Free AWS-Certified-Machine-Learning-Specialty Exam Braindumps

A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population.
How should the Data Scientist correct this issue?

1. A. Drop all records from the dataset where age has been set to 0.
2. B. Replace the age field value for records with a value of 0 with the mean or median value from the dataset.
3. C. Drop the age feature from the dataset and train the model using the rest of the features.
4. D. Use k-means clustering to handle missing features.

Correct Answer: A

A Data Engineer needs to build a model using a dataset containing customer credit card information. How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

1. A. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VP
2. B. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.
3. C. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
4. D. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VP
5. E. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
6. F. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

Correct Answer: D

A data scientist is developing a pipeline to ingest streaming web traffic data. The data scientist needs to implement a process to identify unusual web traffic patterns as part of the pipeline. The patterns will be used downstream for alerting and incident response. The data scientist has access to unlabeled historic data to use, if needed.
The solution needs to do the following:
Calculate an anomaly score for each web traffic entry.
Adapt unusual event identification to changing web patterns over time. Which approach should the data scientist implement to meet these requirements?

1. A. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker Random Cut Forest (RCF) built-in mode
2. B. Use an Amazon Kinesis Data Stream to process the incoming webtrafficdat
3. C. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the RCF modelto calculate the anomaly score for each record.
4. D. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker built-inXGBoost mode
5. E. Use an Amazon Kinesis Data Stream to process the incoming web traffic dat
6. F. Attach apreprocessing AWS Lambda function to perform data enrichment by calling the XGBoost model to calculate the anomaly score for each record.
7. G. Collect the streaming data using Amazon Kinesis Data Firehos
8. H. Map the delivery stream as an inputsource for Amazon Kinesis Data Analytic
9. I. Write a SQL query to run in real time against the streaming datawith the k-Nearest Neighbors (kNN) SQL extension to calculate anomaly scores for each record using a tumbling window.
10. J. Collect the streaming data using Amazon Kinesis Data Firehos
11. K. Map the delivery stream as an inputsource for Amazon Kinesis Data Analytic
12. L. Write a SQL query to run in real time against the streaming datawith the Amazon Random Cut Forest (RCF) SQL extension to calculate anomaly scores for each record using a sliding window.

Correct Answer: D

A company needs to quickly make sense of a large amount of data and gain insight from it. The data is in different formats, the schemas change frequently, and new data sources are added regularly. The company wants to use AWS services to explore multiple data sources, suggest schemas, and enrich and transform the data. The solution should require the least possible coding effort for the data flows and the least possible infrastructure management.
Which combination of AWS services will meet these requirements?

1. A. Amazon EMR for data discovery, enrichment, and transformationAmazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
2. B. Amazon Kinesis Data Analytics for data ingestionAmazon EMR for data discovery, enrichment, and transformation Amazon Redshift for querying and analyzing the results in Amazon S3
3. C. AWS Glue for data discovery, enrichment, and transformationAmazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
4. D. AWS Data Pipeline for data transferAWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformationAmazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights

Correct Answer: A

A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce.
Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

1. A. Cross-validation
2. B. Numerical value binning
3. C. High-degree polynomial transformation
4. D. Logarithmic transformation
5. E. One hot encoding

Correct Answer: AB