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Question 61

You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn't changed; however the accuracy of the model has steadily deteriorated. What issue is most likely causing the steady decline in model accuracy?

A.Poor data quality
B.Lack of model retraining
C.Too few layers in the model for capturing information
D.Incorrect data split ratio during model training, evaluation, validation, and test

Correct Answer: B

Model retraining is the process of updating an existing machine learning model with new data and parameters to improve its performance and accuracy. Model retraining is essential for maintaining the relevance and validity of the model, especially when the data or the environment changes over time. Model retraining can help to avoid or reduce the effects of model degradation, which is the phenomenon of the model's predictive performance decreasing as it is tested on new datasets within rapidly evolving environments1.
For the use case of predicting sales numbers, model accuracy is crucial, because the production model is required to keep up with market changes. Market changes can affect the demand, supply, price, and preference of the products, and thus influence the sales numbers. If the model is not retrained with new data that reflects the market changes, it may become outdated and inaccurate, and fail to capture the patterns and trends of the sales numbers. Therefore, the most likely issue that is causing the steady decline in model accuracy is the lack of model retraining.
The other options are not as likely as option B, because they are not directly related to the model's ability to adapt to market changes. Option A, poor data quality, may affect the model's accuracy, but it is not a specific cause of model degradation over time. Option C, too few layers in the model for capturing information, may affect the model's complexity and expressiveness, but it is not a specific cause of model degradation over time. Option D, incorrect data split ratio during model training, evaluation, validation, and test, may affect the model's generalization and validation, but it is not a specific cause of model degradation over time. Therefore, option B, lack of model retraining, is the best answer for this question.
Reference:
Beware Steep Decline: Understanding Model Degradation In Machine Learning Models

Question 62

Your company needs to generate product summaries for vendors. You evaluate a foundation model from Model Garden for text summarization and find the style of the summaries are not aligned with your company's brand voice. How should you improve this LLM-based summarization model to better meet your business objectives?

A.Replace the pre-trained model with another model in Model Garden.
B.Fine-tune the model using a company-specific dataset.
C.Increase the model's temperature parameter.
D.Tune the token output limit in the response.

Question 63

Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?

A.Categorical hinge
B.Binary cross-entropy
C.Categorical cross-entropy
D.Sparse categorical cross-entropy

Question 64

You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers

A.Increase the buffer size for the shuffle option.
B.Set the prefetch option equal to the training batch size
C.Use the interleave option for reading data
D.Reduce the value of the repeat parameter
E.Decrease the batch size argument in your transformation

Question 65

When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

A.The training channel identifying the location of training data on an Amazon S3 bucket.
B.The validation channel identifying the location of validation data on an Amazon S3 bucket.
C.The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
D.Hyperparameters in a JSON array as documented for the algorithm used.
E.The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
F.The output path specifying where on an Amazon S3 bucket the trained model will persist.