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Die Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) Prüfung ist darauf ausgelegt, die Fähigkeiten und das Wissen eines Kandidaten im Bereich des maschinellen Lernens auf der Amazon Web Services (AWS) Plattform zu validieren. Diese Zertifizierung richtet sich an Personen, die ein solides Verständnis von Konzepten des maschinellen Lernens haben und in der Lage sind, AWS-Services zur Implementierung und Bereitstellung von maschinellen Lernmodellen zu nutzen.

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Seit Neuem aktualisierte MLS-C01 Examfragen für Amazon MLS-C01 Prüfung

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Die AWS Certified Machine Learning - Specialty Certification Exam ist eine breite Palette von Themen ab, einschließlich Datenvorbereitung, Feature -Engineering, Modellauswahl und Optimierung, Algorithmen für maschinelles Lernen und Bereitstellungsstrategien. Die Kandidaten müssen ein tiefes Verständnis von Konzepten für maschinelles Lernen sowie die Fähigkeit demonstrieren, diese Konzepte mit AWS-Tools und -Diensten auf reale Szenarien anzuwenden.

Amazon AWS Certified Machine Learning - Specialty MLS-C01 Prüfungsfragen mit Lösungen (Q318-Q323):

318. Frage
A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.
Which of the following will accomplish this? (Select TWO.)

A. Download and apt-get install the inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.
B. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.
C. Customize the built-in image classification algorithm to use Inception and use this for model training.
D. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network and use this for model training.
E. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.

Antwort: D,E

Begründung:
The best options to use an Inception neural network architecture instead of a ResNet architecture for image classification in Amazon SageMaker are:
Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training. This option allows users to customize the training environment and use any TensorFlow model they want. Users can create a Docker image that contains the TensorFlow Estimator API and the Inception model from the TensorFlow Hub, and push it to Amazon ECR. Then, users can use the SageMaker Estimator class to train the model using the custom Docker image and the training data from Amazon S3.
Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network and use this for model training. This option allows users to use the built-in TensorFlow container provided by SageMaker and write custom code to load and train the Inception model. Users can use the TensorFlow Estimator class to specify the custom code and the training data from Amazon S3. The custom code can use the TensorFlow Hub module to load the Inception model and fine-tune it on the training data.
The other options are not feasible for this scenario because:
Customize the built-in image classification algorithm to use Inception and use this for model training. This option is not possible because the built-in image classification algorithm in SageMaker does not support customizing the neural network architecture. The built-in algorithm only supports ResNet models with different depths and widths.
Create a support case with the SageMaker team to change the default image classification algorithm to Inception. This option is not realistic because the SageMaker team does not provide such a service. Users cannot request the SageMaker team to change the default algorithm or add new algorithms to the built-in ones.
Download and apt-get install the inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker. This option is not advisable because it does not leverage the benefits of SageMaker, such as managed training and deployment, distributed training, and automatic model tuning. Users would have to manually install and configure the Inception network code and the TensorFlow framework on the EC2 instance, and run the training and inference code on the same instance, which may not be optimal for performance and scalability.
References:
Use Your Own Algorithms or Models with Amazon SageMaker
Use the SageMaker TensorFlow Serving Container
TensorFlow Hub

319. Frage
A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the team requires better performance How should the records be stored in Amazon S3 to improve query performance?

A. Compressed JSON
B. CSV files
C. RecordIO
D. Parquet files

Antwort: D

Begründung:
Parquet is a columnar storage format that can store data in a compressed and efficient way. Parquet files can improve query performance by reducing the amount of data that needs to be scanned, as only the relevant columns are read from the files. Parquet files can also support predicate pushdown, which means that the filtering conditions are applied at the storage level, further reducing the data that needs to be processed.
Parquet files are compatible with Amazon Athena, which can leverage the benefits of the columnar format and provide faster and cheaper queries. Therefore, the records should be stored in Parquet files in Amazon S3 to improve query performance.
References:
* Columnar Storage Formats - Amazon Athena
* Parquet SerDe - Amazon Athena
* Optimizing Amazon Athena Queries - Amazon Athena
* Parquet - Apache Software Foundation

320. Frage
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

A. Hyperparameters in a JSON array as documented for the algorithm used.
B. The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
C. The validation channel identifying the location of validation data on an Amazon S3 bucket.
D. The output path specifying where on an Amazon S3 bucket the trained model will persist.
E. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
F. The training channel identifying the location of training data on an Amazon S3 bucket.

Antwort: D,E,F

321. Frage
A company has a podcast platform that has thousands of users. The company implemented an algorithm to detect low podcast engagement based on a 10-minute running window of user events such as listening to.
pausing, and closing the podcast. A machine learning (ML) specialist is designing the ingestion process for these events. The ML specialist needs to transform the data to prepare the data for inference.
How should the ML specialist design the transformation step to meet these requirements with the LEAST operational effort?

A. Use Amazon Kinesis Data Streams to ingest event data. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to transform the most recent 10 minutes of data before inference.
B. Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data.Use AWS Lambda to transform the most recent 10 minutes of data before inference.
C. Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data.
Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to transform the most recent 10 minutes of data before inference.
D. Use Amazon Kinesis Data Streams to ingest event data. Store the data in Amazon S3 by using Amazon Data Firehose. Use AWS Lambda to transform the most recent 10 minutes of data before inference.

Antwort: A

Begründung:
In this scenario, Kinesis Data Streams efficiently ingests real-time event data, while Amazon Managed Service for Apache Flink (formerly Amazon Kinesis Data Analytics) is ideal for transforming and analyzing data in a continuous stream. Apache Flink allows processing of time-based windows, such as the 10-minute sliding window required here, with low operational overhead.
This combination provides an effective solution for low-latency data processing and transformation, meeting the requirements for preparing data for inference with minimal setup and serverless scalability.

322. Frage
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs What does the Specialist need to do1?

A. Organize the Docker container's file structure to execute on GPU instances.
B. Set the GPU flag in the Amazon SageMaker Create TrainingJob request body
C. Bundle the NVIDIA drivers with the Docker image
D. Build the Docker container to be NVIDIA-Docker compatible

Antwort: D

Begründung:
To leverage the NVIDIA GPUs on Amazon EC2 P3 instances, the Machine Learning Specialist needs to build the Docker container to be NVIDIA-Docker compatible. NVIDIA-Docker is a tool that enables GPU- accelerated containers to run on Docker. It automatically configures the container to access the NVIDIA drivers and libraries on the host system. The Specialist does not need to bundle the NVIDIA drivers with the Docker image, as they are already installed on the EC2 P3 instances. The Specialist does not need to organize the Docker container's file structure to execute on GPU instances, as this is not relevant for GPU compatibility. The Specialist does not need to set the GPU flag in the Amazon SageMaker Create TrainingJob request body, as this is only required for using Elastic Inference accelerators, not EC2 P3 instances.
References: NVIDIA-Docker, Using GPU-Accelerated Containers, Using Elastic Inference in Amazon SageMaker

323. Frage
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