Google Professional-Machine-Learning-Engineer Value Pack (Frequently Bought Together)

You trained a text classification model. You have the following SignatureDefs:

You started a TensorFlow-serving component server and tried to send an HTTP request to get a prediction using:
headers = {"content-type": "application/json"}
json_response = requests.post('http:
//localhost:8501/v1/models/text_model:predict', data=data,
headers=headers)
What is the correct way to write the predict request?

• A. data = json.dumps({"signature_name": "seving_default", "instances" [[`ab', `bc', `cd']]})
• B. data = json.dumps({"signature_name": "serving_default", "instances" [[`a', `b'], [`c', `d'], [`e', `f']]})
• C. data = json.dumps({"signature_name": "serving_default", "instances" [[`a', `b', `c'], [`d', `e', `f']]})
• D. data = json.dumps({"signature_name": "serving_default", "instances" [[`a', `b', `c', `d', `e', `f']]})

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. Remove moderation for languages for which the false positive rate is too high.
• B. Add a regularization term such as the Min-Diff algorithm to the loss function.
• C. Replace the in-house word2vec with GPT-3 or T5.
• D. Train a classifier using the chat messages in their original language.

You work for a large financial institution that is planning to use Dialogflow to create a chatbot for the company's mobile app. You have reviewed old chat logs and tagged each conversation for intent based on each customer's stated intention for contacting customer service. About 70% of customer inquiries are simple requests that are solved within 10 intents. The remaining 30% of inquiries require much longer and more complicated requests. Which intents should you automate first?

• A. Automate the more complicated requests first because those require more of the agents' time.
• B. Automate the 10 intents that cover 70% of the requests so that live agents can handle the more complicated requests.
• C. Automate a blend of the shortest and longest intents to be representative of all intents.
• D. Automate intents in places where common words such as payment only appear once to avoid confusing the software.

You have deployed a deep learning model to an Agent Platform endpoint using a machine type with NVIDIA GPUs. You initially configured the endpoint to autoscale based on a target CPU utilization of 60%. During a load test, you observe that prediction latency increases significantly as traffic rises, but the number of replicas remains constant. Cloud Monitoring indicates that CPU utilization stays below 40%, while the GPU utilization consistently exceeds 90%. You need to ensure that the endpoint scales efficiently to handle the increased load. What should you do?

• A. Redeploy the model to a machine type that includes a TPU.
• B. Update the autoscaling configuration to decrease the target CPU utilization to 20%.
• C. Increase the minimum replica count for the endpoint to match the peak load that was observed during testing.
• D. Update the autoscaling configuration to scale based on the GPU duty cycle metric.

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. Use the tf.distribute.Strategy API and run a distributed training job.
• B. Replace the NVIDIA P100 GPU with a K80 GPU in the training job.
• C. Increase the instance memory to 512 GB, and increase the batch size.
• D. Enable early stopping in your Vertex AI Training job.