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To be eligible for the AWS Certified Machine Learning - Specialty certification exam, candidates must have a minimum of one year of experience in developing and maintaining machine learning models on the AWS platform. They must also have a strong understanding of AWS services, such as Amazon SageMaker, Amazon EC2, Amazon S3, and AWS Lambda.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q111-Q116):

NEW QUESTION # 111
A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?

A. Add event type and event value fields to the interactions dataset in Amazon Personalize.B. Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.C. Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.D. Use the event tracker in Amazon Personalize to include real-time user interactions.

Answer: D

NEW QUESTION # 112
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class?

A. Naive Bayesian classifierB. Single Perceptron with sigmoidal activation functionC. Decision treeD. Linear support vector machine (SVM)

Answer: B

NEW QUESTION # 113
Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?

A. Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.B. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.C. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.D. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.

Answer: B

Explanation:
Explanation/Reference: https://docs.aws.amazon.com/machine-learning/latest/dg/data-transformations-reference.html

NEW QUESTION # 114
A Machine Learning Specialist is working with a large cybersecurity company that manages security events in real time for companies around the world. The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested. The company also wants be able to save the results in its data lake for later processing and analysis.
What is the MOST efficient way to accomplish these tasks?

A. Ingest the data and store it in Amazon S3. Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.B. Ingest the data into Apache Spark Streaming using Amazon EMR, and use Spark MLlib with k- means to perform anomaly detection. Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake.C. Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data. Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data.D. Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection. Then use Kinesis Data Firehose to stream the results to Amazon S3.

Answer: D

Explanation:
https://aws.amazon.com/tw/blogs/machine-learning/use-the-built-in-amazon-sagemaker-random- cut-forest-algorithm-for-anomaly-detection/

NEW QUESTION # 115
A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket.
A Machine Learning Specialist wants to use SQL to run queries on this data.
Which solution requires the LEAST effort to be able to query this data?

A. Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.B. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.C. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.D. Use AWS Glue to catalogue the data and Amazon Athena to run queries.

Answer: D

NEW QUESTION # 116
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