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Valid Exam AWS-Certified-Machine-Learning-Specialty Preparation | Test AWS-Certi
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To be eligible to take the AWS Certified Machine Learning - Specialty certification exam, the candidate must have a minimum of one year of experience using AWS services, and must have a strong understanding of machine learning concepts and techniques. AWS Certified Machine Learning - Specialty certification exam is a combination of multiple-choice and multiple-response questions, and requires the candidate to demonstrate their practical skills by completing a hands-on lab exercise. Upon passing the exam, the candidate will receive the AWS Certified Machine Learning - Specialty certification, which is valid for three years.
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Amazon MLS-C01 exam covers a wide range of topics, including data preparation, feature engineering, model training, evaluation and deployment, and machine learning implementation and operations. AWS-Certified-Machine-Learning-Specialty exam also covers AWS services such as Amazon SageMaker, AWS Deep Learning AMIs, Amazon S3, Amazon EMR, and Amazon Redshift. AWS-Certified-Machine-Learning-Specialty exam is designed to assess the candidate's ability to use these services to build, train, and deploy ML models.
The Amazon AWS-Certified-Machine-Learning-Specialty Exam is intended for professionals who are already working in the field of machine learning or those who are planning to start a career in this field. To take the exam, candidates should have a good understanding of programming languages such as Python or R, as well as experience with AWS services like S3, EC2, and SageMaker.
Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q74-Q79):NEW QUESTION # 74
A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS.
How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?
- A. Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.
- B. Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
- C. Configure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.
- D. Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.
Answer: D
Explanation:
To read data from an Amazon S3 bucket that is protected with server-side encryption using AWS KMS, the Amazon SageMaker notebook instance needs to have an IAM role that has permission to access the S3 bucket and the KMS key. The IAM role is an identity that defines the permissions for the notebook instance to interact with other AWS services. The IAM role can be assigned to the notebook instance when it is created or updated later.
The KMS key policy is a document that specifies who can use and manage the KMS key. The KMS key policy can grant permission to the IAM role of the notebook instance to decrypt the data in the S3 bucket. The KMS key policy can also grant permission to other principals, such as AWS accounts, IAM users, or IAM roles, to use the KMS key for encryption and decryption operations.
Therefore, the Machine Learning Specialist should assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role. This way, the notebook instance can use the IAM role credentials to access the S3 bucket and the KMS key, and read the encrypted data from the S3 bucket.
References:
Create an IAM Role to Grant Permissions to Your Notebook Instance
Using Key Policies in AWS KMS
NEW QUESTION # 75
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?
- A. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.
- B. Reduce the learning rate and run the training process until the training loss stops decreasing.
- C. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.
- D. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.
Answer: B
NEW QUESTION # 76
A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year's worth of credit card transactions data. The model needs to identify the fraudulent transactions (positives) from the regular ones (negatives). The company's goal is to accurately capture as many positives as possible.
Which metrics should the data scientist use to optimize the model? (Choose two.)
- A. Area under the precision-recall curve
- B. True positive rate
- C. False positive rate
- D. Accuracy
- E. Specificity
Answer: A,B
Explanation:
The data scientist should use the area under the precision-recall curve and the true positive rate to optimize the model. These metrics are suitable for imbalanced classification problems, such as credit card fraud detection, where the positive class (fraudulent transactions) is much rarer than the negative class (non-fraudulent transactions).
The area under the precision-recall curve (AUPRC) is a measure of how well the model can identify the positive class among all the predicted positives. Precision is the fraction of predicted positives that are actually positive, and recall is the fraction of actual positives that are correctly predicted. A higher AUPRC means that the model can achieve a higher precision with a higher recall, which is desirable for fraud detection.
The true positive rate (TPR) is another name for recall. It is also known as sensitivity or hit rate. It measures the proportion of actual positives that are correctly identified by the model. A higher TPR means that the model can capture more positives, which is the company's goal.
Metrics for Imbalanced Classification in Python - Machine Learning Mastery Precision-Recall - scikit-learn
NEW QUESTION # 77
A machine learning (ML) specialist is administering a production Amazon SageMaker endpoint with model monitoring configured. Amazon SageMaker Model Monitor detects violations on the SageMaker endpoint, so the ML specialist retrains the model with the latest dataset. This dataset is statistically representative of the current production traffic. The ML specialist notices that even after deploying the new SageMaker model and running the first monitoring job, the SageMaker endpoint still has violations.
What should the ML specialist do to resolve the violations?
- A. Delete the endpoint and recreate it with the original configuration.
- B. Run the Model Monitor baseline job again on the new training set. Configure Model Monitor to use the new baseline.
- C. Manually trigger the monitoring job to re-evaluate the SageMaker endpoint traffic sample.
- D. Retrain the model again by using a combination of the original training set and the new training set.
Answer: B
Explanation:
The ML specialist should run the Model Monitor baseline job again on the new training set and configure Model Monitor to use the new baseline. This is because the baseline job computes the statistics and constraints for the data quality and model quality metrics, which are used to detect violations. If the training set changes, the baseline job should be updated accordingly to reflect the new distribution of the data and the model performance. Otherwise, the old baseline may not be representative of the current production traffic and may cause false alarms or miss violations. References:
Monitor data and model quality - Amazon SageMaker
Detecting and analyzing incorrect model predictions with Amazon SageMaker Model Monitor and Debugger | AWS Machine Learning Blog
NEW QUESTION # 78
A data scientist needs to create a model for predictive maintenance. The model will be based on historical data to identify rare anomalies in the data.
The historical data is stored in an Amazon S3 bucket. The data scientist needs to use Amazon SageMaker Data Wrangler to ingest the data. The data scientists also needs to perform exploratory data analysis (EDA) to understand the statistical properties of the data.
Which solution will meet these requirements with the LEAST amount of compute resources?
- A. Import the data by using the Randomized option. Infer the random size from domain knowledge.
- B. Import the data by using the None option.
- C. Import the data by using the First K option. Infer the value of K from domain knowledge.
- D. Import the data by using the Stratified option.
Answer: C
Explanation:
To perform efficient exploratory data analysis (EDA) on a large dataset for anomaly detection, using the First K option in SageMaker Data Wrangler is an optimal choice. This option allows the data scientist to select the first K rows, limiting the data loaded into memory, which conserves compute resources.
Given that the First K option allows the data scientist to determine K based on domain knowledge, this approach provides a representative sample without requiring extensive compute resources. Other options like randomized sampling may not provide data samples that are as useful for initial analysis in a time-series or sequential dataset context.
NEW QUESTION # 79
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