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How to Prepare for the AWS Certified Machine Learning Specialty Exam
Preparation Guide for For AWS Certified Machine Learning Specialty Exam
Introduction
Amazon Web Services is the current market leader in the field of cloud computing. Many organizations are boarding the AWS train for very promising benefits. Profitability, flexibility, ease of use and comprehensive support are the pillars of AWS's popularity. As AWS gained popularity, many companies began looking for AWS certified professionals.
AWS certified machine learning specialty certification is one of many AWS certifications popular today. AWS provides certification to validate an individual's skills and experience in AWS specific tools, resources and technologies. The following discussion will focus on the details of the AWS certified machine learning specialty certification. The purpose of the following discussion is to support the preparation of candidates for the certification exam.
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How to book the AWS Certified Machine Learning - Specialty
These are following steps for registering the Amazon MLS exam:
- Step 1 : Signup/Login to AWS account
- Step 2 : Search for the exam
- Step 3 : Select Date and Center of examination and confirm with payment
Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q194-Q199):
NEW QUESTION # 194
A machine learning (ML) specialist wants to create a data preparation job that uses a PySpark script with complex window aggregation operations to create data for training and testing. The ML specialist needs to evaluate the impact of the number of features and the sample count on model performance.
Which approach should the ML specialist use to determine the ideal data transformations for the model?
- A. Add an Amazon SageMaker Experiments tracker to the script to capture key parameters. Run the script as a SageMaker processing job.
- B. Add an Amazon SageMaker Debugger hook to the script to capture key parameters. Run the script as a SageMaker processing job.
- C. Add an Amazon SageMaker Experiments tracker to the script to capture key metrics. Run the script as an AWS Glue job.
- D. Add an Amazon SageMaker Debugger hook to the script to capture key metrics. Run the script as an AWS Glue job.
Answer: C
NEW QUESTION # 195
A Data Scientist needs to migrate an existing on-premises ETL process to the cloud. The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing.
The Data Scientist has been given the following requirements to the cloud solution:
* Combine multiple data sources.
* Reuse existing PySpark logic.
* Run the solution on the existing schedule.
* Minimize the number of servers that will need to be managed.
Which architecture should the Data Scientist use to build this solution?
- A. Write the raw data to Amazon S3. Create an AWS Glue ETL job to perform the ETL processing against the input data. Write the ETL job in PySpark to leverage the existing logic. Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule. Configure the output target of the ETL job to write to a
"processed" location in Amazon S3 that is accessible for downstream use. - B. Use Amazon Kinesis Data Analytics to stream the input data and perform real-time SQL queries against the stream to carry out the required transformations within the stream. Deliver the output results to a
"processed" location in Amazon S3 that is accessible for downstream use. - C. Write the raw data to Amazon S3. Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule. Use the existing PySpark logic to run the ETL job on the EMR cluster. Output the results to a "processed" location in Amazon S3 that is accessible for downstream use.
- D. Write the raw data to Amazon S3. Schedule an AWS Lambda function to run on the existing schedule and process the input data from Amazon S3. Write the Lambda logic in Python and implement the existing PySpark logic to perform the ETL process. Have the Lambda function output the results to a "processed" location in Amazon S3 that is accessible for downstream use.
Answer: B
NEW QUESTION # 196
A Machine Learning Specialist deployed a model that provides product recommendations on a company's website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago Which method should the Specialist try to improve model performance?
- A. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
- B. The model's hyperparameters should be periodically updated to prevent drift
- C. The model needs to be completely re-engineered because it is unable to handle product inventory changes
- D. The model should be periodically retrained using the original training data plus new data as product inventory changes
Answer: C
NEW QUESTION # 197
A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.
How should the Specialist address this issue and what is the reason behind it?
- A. The learning rate should be increased because the optimization process was trapped at a local minimum.
- B. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
- C. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
- D. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
Answer: A
NEW QUESTION # 198
A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large?
- A. Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development
- B. Use an Amazon SageMaker notebook for both feature engineering and model development
- C. Use Amazon ML for both feature engineering and model development.
- D. Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development
Answer: D
NEW QUESTION # 199
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