Amazon Web Services MLS-C01 Exam With Confidence Using Practice Dumps

A pronunciation lexicon is a file that defines how words or phrases should be pronounced by Amazon Polly. A lexicon can help customize the speech output for words that are uncommon, foreign, or have multiple pronunciations. A lexicon must conform to the Pronunciation Lexicon Specification (PLS) standard and can be stored in an AWS region using the Amazon Polly API. To use a lexicon for synthesizing speech, the lexicon name must be specified in the SSML tag. For example, the following lexicon defines how to pronounce the acronym W3C:

W3C xml:lang=“en-US” > The W3C is an international community that develops open standards to ensure the long-term growth of the Web.

Customize pronunciation using lexicons in Amazon Polly: A blog post that explains how to use lexicons for creating custom pronunciations.

Managing Lexicons: A documentation page that describes how to store and retrieve lexicons using the Amazon Polly API.

Serverless inference in SageMaker is designed for workloads with intermittent traffic and unpredictable usage patterns, which aligns with the media company's periodic high-traffic windows. Because the payload is less than 4 MB (serverless inference supports payloads up to 4 MB), serverless inference is appropriate and provisioned concurrency ensures the endpoints are warm and ready during peak times to minimize latency.

From AWS documentation:

“Amazon SageMaker Serverless Inference is ideal for applications with intermittent or unpredictable traffic. You can optionally enable provisioned concurrency to ensure that your endpoints are always ready to process requests during anticipated peak traffic hours.”

— AWS SageMaker Serverless Inference documentation

Using real-time inference with auto scaling (A) incurs a higher cost as it requires always-on instances. Batch transform (D) is for offline, large-scale inferences, not low-latency real-time inference. Asynchronous inference (C) is typically used for large payloads (>4 MB) or long processing times.

Applying L1 regularization encourages sparsity by penalizing weights directly, often driving many weights to zero. In this case, the ML specialist observes that all weights become zero, which suggests that the L1 regularization parameter is set too high. This high value overly penalizes non-zero weights, effectively removing all features from the model.

To improve the model, the ML specialist should reduce the L1 regularization parameter, allowing some features to retain non-zero weights. This adjustment will make the model less prone to excessive sparsity, allowing it to better capture essential patterns in the data without dropping all features. Introducing L2 regularization is another approach but may not directly resolve this specific issue of all-zero weights as effectively as reducing L1.