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How to Prepare For AWS Certified Machine Learning - Specialty

Preparation Guide for AWS Certified Machine Learning - Specialty

Introduction for AWS Certified Machine Learning - Specialty

The AWS Certified Machine Learning - Specialty (MLS-C01) examination is intended for individuals who perform a development or data science role. This exam validates an examinee's ability to build, train, tune, and deploy learning (ML) models using the AWS Cloud.

It validates an examinee's ability to design, implement, deploy, and maintain ML solutions for given business problems. It will validate the candidate's ability to:

Identify appropriate AWS services to implement ML solutions.
Select and justify the appropriate ML approach for a given business problem.
Design and implement scalable, cost-optimized, reliable, and secure ML solutions

The AWS Certified Machine Learning - Specialty certification is intended for individuals who perform a development or data science role. It validates a candidate's ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q313-Q318):

NEW QUESTION # 313
A Machine Learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar- sized datasets. The team's leaders need to accelerate the training process.
What can a Machine Learning Specialist do to address this concern?

A. Use Amazon Machine Learning to train the models.
B. Use Amazon Kinesis to stream the data to Amazon SageMaker.
C. Use Amazon SageMaker Pipe mode.
D. Use AWS Glue to transform the CSV dataset to the JSON format.

Answer: C

Explanation:
Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances. B would not apply in this scenario. C is a streaming ingestion solution, but is not applicable in this scenario. D transforms the data structure

NEW QUESTION # 314
A company is running an Amazon SageMaker training job that will access data stored in its Amazon S3 bucket A compliance policy requires that the data never be transmitted across the internet How should the company set up the job?

A. Launch the notebook instances in a public subnet and access the data through a NAT gateway
B. Launch the notebook instances in a private subnet and access the data through an S3 VPC endpoint.
C. Launch the notebook instances in a public subnet and access the data through the public S3 endpoint
D. Launch the notebook instances in a private subnet and access the data through a NAT gateway

Answer: B

NEW QUESTION # 315
A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000 Test set images = 100 (constant test set) The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

A. Increase the number of layers for the neural network.
B. Increase the number of epochs for model training.
C. Increase the dropout rate for the second-to-last layer.
D. Increase the training data by adding variation in rotation for training images.

Answer: B

NEW QUESTION # 316
A company wants to forecast the daily price of newly launched products based on 3 years of data for older product prices, sales, and rebates. The time-series data has irregular timestamps and is missing some values.
Data scientist must build a dataset to replace the missing values. The data scientist needs a solution that resamptes the data daily and exports the data for further modeling.
Which solution will meet these requirements with the LEAST implementation effort?

A. Use Amazon SageMaker Studio Data Wrangler.
B. Use Amazon SageMaker Studio Notebook with Pandas.
C. Use Amazon EMR Serveriess with PySpark.
D. Use AWS Glue DataBrew.

Answer: A

Explanation:
Amazon SageMaker Studio Data Wrangler is a visual data preparation tool that enables users to clean and normalize data without writing any code. Using Data Wrangler, the data scientist can easily import the time-series data from various sources, such as Amazon S3, Amazon Athena, or Amazon Redshift. Data Wrangler can automatically generate data insights and quality reports, which can help identify and fix missing values, outliers, and anomalies in the data. Data Wrangler also provides over 250 built-in transformations, such as resampling, interpolation, aggregation, and filtering, which can be applied to the data with a point-and-click interface. Data Wrangler can also export the prepared data to different destinations, such as Amazon S3, Amazon SageMaker Feature Store, or Amazon SageMaker Pipelines, for further modeling and analysis. Data Wrangler is integrated with Amazon SageMaker Studio, a web-based IDE for machine learning, which makes it easy to access and use the tool. Data Wrangler is a serverless and fully managed service, which means the data scientist does not need to provision, configure, or manage any infrastructure or clusters.
Option A is incorrect because Amazon EMR Serverless is a serverless option for running big data analytics applications using open-source frameworks, such as Apache Spark. However, using Amazon EMR Serverless would require the data scientist to write PySpark code to perform the data preparation tasks, such as resampling, imputation, and aggregation. This would require more implementation effort than using Data Wrangler, which provides a visual and code-free interface for data preparation.
Option B is incorrect because AWS Glue DataBrew is another visual data preparation tool that can be used to clean and normalize data without writing code. However, DataBrew does not support time-series data as a data type, and does not provide built-in transformations for resampling, interpolation, or aggregation of time-series data. Therefore, using DataBrew would not meet the requirements of the use case.
Option D is incorrect because using Amazon SageMaker Studio Notebook with Pandas would also require the data scientist to write Python code to perform the data preparation tasks. Pandas is a popular Python library for data analysis and manipulation, which supports time-series data and provides various methods for resampling, interpolation, and aggregation. However, using Pandas would require more implementation effort than using Data Wrangler, which provides a visual and code-free interface for data preparation.
References:
1: Amazon SageMaker Data Wrangler documentation
2: Amazon EMR Serverless documentation
3: AWS Glue DataBrew documentation
4: Pandas documentation

NEW QUESTION # 317
A data scientist uses Amazon SageMaker Data Wrangler to define and perform transformations and feature engineering on historical data. The data scientist saves the transformations to SageMaker Feature Store.
The historical data is periodically uploaded to an Amazon S3 bucket. The data scientist needs to transform the new historic data and add it to the online feature store The data scientist needs to prepare the .....historic data for training and inference by using native integrations.
Which solution will meet these requirements with the LEAST development effort?

A. Use AWS Lambda to run a predefined SageMaker pipeline to perform the transformations on each new dataset that arrives in the S3 bucket.
B. Run an AWS Step Functions step and a predefined SageMaker pipeline to perform the transformations on each new dalaset that arrives in the S3 bucket
C. Use Apache Airflow to orchestrate a set of predefined transformations on each new dataset that arrives in the S3 bucket.
D. Configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket.

Answer: D

Explanation:
Explanation
The best solution is to configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket. This solution requires the least development effort because it leverages the native integration between EventBridge and SageMaker Pipelines, which allows you to trigger a pipeline execution based on an event rule. EventBridge can monitor the S3 bucket for new data uploads and invoke the pipeline that contains the same transformations and feature engineering steps that were defined in SageMaker Data Wrangler. The pipeline can then ingest the transformed data into the online feature store for training and inference.
The other solutions are less optimal because they require more development effort and additional services.
Using AWS Lambda or AWS Step Functions would require writing custom code to invoke the SageMaker pipeline and handle any errors or retries. Using Apache Airflow would require setting up and maintaining an Airflow server and DAGs, as well as integrating with the SageMaker API.
References:
Amazon EventBridge and Amazon SageMaker Pipelines integration
Create a pipeline using a JSON specification
Ingest data into a feature group

NEW QUESTION # 318
......

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