Free AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 – Amazon AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Practice Test Question

Reference / correct answer:

To create and manage features using Amazon SageMaker Feature Store, follow these steps: 1)Create a feature group : Organize your features by defining a feature group. 2)Ingest the records : Load the data into the feature group. 3)Access the store to build datasets for training : Retrieve the data from the feature group to prepare for model training. upvoted 5 times

Explanation: Create a feature group: You first define the schema (features) of your dataset. Ingest the records: Populate the feature group with data. Access the store to build datasets for training: Retrieve the ingested data to use in training models. upvoted 3 times

Correct Steps (In Order): ✅ 1. Create a feature group. ✅ 2. Ingest the records. ✅ 3. Access the store to build datasets for training. Why? 1️⃣ Create a Feature Group → Defines schema & storage for feature data. 2️⃣ Ingest Records → Stores data in the feature group. 3️⃣ Access Store → Retrieves features for model training. Keywords to Remember: ✅ Feature Group → Defines structure ✅ Ingest Records → Store features ✅ Access Store → Retrieve for training upvoted 4 times

Reference / correct answer:

Tokens are the smallest units that an LLM processes. Embeddings transform text into high-dimensional vectors capturing semantic meaning. RAG combines generated content with retrieved external information to enrich the output. upvoted 5 times

Token Embedding RAG upvoted 1 times

Token : Represents a unit of text used in processing and generating responses by the model. Temperature : Controls the randomness and creativity of the generated output, allowing for adjustments in the model's response style. Embedding : Converts text into vector representations to capture semantic meaning, enhancing the model's ability to understand and generate coherent content. upvoted 3 times

Reference / correct answer:

Most accepted answer: A. Create a new model by using Amazon SageMaker Autopilot. Report the model's achieved performance.

Community votes: A=4

Selected Answer: A Autopilot is the least effort in evaluating a model for usecase upvoted 1 times

Selected Answer: A Amazon Bedrock provides foundation models (LLMs) for tasks like text generation but is not used for structured data prediction. upvoted 2 times

Selected Answer: A Best answere upvoted 2 times

Selected Answer: A A is the most logical answer. B. There is too much effort to set up those scripts and EC2. C. Macie does not fit this use case. D. Lots of effort to set up bedrock for this purpose. upvoted 3 times