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AWS-Certified-Machine-Learning-Specialty Exam Guide | Valid AWS-Certified-Machine-Learning-Specialty Exam Objectives

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What Are Career Opportunities for Certified Specialists?

If you are determined to get this AWS Machine Learning Specialty certification, you should be ready to understand how machine learning works and how to implement its features to solve different business problems and improve processes. By adding this certification under your belt, you can apply for different positions, such as:

Machine Learning Engineer;
Senior Development Manager, Applied Science;
Deep and Machine Learning Specialist.
Software Development Engineer;

Any successful candidate who manages to get the AWS Machine Learning Specialty certification will have improved chances to get a well-paid job and appealing incentives. We checked what Payscale.com mentions the opportunities available for the positions above. According to the benchmark of this site, a Machine Learning Engineer will get around $111k per annum. On the other hand, a Software Development Engineer can win approximately $110k in one year. Of course, a candidate who gets an international certificate like the AWS one will benefit from a higher salary. In case you wonder what a Machine Learning Engineer does, then you should know that he/she works with artificial intelligence and creates different programs and algorithms that improve business development and make processes more effective. The person working in this position is quite autonomous and has an open-minded attitude. He/she will immediately identify faults and come with immediate solutions to prevent any system breakdowns.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q25-Q30):

NEW QUESTION # 25
A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the data Which type of data repository is the MOST cost-effective solution?

A. An Amazon S3 data lake with hourly object prefixes
B. An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes
C. An Amazon EBS-backed Amazon EC2 instance with hourly directories
D. An Amazon RDS database with hourly table partitions

Answer: A

Explanation:
An Amazon S3 data lake is a cost-effective solution for storing and analyzing large amounts of time-based training data for a new model. Amazon S3 is a highly scalable, durable, and secure object storage service that can store any amount of data in any format. Amazon S3 also offers low-cost storage classes, such as S3 Standard-IA and S3 One Zone-IA, that can reduce the storage costs for infrequently accessed data. By using hourly object prefixes, the Machine Learning Specialist can organize the data into logical partitions based on the time of ingestion. This can enable efficient data access and management, as well as support incremental updates and deletes. The Specialist can also use Amazon S3 lifecycle policies to automatically transition the data to lower-cost storage classes or delete the data after a certain period of time. This way, the Specialist can always train on the last 24 hours of the data and optimize the storage costs.
References:
What is a data lake? - Amazon Web Services
Amazon S3 Storage Classes - Amazon Simple Storage Service
Managing your storage lifecycle - Amazon Simple Storage Service
Best Practices Design Patterns: Optimizing Amazon S3 Performance

NEW QUESTION # 26
An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.
What combination of services is the MOST efficient to accomplish the task?

A. Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)
B. Amazon Transcribe, Amazon Translate, and Amazon Comprehend
C. Amazon Transcribe, Amazon Translate and Amazon SageMaker BlazingText
D. Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq

Answer: B

Explanation:
https://aws.amazon.com/getting-started/hands-on/analyze-sentiment-comprehend/

NEW QUESTION # 27
A machine learning specialist needs to analyze comments on a news website with users across the globe. The specialist must find the most discussed topics in the comments that are in either English or Spanish.
What steps could be used to accomplish this task? (Choose two.)

A. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics.
B. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Lex to extract topics form the content.
C. Use an Amazon SageMaker BlazingText algorithm to find the topics independently from language.
Proceed with the analysis.
D. Use an Amazon SageMaker seq2seq algorithm to translate from Spanish to English, if necessary. Use a SageMaker Latent Dirichlet Allocation (LDA) algorithm to find the topics.
E. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics.

Answer: A,E

Explanation:
Explanation
To find the most discussed topics in the comments that are in either English or Spanish, the machine learning specialist needs to perform two steps: first, translate the comments from Spanish to English if necessary, and second, apply a topic modeling algorithm to the comments. The following options are valid ways to accomplish these steps using AWS services:
Option C: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics. Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text.
Amazon Comprehend topic modeling is a feature that automatically organizes a collection of text documents into topics that contain commonly used words and phrases.
Option E: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics. Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker Neural Topic Model (NTM) is an unsupervised learning algorithm that is used to organize a corpus of documents into topics that contain word groupings based on their statistical distribution.
The other options are not valid because:
Option A: Amazon SageMaker BlazingText algorithm is not a topic modeling algorithm, but a text classification and word embedding algorithm. It cannot find the topics independently from language, as different languages have different word distributions and semantics.
Option B: Amazon SageMaker seq2seq algorithm is not a translation algorithm, but a sequence-to-sequence learning algorithm that can be used for tasks such as summarization, chatbot, and question answering. Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm is a topic modeling algorithm, but it requires the input documents to be in the same language and preprocessed into a bag-of-words format.
Option D: Amazon Lex is not a topic modeling algorithm, but a service for building conversational interfaces into any application using voice and text. It cannot extract topics from the content, but only intents and slots based on a predefined bot configuration. References:
Amazon Translate
Amazon Comprehend
Amazon SageMaker
Amazon SageMaker Neural Topic Model (NTM) Algorithm
Amazon SageMaker BlazingText
Amazon SageMaker Seq2Seq
Amazon SageMaker Latent Dirichlet Allocation (LDA) Algorithm
Amazon Lex

NEW QUESTION # 28
A large JSON dataset for a project has been uploaded to a private Amazon S3 bucket The Machine Learning Specialist wants to securely access and explore the data from an Amazon SageMaker notebook instance A new VPC was created and assigned to the Specialist How can the privacy and integrity of the data stored in Amazon S3 be maintained while granting access to the Specialist for analysis?

A. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Define a custom S3 bucket policy to only allow requests from your VPC to access the S3 bucket
B. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled. Generate an S3 pre-signed URL for access to data in the bucket
C. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Copy the JSON dataset from Amazon S3 into the ML storage volume on the SageMaker notebook instance and work against the local dataset
D. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled Use an S3 ACL to open read privileges to the everyone group

Answer: A

Explanation:
Explanation
The best way to maintain the privacy and integrity of the data stored in Amazon S3 is to use a combination of VPC endpoints and S3 bucket policies. A VPC endpoint allows the SageMaker notebook instance to access the S3 bucket without going through the public internet. A bucket policy allows the S3 bucket owner to specify which VPCs or VPC endpoints can access the bucket. This way, the data is protected from unauthorized access and tampering. The other options are either insecure (A and D) or inefficient (B). References: Using Amazon S3 VPC Endpoints, Using Bucket Policies and User Policies

NEW QUESTION # 29
A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test data. However, with unknown data, it is not working as expected. The existing parameters are provided as follows.

Which parameter tuning guidelines should the Specialist follow to avoid overfitting?

A. Lower the min_child_weight parameter value.
B. Increase the max_depth parameter value.
C. Lower the max_depth parameter value.
D. Update the objective to binary:logistic.

Answer: C

Explanation:
Explanation
Overfitting occurs when a model performs well on the training data but poorly on the test data. This is often because the model has learned the training data too well and is not able to generalize to new data. To avoid overfitting, the Machine Learning Specialist should lower the max_depth parameter value. This will reduce the complexity of the model and make it less likely to overfit. According to the XGBoost documentation1, the max_depth parameter controls the maximum depth of a tree and lower values can help prevent overfitting. The documentation also suggests other ways to control overfitting, such as adding randomness, using regularization, and using early stopping1. References:
XGBoost Parameters

NEW QUESTION # 30
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