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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q13-Q18):

NEW QUESTION # 13
A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.
The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.
Which solution will meet these requirements?

A. Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
B. Create a model group for each category. Move the existing models into these category model groups.
C. Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.
D. Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.

Answer: D

NEW QUESTION # 14
A company is developing a customer support AI assistant by using an Amazon Bedrock Retrieval Augmented Generation (RAG) pipeline. The AI assistant retrieves articles from a knowledge base stored in Amazon S3.
The company uses Amazon OpenSearch Service to index the knowledge base. The AI assistant uses an Amazon Bedrock Titan Embeddings model for vector search.
The company wants to improve the relevance of the retrieved articles to improve the quality of the AI assistant's answers.
Which solution will meet these requirements?

A. Use Amazon Bedrock Provisioned Throughput to process queries more efficiently.
B. Use Amazon Athena to pre-filter the articles based on metadata before retrieval.
C. Use a reranker model before passing the articles to the foundation model (FM).
D. Use auto-summarization on the retrieved articles by using Amazon SageMaker JumpStart.

Answer: C

Explanation:
In a Retrieval Augmented Generation (RAG) architecture, retrieval quality directly impacts response accuracy. AWS documentation for Bedrock and OpenSearch highlights the use of reranker models to improve relevance after initial vector search retrieval.
Vector search retrieves documents based on embedding similarity, but the top results are not always the most contextually relevant. A reranker model evaluates the retrieved documents against the user query and reorders them based on semantic relevance before sending them to the foundation model.
Option A improves readability but does not improve retrieval relevance. Option C filters data before retrieval, which can reduce recall. Option D improves performance, not relevance.
AWS explicitly recommends reranking as a best practice for improving answer quality in RAG systems.
Therefore, Option B is the correct solution.

NEW QUESTION # 15
A company is developing a new ML model that uses the XGBoost algorithm. The company will train the model on data that is stored in an Amazon S3 bucket. The data is in a nested JSON format.
An ML engineer needs to convert the JSON files into a tabular format.
Which solution will meet this requirement with the LEAST operational overhead?

A. Create an AWS Lambda function that uses a Python runtime and invokes the reduce() function to convert the files. Invoke the Lambda function.
B. Write custom Scala code to convert the files. Use Amazon EMR Serverless to run the Scala code.
C. Create an AWS Glue PySpark job that uses the Relationalize transform to convert the files.
D. Create an Amazon Athena database that is based on the JSON files. Use the Athena flatten function to convert the data.

Answer: C

Explanation:
The AWS Glue PySpark Relationalize transform is purpose-built to convert nested JSON into tabular format with minimal operational overhead. It automates the flattening process without requiring custom code or complex infrastructure, making it the most efficient solution for preparing the data for XGBoost training.

NEW QUESTION # 16
A company is planning to use Amazon SageMaker to make classification ratings that are based on images. The company has 6 GB of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM). The SVM is in the same VPC as SageMaker.
An ML engineer must make the training data accessible for ML models that are in the SageMaker environment.
Which solution will meet these requirements?

A. Create an Amazon S3 bucket. Use Mountpoint for Amazon S3 to link the S3 bucket to the FSx for ONTAP file system.
B. Create a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
C. Mount the FSx for ONTAP file system as a volume to the SageMaker Instance.
D. Create a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system.

Answer: C

NEW QUESTION # 17
An insurance company needs to automate claim compliance reviews because human reviews are expensive and error-prone. The company has a large set of claims and a compliance label for each. Each claim consists of a few sentences in English, many of which contain complex related information. Management would like to use Amazon SageMaker built-in algorithms to design a machine learning supervised model that can be trained to read each claim and predict if the claim is compliant or not.
Which approach should be used to extract features from the claims to be used as inputs for the downstream supervised task?

A. Apply Amazon SageMaker Object2Vec to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
B. Apply Amazon SageMaker BlazingText in Word2Vec mode to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
C. Derive a dictionary of tokens from claims in the entire dataset. Apply one-hot encoding to tokens found in each claim of the training set. Send the derived features space as inputs to an Amazon SageMaker built- in supervised learning algorithm.
D. Apply Amazon SageMaker BlazingText in classification mode to labeled claims in the training set to derive features for the claims that correspond to the compliant and non-compliant labels, respectively.

Answer: A

Explanation:
Amazon SageMaker Object2Vec generalizes the Word2Vec embedding technique for words to more complex objects, such as sentences and paragraphs. Since the supervised learning task is at the level of whole claims, for which there are labels, and no labels are available at the word level, Object2Vec needs be used instead of Word2Vec.

NEW QUESTION # 18
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