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Training Options for MLS-C01 Exam
If you don't have much hands-on experience in machine learning, it is recommended to enroll in the course and gain it before going for the AWS Certified Machine Learning – Specialty exam. There are five options offered by AWS itself, some of them are as follows:
- Deep Learning on AWS
This course also lasts for only one day but still has comprehensive content. It emphasizes the AWS DL (Deep Learning) solutions as well as the use of MXNet and Amazon SageMaker. Also, the candidates are going to understand how to deploy DL models by utilizing AWS services and build intelligent systems. This training can be taken in either live-classroom form or live-virtual.
- The Machine Learning Pipeline on AWS (by AWS Training)
During these in-classroom or virtual sessions, the candidates will come across exceptional knowledge about how to use the machine learning pipeline to solve real business problems. It is the best project-based studying environment for individuals that are passionate about working with ML models using Amazon SageMaker. At the end of the course, students will be able to solve any issues related to fraud detection, flight delays, recommendation engines, etc. It leads the applicants to the path of overcoming any challenges effectively and get knowledge of machine learning thoroughly to take the test. You will find this 4-days course easy if you have prior experience in the field as well as knowledge of Python and Statistics.
- Process Model: CRISP-DM on the AWS Stack (by AWS Training)
In contrast to the previous option, this training is just a one-day course. The candidates can follow it in different languages like English, French, Simplified Chinese, Indonesian, Japanese, and Korean. It focuses on the CRISP-DM model in relation to data science, including all its six phases and framework and methodology. The course also shows applicants how to use CRISP-DM to resolve various problems within their daily work. The good thing about this training is that it's free.
Recommended Experience
Before registering for the AWS Certified Machine Learning – Specialty exam, the applicant should ensure meeting some prerequisites as stated by the vendor. First, working experience of 1-2 years in running ML workloads as well as their development and architecting on AWS cloud is a must for the candidate. Moreover, it is recommended to have practical skills in executing the hyperparameter optimization, deep learning and ML frameworks, and operational and model-training best practices for AWS Machine learning.
How much AWS Certified Machine Learning - Specialty Cost
The price of Amazon MLS exam is $150 USD.
Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q75-Q80):
NEW QUESTION # 75
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 using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet
- B. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.
- C. Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.
- D. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.
Answer: D
Explanation:
https://medium.com/searce/convert-csv-json-files-to-apache-parquet-using-aws-glue-a760d177b45f
https://github.com/ecloudvalley/Building-a-Data-Lake-with-AWS-Glue-and-Amazon-S3
NEW QUESTION # 76
A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs The workflow consists of the following processes
* Start the workflow as soon as data is uploaded to Amazon S3
* When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3
* Store the results of joining datasets in Amazon S3
* If one of the jobs fails, send a notification to the Administrator
Which configuration will meet these requirements?
- A. Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
- B. Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3 Use AWS Glue to join the datasets in Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
- C. Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3 Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
- D. Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure
Answer: A
NEW QUESTION # 77
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. Parquet files
- B. RecordIO
- C. CSV files
- D. Compressed JSON
Answer: A
NEW QUESTION # 78
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 training data by adding variation in rotation for training images.
- C. Increase the number of epochs for model training
- D. Increase the dropout rate for the second-to-last layer.
Answer: B
Explanation:
The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
NEW QUESTION # 79
A Machine Learning Specialist is attempting to build a linear regression model.
Given the displayed residual plot only, what is the MOST likely problem with the model?
- A. Linear regression is inappropriate. The underlying data has outliers.
- B. Linear regression is inappropriate. The residuals do not have constant variance.
- C. Linear regression is appropriate. The residuals have constant variance.
- D. Linear regression is appropriate. The residuals have a zero mean.
Answer: C
NEW QUESTION # 80
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