amazon SageMaker、Amazon S3和Amazon EC2有著扎實的了解，並且有Python和R等編程語言的經驗。考試本身由65道多選和多響應問題組成，必須在3個小時內完成。通過考試的考生將獲得AWS認證的機器學習 - 專業認證，有效期為三年。

最新的 AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty 免費考試真題 (Q63-Q68):

問題 #63
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

• A. Mean absolute percentage error (MAPE)
• B. Misclassification rate
• C. Recall
• D. Area Under the ROC Curve (AUC)

答案：D

解題說明：
Area Under the ROC Curve (AUC) is a metric that measures the performance of a binary classifier across all possible thresholds. It is also known as the probability that a randomly chosen positive example will be ranked higher than a randomly chosen negative example by the classifier. AUC is a good metric to compare different classification models because it is independent of the class distribution and the decision threshold. It also captures both the sensitivity (true positive rate) and the specificity (true negative rate) of the model. References:
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Specialty Sample Questions

 

問題 #64
The displayed graph is from a foresting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

• A. The model predicts the seasonality well, but not the trend.
• B. The model does not predict the trend or the seasonality well.
• C. The model predicts both the trend and the seasonality well.
• D. The model predicts the trend well, but not the seasonality.

答案：D

 

問題 #65
A technology startup is using complex deep neural networks and GPU compute to recommend the company's products to its existing customers based upon each customer's habits and interactions. The solution currently pulls each dataset from an Amazon S3 bucket before loading the data into a TensorFlow model pulled from the company's Git repository that runs locally. This job then runs for several hours while continually outputting its progress to the same S3 bucket. The job can be paused, restarted, and continued at any time in the event of a failure, and is run from a central queue.
Senior managers are concerned about the complexity of the solution's resource management and the costs involved in repeating the process regularly. They ask for the workload to be automated so it runs once a week, starting Monday and completing by the close of business Friday.
Which architecture should be used to scale the solution at the lowest cost?

• A. Implement the solution using Amazon ECS running on Spot Instances and schedule the task using the ECS service scheduler
• B. Implement the solution using AWS Deep Learning Containers and run the container as a job using AWS Batch on a GPU-compatible Spot Instance
• C. Implement the solution using a low-cost GPU-compatible Amazon EC2 instance and use the AWS Instance Scheduler to schedule the task
• D. Implement the solution using AWS Deep Learning Containers, run the workload using AWS Fargate running on Spot Instances, and then schedule the task using the built-in task scheduler

答案：D

 

問題 #66
A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3 The source systems send data in CSV format in real lime The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3 Which solution takes the LEAST effort to implement?

• A. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convertdata into Parquet.
• B. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.
• C. Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use ApacheSpark to convert data into Parquet.
• D. Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 toserialize data as Parquet

答案：A

解題說明：
Amazon Kinesis Data Streams is a service that can capture, store, and process streaming data in real time.
Amazon Kinesis Data Firehose is a service that can deliver streaming data to various destinations, such as Amazon S3, Amazon Redshift, or Amazon Elasticsearch Service. Amazon Kinesis Data Firehose can also transform the data before delivering it, such as converting the data format, compressing the data, or encrypting the data. One of the supported data formats that Amazon Kinesis Data Firehose can convert to is Apache Parquet, which is a columnar storage format that can improve the performance and cost-efficiency of analytics queries. By using Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose, the Mobile Network Operator can ingest the .CSV data from the source systems and use Amazon Kinesis Data Firehose to convert the data into Parquet before storing it on Amazon S3. This solution takes the least effort to implement, as it does not require any additional resources, such as Amazon EC2 instances, Amazon EMR clusters, or Amazon Glue jobs. The solution can also leverage the built-in features of Amazon Kinesis Data Firehose, such as data buffering, batching, retry, and error handling.
Amazon Kinesis Data Streams - Amazon Web Services
Amazon Kinesis Data Firehose - Amazon Web Services
Data Transformation - Amazon Kinesis Data Firehose
Apache Parquet - Amazon Athena

 

問題 #67
A large JSON dataset for a project has been uploaded to a private Amazon S3 bucket The Machine Learning Specialist wants to securely access and explore the data from an Amazon SageMaker notebook instance A new VPC was created and assigned to the Specialist How can the privacy and integrity of the data stored in Amazon S3 be maintained while granting access to the Specialist for analysis?

• A. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled Use an S3 ACL to open read privileges to the everyone group
• B. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Define a custom S3 bucket policy to only allow requests from your VPC to access the S3 bucket
• C. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled. Generate an S3 pre-signed URL for access to data in the bucket
• D. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Copy the JSON dataset from Amazon S3 into the ML storage volume on the SageMaker notebook instance and work against the local dataset

答案：D

 

問題 #68
......

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