A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial data. The company is worried about data egress and wants an ML engineer to secure the environment.
Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

1. A. Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
2. B. Use SCPs to restrict access to SageMaker.
3. C. Disable root access on the SageMaker notebook instances.
4. D. Enable network isolation for training jobs and models.
5. E. Restrict notebook presigned URLs to specific IPs used by the company.
6. F. Protect data with encryption at rest and in transi
7. G. Use AWS Key Management Service (AWS KMS) to manage encryption keys.

Correct Answer: BDE
https://aws.amazon.com/blogs/machine-learning/millennium-management-secure-machine-learning-using-amaz

A company is building a new version of a recommendation engine. Machine learning (ML) specialists need to keep adding new data from users to improve personalized recommendations. The ML specialists gather data from the users’ interactions on the platform and from sources such as external websites and social media.
The pipeline cleans, transforms, enriches, and compresses terabytes of data daily, and this data is stored in Amazon S3. A set of Python scripts was coded to do the job and is stored in a large Amazon EC2 instance. The whole process takes more than 20 hours to finish, with each script taking at least an hour. The company wants to move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers.
Which approach will address all of these requirements with the LEAST development effort?

1. A. Load the data into an Amazon Redshift cluste
2. B. Execute the pipeline by using SQ
3. C. Store the results in Amazon S3.
4. D. Load the data into Amazon DynamoD
5. E. Convert the scripts to an AWS Lambda functio
6. F. Execute the pipeline by triggering Lambda execution
7. G. Store the results in Amazon S3.
8. H. Create an AWS Glue jo
9. I. Convert the scripts to PySpar
10. J. Execute the pipelin
11. K. Store the results in Amazon S3.
12. L. Create a set of individual AWS Lambda functions to execute each of the script
13. M. Build a step function by using the AWS Step Functions Data Science SD
14. N. Store the results in Amazon S3.

Correct Answer: B

A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena The dataset contains more than 800.000 records stored as plaintext CSV files Each record contains 200 columns and is approximately 1 5 MB in size Most queries will span 5 to 10 columns only
How should the Machine Learning Specialist transform the dataset to minimize query runtime?

1. A. Convert the records to Apache Parquet format
2. B. Convert the records to JSON format
3. C. Convert the records to GZIP CSV format
4. D. Convert the records to XML format

Correct Answer: A
Using compressions will reduce the amount of data scanned by Amazon Athena, and also reduce your S3 bucket storage. It’s a Win-Win for your AWS bill. Supported formats: GZIP, LZO, SNAPPY (Parquet) and ZLIB.

A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.
What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

1. A. Root Mean Square Error (RMSE)
2. B. Residual plots
3. C. Area under the curve
4. D. Confusion matrix

Correct Answer: B

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?

1. A. The model needs to be completely re-engineered because it is unable to handle product inventory changes
2. B. The model's hyperparameters should be periodically updated to prevent drift
3. C. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
4. D. The model should be periodically retrained using the original training data plus new data as product inventory changes

Correct Answer: D