You work for a gaming company that develops massively multiplayer online (MMO) games. You built a TensorFlow model that predicts whether players will make in-app purchases of more than $10 in the next two weeks. The model's predictions will be used to adapt each user's game experience. User data is stored in BigQuery. How should you serve your model while optimizing cost, user experience, and ease of management?
A. Import the model into BigQuery ML. Make predictions using batch reading data from BigQuery, and push the data to Cloud SQL
B. Deploy the model to Vertex AI Prediction. Make predictions using batch reading data from Cloud Bigtable, and push the data to Cloud SQL.
C. Embed the model in the mobile application. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.
D. Embed the model in the streaming Dataflow pipeline. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.
You recently developed a deep learning model using Keras, and now you are experimenting with different training strategies. First, you trained the model using a single GPU, but the training process was too slow. Next, you distributed the training across 4 GPUs using tf.distribute.MirroredStrategy (with no other changes), but you did not observe a decrease in training time. What should you do?
A. Distribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset
B. Create a custom training loop.
C. Use a TPU with tf.distribute.TPUStrategy.
D. Increase the batch size.
You work for a gaming company that has millions of customers around the world. All games offer a chat feature that allows players to communicate with each other in real time. Messages can be typed in more than 20 languages and are translated in real time using the Cloud Translation API. You have been asked to build an ML system to moderate the chat in real time while assuring that the performance is uniform across the various languages and without changing the serving infrastructure.
You trained your first model using an in-house word2vec model for embedding the chat messages translated by the Cloud Translation API. However, the model has significant differences in performance across the different languages. How should you improve it?
A. Add a regularization term such as the Min-Diff algorithm to the loss function.
B. Train a classifier using the chat messages in their original language.
C. Replace the in-house word2vec with GPT-3 or T5.
D. Remove moderation for languages for which the false positive rate is too high.
You have successfully deployed to production a large and complex TensorFlow model trained on tabular data. You want to predict the lifetime value (LTV) field for each subscription stored in the BigQuery table named subscription. subscriptionPurchase in the project named my-fortune500-company-project.
You have organized all your training code, from preprocessing data from the BigQuery table up to deploying the validated model to the Vertex AI endpoint, into a TensorFlow Extended (TFX) pipeline. You want to prevent prediction drift, i.e., a situation when a feature data distribution in production changes significantly over time. What should you do?
A. Implement continuous retraining of the model daily using Vertex AI Pipelines.
B. Add a model monitoring job where 10% of incoming predictions are sampled 24 hours.
C. Add a model monitoring job where 90% of incoming predictions are sampled 24 hours.
D. Add a model monitoring job where 10% of incoming predictions are sampled every hour.
You are developing an ML model intended to classify whether X-ray images indicate bone fracture risk. You have trained a ResNet architecture on Vertex AI using a TPU as an accelerator, however you are unsatisfied with the training time and memory usage. You want to quickly iterate your training code but make minimal changes to the code. You also want to minimize impact on the model's accuracy. What should you do?
A. Reduce the number of layers in the model architecture.
B. Reduce the global batch size from 1024 to 256.
C. Reduce the dimensions of the images used in the model.
D. Configure your model to use bfloat16 instead of float32.
You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?
A. Reinforcement learning
B. Recommender system
C. Recurrent Neural Networks (RNN)
D. Convolutional Neural Networks (CNN)
You built a custom ML model using scikit-learn. Training time is taking longer than expected. You decide to migrate your model to Vertex AI Training, and you want to improve the model's training time. What should you try out first?
A. Migrate your model to TensorFlow, and train it using Vertex AI Training.
B. Train your model in a distributed mode using multiple Compute Engine VMs.
C. Train your model with DLVM images on Vertex AI, and ensure that your code utilizes NumPy and SciPy internal methods whenever possible.
D. Train your model using Vertex AI Training with GPUs.
You are an ML engineer at a travel company. You have been researching customers' travel behavior for many years, and you have deployed models that predict customers' vacation patterns. You have observed that customers' vacation destinations vary based on seasonality and holidays; however, these seasonal variations are similar across years. You want to quickly and easily store and compare the model versions and performance statistics across years. What should you do?
A. Store the performance statistics in Cloud SQL. Query that database to compare the performance statistics across the model versions.
B. Create versions of your models for each season per year in Vertex AI. Compare the performance statistics across the models in the Evaluate tab of the Vertex AI UI.
C. Store the performance statistics of each pipeline run in Kubeflow under an experiment for each season per year. Compare the results across the experiments in the Kubeflow UI.
D. Store the performance statistics of each version of your models using seasons and years as events in Vertex ML Metadata. Compare the results across the slices.
You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company's manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?
A. Train a regression model using AutoML Tables.
B. Develop a custom TensorFlow regression model, and optimize it using Vertex AI Training.
C. Develop a custom scikit-learn regression model, and optimize it using Vertex AI Training.
D. Develop a regression model using BigQuery ML.
You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?
A. Increase the instance memory to 512 GB and increase the batch size.
B. Replace the NVIDIA P100 GPU with a v3-32 TPU in the training job.
C. Enable early stopping in your Vertex AI Training job.
D. Use the tf.distribute.Strategy API and run a distributed training job.
Nowadays, the certification exams become more and more important and required by more and more enterprises when applying for a job. But how to prepare for the exam effectively? How to prepare for the exam in a short time with less efforts? How to get a ideal result and how to find the most reliable resources? Here on Vcedump.com, you will find all the answers. Vcedump.com provide not only Google exam questions, answers and explanations but also complete assistance on your exam preparation and certification application. If you are confused on your PROFESSIONAL-MACHINE-LEARNING-ENGINEER exam preparations and Google certification application, do not hesitate to visit our Vcedump.com to find your solutions here.