Online Access Free Professional-Machine-Learning-Engineer Practice Test

You need to write a generic test to verify whether Dense Neural Network (DNN) models automatically released by your team have a sufficient number of parameters to learn the task for which they were built. What should you do?

• A.Train the model for a few iterations, and check for NaN values.
• B.Train the model for a few iterations, and verify that the loss is constant.
• C.Train a simple linear model, and determine if the DNN model outperforms it.
• D.Train the model with no regularization, and verify that the loss function is close to zero.

You are developing a Gemini-based onboarding chatbot to answer users' questions based on five PDF onboarding documents in Cloud Storage. The documents are updated weekly or monthly as needed. You expect that the application will receive high-volume, repeated user queries against this documentation context. You need to use the most appropriate and cost-effective solution for managing the context and ensuring that the chatbot has up-to-date information. What should you do?

• A.Send requests with the five PDF files as content pointing to the URI file. Use implicit context caching.
• B.Fine-tune a Gemini model using the text from the five PDF files as a training dataset.
• C.Create an explicit context cache using the five PDF files, and regenerate the cache when the source files change.
• D.Create an explicit context cache using the five PDF files. Set a time-to-live (TTL) of seven days, and regenerate the cache every seven days.

You are an ML engineer at an ecommerce company and have been tasked with building a model that predicts how much inventory the logistics team should order each month. Which approach should you take?

• A.Use a time series forecasting model to predict each item's monthly sales. Give the results to the logistics team so they can base inventory on the amount predicted by the model.
• B.Use a classification model to classify inventory levels as UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKEGive the report to the logistics team each month so they can fine- tune inventory levels.
• C.Use a regression model to predict how much additional inventory should be purchased each month. Give the results to the logistics team at the beginning of the month so they can increase inventory by the amount predicted by the model.
• D.Use a clustering algorithm to group popular items together. Give the list to the logistics team so they can increase inventory of the popular items.

You are developing an ML model that predicts the cost of used automobiles based on data such as location, condition, model type, color, and engine/battery efficiency. The data is updated every night. Car dealerships will use the model to determine appropriate car prices. You created a Vertex AI pipeline that reads the data splits the data into training/evaluation/test sets performs feature engineering trains the model by using the training dataset and validates the model by using the evaluation dataset. You need to configure a retraining workflow that minimizes cost.
What should you do?

• A.Compare the training and evaluation losses of the current run. If the losses are similar, deploy the model to a Vertex AI endpoint with training/serving skew threshold model monitoring. When the model monitoring threshold is triggered redeploy the pipeline.
• B.Compare the results to the evaluation results from a previous run. If the performance improved deploy the model to a Vertex AI endpoint with training/serving skew threshold model monitoring.
  When the model monitoring threshold is triggered redeploy the pipeline.
• C.Compare the training and evaluation losses of the current run. If the losses are similar, deploy the model to a Vertex AI endpoint. Configure a cron job to redeploy the pipeline every night.
• D.Compare the results to the evaluation results from a previous run. If the performance improved deploy the model to a Vertex AI endpoint. Configure a cron job to redeploy the pipeline every night.

You have recently created a proof-of-concept (POC) deep learning model. You are satisfied with the overall architecture, but you need to determine the value for a couple of hyperparameters.
You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate.
You configure the following settings:
- For the embedding dimension, you set the type to INTEGER with a
minValue of 16 and maxValue of 64.
- For the learning rate, you set the type to DOUBLE with a minValue of
10e-05 and maxValue of 10e-02.
You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy. Training time is not a concern. How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?

• A.Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a small number of parallel trials.
• B.Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a large number of parallel trials.
• C.Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials.
• D.Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials.