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Amazon MLS-C01 考試大綱：
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綜合全面最新MLS-C01題庫資訊，最好的考試題庫幫助妳壹次性通過MLS-C01考試
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最新的 AWS Certified Specialty MLS-C01 免費考試真題 (Q26-Q31):
問題 #26
A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.
How should the data scientist split the dataset into a training and test set for this use case?
- A. Randomly select 10% of the users. Split off all interaction data from these users for the test set.
- B. Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.
- C. Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.
- D. Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.
答案：D
問題 #27
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 time. 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 using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet
- B. Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.
- C. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.
- D. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.
答案：C
問題 #28
A retail company uses a machine learning (ML) model for daily sales forecasting. The company's brand manager reports that the model has provided inaccurate results for the past 3 weeks.
At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company's ML team is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model's inaccuracies.
What should the ML team do on the SageMaker Studio notebook to visualize the model's degradation MOST accurately?
- A. Create a histogram of the model errors over the last 3 weeks. In addition, create a histogram of the model errors from before that period.
- B. Create a line chart with the weekly mean absolute error (MAE) of the model.
- C. Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition, create a scatter plot of daily sales versus model error from before that period.
- D. Create a histogram of the daily sales over the last 3 weeks. In addition, create a histogram of the daily sales from before that period.
答案：B
問題 #29
A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users.
The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company's business goals. To measure long-term effectiveness, the team wants to run multiple served by the models.
Which solution satisfies these requirements with MINIMAL effort?
- A. Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.
- B. Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices. Programmatically control which model is invoked for inference based on the medical device type.
- C. Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.
- D. Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer.
答案：C
解題說明：
A/B testing with Amazon SageMaker is required in the Exam. In A/B testing, you test different variants of your models and compare how each variant performs. Amazon SageMaker enables you to test multiple models or model versions behind the `same endpoint` using `production variants`. Each production variant identifies a machine learning (ML) model and the resources deployed for hosting the model. To test multiple models by `distributing traffic` between them, specify the `percentage of the traffic` that gets routed to each model by specifying the `weight` for each `production variant` in the endpoint configuration.
https://docs.aws.amazon.com/sagemaker/latest/dg/model-ab-testing.html#model-testing-target-variant
問題 #30
A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?
- A. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
- B. Create a NAT gateway within the corporate VPC.
- C. Create VPC peering with Amazon VPC hosting Amazon SageMaker.
- D. Route Amazon SageMaker traffic through an on-premises network.
答案：B
解題說明：
https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf (46)
問題 #31
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