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[General] MLA-C01 Test Guide | Pdf MLA-C01 Free

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【General】 MLA-C01 Test Guide | Pdf MLA-C01 Free

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Amazon MLA-C01 Exam Syllabus Topics:
TopicDetails
Topic 1
  • ML Model Development: This section of the exam measures skills of Fraud Examiners and covers choosing and training machine learning models to solve business problems such as fraud detection. It includes selecting algorithms, using built-in or custom models, tuning parameters, and evaluating performance with standard metrics. The domain emphasizes refining models to avoid overfitting and maintaining version control to support ongoing investigations and audit trails.
Topic 2
  • Deployment and Orchestration of ML Workflows: This section of the exam measures skills of Forensic Data Analysts and focuses on deploying machine learning models into production environments. It covers choosing the right infrastructure, managing containers, automating scaling, and orchestrating workflows through CI
  • CD pipelines. Candidates must be able to build and script environments that support consistent deployment and efficient retraining cycles in real-world fraud detection systems.
Topic 3
  • Data Preparation for Machine Learning (ML): This section of the exam measures skills of Forensic Data Analysts and covers collecting, storing, and preparing data for machine learning. It focuses on understanding different data formats, ingestion methods, and AWS tools used to process and transform data. Candidates are expected to clean and engineer features, ensure data integrity, and address biases or compliance issues, which are crucial for preparing high-quality datasets in fraud analysis contexts.
Topic 4
  • ML Solution Monitoring, Maintenance, and Security: This section of the exam measures skills of Fraud Examiners and assesses the ability to monitor machine learning models, manage infrastructure costs, and apply security best practices. It includes setting up model performance tracking, detecting drift, and using AWS tools for logging and alerts. Candidates are also tested on configuring access controls, auditing environments, and maintaining compliance in sensitive data environments like financial fraud detection.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q144-Q149):NEW QUESTION # 144
A company wants to develop an ML model by using tabular data from its customers. The data contains meaningful ordered features with sensitive information that should not be discarded. An ML engineer must ensure that the sensitive data is masked before another team starts to build the model.
Which solution will meet these requirements?
  • A. Run an Amazon EMR job to change the sensitive data to random values.
  • B. Use Amazon Made to categorize the sensitive data.
  • C. Prepare the data by using AWS Glue DataBrew.
  • D. Run an AWS Batch job to change the sensitive data to random values.
Answer: C

NEW QUESTION # 145
An ML engineer needs to encrypt all data in transit when an ML training job runs. The ML engineer must ensure that encryption in transit is applied to processes that Amazon SageMaker uses during the training job.
Which solution will meet these requirements?
  • A. Encrypt communication between nodes for batch processing.
  • B. Specify an AWS Key Management Service (AWS KMS) key during creation of the SageMaker domain.
  • C. Encrypt communication between nodes in a training cluster.
  • D. Specify an AWS Key Management Service (AWS KMS) key during creation of the training job request.
Answer: C

NEW QUESTION # 146
A medical company is using AWS to build a tool to recommend treatments for patients. The company has obtained health records and self-reported textual information in English from patients. The company needs to use this information to gain insight about the patients.
Which solution will meet this requirement with the LEAST development effort?
  • A. Use the Amazon SageMaker Sequence-to-Sequence (seq2seq) algorithm to create a text summary from the data.
  • B. Use Amazon Kendra to create a quick-search tool to query the data.
  • C. Use Amazon Comprehend Medical to summarize the data.
  • D. Use Amazon SageMaker to build a recurrent neural network (RNN) to summarize the data.
Answer: C

NEW QUESTION # 147
A company is gathering audio, video, and text data in various languages. The company needs to use a large language model (LLM) to summarize the gathered data that is in Spanish.
Which solution will meet these requirements in the LEAST amount of time?
  • A. Use Amazon Comprehend and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Stable Diffusion model to summarize the text.
  • B. Use Amazon Transcribe and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Jurassic model to summarize the text.
  • C. Use Amazon Rekognition and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Anthropic Claude model to summarize the text.
  • D. Train and deploy a model in Amazon SageMaker to convert the data into English text. Train and deploy an LLM in SageMaker to summarize the text.
Answer: B
Explanation:
Amazon Transcribeis well-suited for converting audio data into text, including Spanish.
Amazon Translatecan efficiently translate Spanish text into English if needed.
Amazon Bedrock, with theJurassic model, is designed for tasks like text summarization and can handle large language models (LLMs) seamlessly. This combination provides a low-code, managed solution to process audio, video, and text data with minimal time and effort.

NEW QUESTION # 148
An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the training data before passing the production inference data to the model for predictions.
Which solution will meet this requirement?
  • A. Calculate a new set of min-max normalization statistics from each production sample. Use these values to normalize all the production samples.
  • B. Keep the min-max normalization statistics from the training set. Use these values to normalize the production samples.
  • C. Calculate a new set of min-max normalization statistics from a batch of production samples. Use these values to normalize all the production samples.
  • D. Apply statistics from a well-known dataset to normalize the production samples.
Answer: B

NEW QUESTION # 149
......
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