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Der Erhalt der Amazon MLS-C01-Zertifizierung zeigt Ihre Fähigkeit, komplexe Machine-Learning-Probleme mit AWS-Services zu lösen. Diese Zertifizierung validiert Ihre Fähigkeiten im Designen, Entwickeln und Bereitstellen von Machine-Learning-Modellen auf der AWS-Cloud-Plattform. Durch den Erwerb dieser Zertifizierung können Sie Ihr Fachwissen potenziellen Arbeitgebern und Kunden präsentieren und Ihre Karrieremöglichkeiten im Bereich des Machine-Learnings erhöhen.

Die Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Zertifizierungsprüfung ist eine ausgezeichnete Wahl für IT-Profis, die ihre Fähigkeiten im Bereich Machine Learning auf der AWS-Plattform validieren möchten. Die Prüfung deckt eine Vielzahl von Themen ab und ist darauf ausgelegt, das Wissen, die Fähigkeiten und die Fertigkeiten der Kandidaten in der Entwicklung und Bereitstellung von Machine Learning-Lösungen auf AWS zu testen. Durch den Erwerb dieser Zertifizierung können sich Kandidaten von ihren Kollegen abheben und neue Karrieremöglichkeiten im Bereich Machine Learning eröffnen.

>> AWS-Certified-Machine-Learning-Specialty Fragen Antworten <<

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Die Amazon MLS-C01-Prüfung umfasst eine Vielzahl von Themen im Zusammenhang mit dem maschinellen Lernen, einschließlich der Datenbereitung, der Feature-Entwicklung, der Modellwahl, dem Modelltraining und der Modellbereitstellung. Es behandelt auch Themen im Zusammenhang mit AWS-Services und -Tools, die im Maschinenlernen üblicherweise verwendet werden, wie Amazon SageMaker, Amazon Rekognition, Amazon Comprehend und Amazon Polly. Die Kandidaten sollten ein tiefes Verständnis für diese Themen haben und praktische Erfahrung mit diesen AWS-Services und -Tools haben.

Amazon AWS Certified Machine Learning - Specialty AWS-Certified-Machine-Learning-Specialty Prüfungsfragen mit Lösungen (Q21-Q26):

21. Frage
A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream. As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.
Which next step is MOST likely to improve the data ingestion rate into Amazon S3?

• A. Decrease the retention period for the data stream.
• B. Increase the number of S3 prefixes for the delivery stream to write to.
• C. Add more consumers using the Kinesis Client Library (KCL).
• D. Increase the number of shards for the data stream.

Antwort: D

 

22. Frage
A large consumer goods manufacturer has the following products on sale:
* 34 different toothpaste variants
* 48 different toothbrush variants
* 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3. Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products. The company wants to predict the demand for a new product that will soon be launched.
Which solution should a Machine Learning Specialist apply?

• A. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product.
• B. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
• C. Train a custom ARIMA model to forecast demand for the new product.
• D. Train a custom XGBoost model to forecast demand for the new product.

Antwort: A

Begründung:
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.
Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html

 

23. Frage
A manufacturing company asks its Machine Learning Specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100000 images per defect type for training During the injial training of the image classification model the Specialist notices that the validation accuracy is 80%, while the training accuracy is 90% It is known that human-level performance for this type of image classification is around 90% What should the Specialist consider to fix this issue1?

• A. Making the network larger
• B. Using a different optimizer
• C. Using some form of regularization
• D. A longer training time

Antwort: C

Begründung:
Explanation
Regularization is a technique that can be used to prevent overfitting and improve model performance on unseen data. Overfitting occurs when the model learns the training data too well and fails to generalize to new and unseen data. This can be seen in the question, where the validation accuracy is lower than the training accuracy, and both are lower than the human-level performance. Regularization is a way of adding some constraints or penalties to the model to reduce its complexity and prevent it from memorizing the training data.
Some common forms of regularization for image classification are:
Weight decay: Adding a term to the loss function that penalizes large weights in the model. This can help reduce the variance and noise in the model and make it more robust to small changes in the input.
Dropout: Randomly dropping out some units or connections in the model during training. This can help reduce the co-dependency among the units and make the model more resilient to missing or corrupted features.
Data augmentation: Artificially increasing the size and diversity of the training data by applying random transformations, such as cropping, flipping, rotating, scaling, etc. This can help the model learn more invariant and generalizable features and reduce the risk of overfitting to specific patterns in the training data.
The other options are not likely to fix the issue of overfitting, and may even worsen it:
A longer training time: This can lead to more overfitting, as the model will have more chances to fit the noise and details in the training data that are not relevant for the validation data.
Making the network larger: This can increase the model capacity and complexity, which can also lead to more overfitting, as the model will have more parameters to learn and adjust to the training data.
Using a different optimizer: This can affect the speed and stability of the training process, but not necessarily the generalization ability of the model. The choice of optimizer depends on the characteristics of the data and the model, and there is no guarantee that a different optimizer will prevent overfitting.
References:
Regularization (machine learning)
Image Classification: Regularization
How to Reduce Overfitting With Dropout Regularization in Keras

 

24. Frage
A retail company stores 100 GB of daily transactional data in Amazon S3 at periodic intervals. The company wants to identify the schema of the transactional data. The company also wants to perform transformations on the transactional data that is in Amazon S3.
The company wants to use a machine learning (ML) approach to detect fraud in the transformed data.
Which combination of solutions will meet these requirements with the LEAST operational overhead? {Select THREE.)

• A. Use AWS Glue workflows and AWS Glue jobs to perform data transformations.
• B. Use Amazon Redshift ML to train a model to detect fraud.
• C. Use AWS Glue crawlers to scan the data and identify the schema.
• D. Use Amazon Fraud Detector to train a model to detect fraud.
• E. Use Amazon Redshift to store procedures to perform data transformations
• F. Use Amazon Athena to scan the data and identify the schema.

Antwort: A,C,D

Begründung:
To meet the requirements with the least operational overhead, the company should use AWS Glue crawlers, AWS Glue workflows and jobs, and Amazon Fraud Detector. AWS Glue crawlers can scan the data in Amazon S3 and identify the schema, which is then stored in the AWS Glue Data Catalog. AWS Glue workflows and jobs can perform data transformations on the data in Amazon S3 using serverless Spark or Python scripts. Amazon Fraud Detector can train a model to detect fraud using the transformed data and the company's historical fraud labels, and then generate fraud predictions using a simple API call.
Option A is incorrect because Amazon Athena is a serverless query service that can analyze data in Amazon S3 using standard SQL, but it does not perform data transformations or fraud detection.
Option C is incorrect because Amazon Redshift is a cloud data warehouse that can store and query data using SQL, but it requires provisioning and managing clusters, which adds operational overhead. Moreover, Amazon Redshift does not provide a built-in fraud detection capability.
Option E is incorrect because Amazon Redshift ML is a feature that allows users to create, train, and deploy machine learning models using SQL commands in Amazon Redshift. However, using Amazon Redshift ML would require loading the data from Amazon S3 to Amazon Redshift, which adds complexity and cost. Also, Amazon Redshift ML does not support fraud detection as a use case.
AWS Glue Crawlers
AWS Glue Workflows and Jobs
Amazon Fraud Detector

 

25. Frage
An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers want to detect critical manufacturing defects in near-real time during testing. All of the data needs to be stored for offline analysis.
What approach would be the MOST effective to perform near-real time defect detection?

• A. Use Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random Cut Forest (RCF) algorithm to determine anomalies.
• B. Use Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry out Apache Spark ML k-means clustering to determine anomalies.
• C. Use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further
• D. Use AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from within AWS IoT Analytics to carry out analysis for anomalies.

Antwort: C

Begründung:
analysis.
Explanation:
The company wants to perform near-real time defect detection on a time-series of 200 performance metrics, and store all the data for offline analysis. The best approach for this scenario is to use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further analysis.
Amazon Kinesis Data Firehose is a service that can capture, transform, and deliver streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk. Kinesis Data Firehose can handle any amount and frequency of data, and automatically scale to match the throughput. Kinesis Data Firehose can also compress, encrypt, and batch the data before delivering it to the destination, reducing the storage cost and enhancing the security.
Amazon Kinesis Data Analytics is a service that can analyze streaming data in real time using SQL or Apache Flink applications. Kinesis Data Analytics can use built-in functions and algorithms to perform various analytics tasks, such as aggregations, joins, filters, windows, and anomaly detection. One of the built-in algorithms that Kinesis Data Analytics supports is Random Cut Forest (RCF), which is a supervised learning algorithm for forecasting scalar time series using recurrent neural networks. RCF can detect anomalies in streaming data by assigning an anomaly score to each data point, based on how distant it is from the rest of the data. RCF can handle multiple related time series, such as the performance metrics of the aircraft engine, and learn a global model that captures the common patterns and trends across the time series.
Therefore, the company can use the following architecture to build the near-real time defect detection solution:
Use Amazon Kinesis Data Firehose for ingestion: The company can use Kinesis Data Firehose to capture the streaming data from the aircraft engine testing, and deliver it to two destinations: Amazon S3 and Amazon Kinesis Data Analytics. The company can configure the Kinesis Data Firehose delivery stream to specify the source, the buffer size and interval, the compression and encryption options, the error handling and retry logic, and the destination details.
Use Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection: The company can use Kinesis Data Analytics to create a SQL application that can read the streaming data from the Kinesis Data Firehose delivery stream, and apply the RCF algorithm to detect anomalies. The company can use the RANDOM_CUT_FOREST or RANDOM_CUT_FOREST_WITH_EXPLANATION functions to compute the anomaly scores and attributions for each data point, and use the WHERE clause to filter out the normal data points. The company can also use the CURSOR function to specify the input stream, and the PUMP function to write the output stream to another destination, such as Amazon Kinesis Data Streams or AWS Lambda.
Use Kinesis Data Firehose to store data in Amazon S3 for further analysis: The company can use Kinesis Data Firehose to store the raw and processed data in Amazon S3 for offline analysis. The company can use the S3 destination of the Kinesis Data Firehose delivery stream to store the raw data, and use another Kinesis Data Firehose delivery stream to store the output of the Kinesis Data Analytics application. The company can also use AWS Glue or Amazon Athena to catalog, query, and analyze the data in Amazon S3.
References:
What Is Amazon Kinesis Data Firehose?
What Is Amazon Kinesis Data Analytics for SQL Applications?
DeepAR Forecasting Algorithm - Amazon SageMaker

 

26. Frage
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