Free Google Professional-Machine-Learning-Engineer Exam Questions

Question 1

You developed an ML model with AI Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine

(GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?

A : gnificantly increase the max_batch_size TensorFlow Serving parameter.

B : itch to the tensorflow-model-server-universal version of TensorFlow Serving.

C : gnificantly increase the max_enqueued_batches TensorFlow Serving parameter.

D : compile TensorFlow Serving using the source to support CPU-specific optimizations. Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes

Answer: D

Question 2

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 : crease the instance memory to 512 GB and increase the batch size.

B : place the NVIDIA P100 GPU with a v3-32 TPU in the training job.

C : able early stopping in your Vertex AI Training job.

D : the tf.distribute.Strategy API and run a distributed training job.

Answer: C

Question 3

You trained a model, packaged it with a custom Docker container for serving, and deployed it to Vertex Al Model Registry. When you submit a batch prediction job, it fails with this error "Error model server never became ready Please validate that your model file or container configuration are valid. There are no additional errors in the logs What should you do?

A : Add a logging configuration to your application to emit logs to Cloud Logging.

B : Change the HTTP port in your model's configuration to the default value of 8080

C : Change the health Route value in your models configuration to /heal thcheck

D : Pull the Docker image locally and use the decker run command to launch it locally. Use the docker logs command to explore the error logs.

Answer: D

Question 4

You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?

A : ain local surrogate models to explain individual predictions.

B : nfigure sampled Shapley explanations on Vertex Explainable AI.

C : nfigure integrated gradients explanations on Vertex Explainable AI.

D : asure the effect of each feature as the weight of the feature multiplied by the feature value.

Answer: A

Question 5

You need to design an architecture that serves asynchronous predictions to determine whether a particular mission-critical machine part will fail. Your system collects data from multiple sensors from the machine. You want to build a model that will predict a failure in the next N minutes, given the average of each sensor’s data from the past 12 hours. How should you design the architecture?

A : 1. HTTP requests are sent by the sensors to your ML model, which is deployed as a microservice and exposes a REST API for prediction2. Your application queries a Vertex AI endpoint where you deployed your model.3. Responses are received by the caller application as soon as the model produces the prediction.

B : 1. Events are sent by the sensors to Pub/Sub, consumed in real time, and processed by a Dataflow stream processing pipeline.2. The pipeline invokes the model for prediction and sends the predictions to another Pub/Sub topic.3. Pub/Sub messages containing predictions are then consumed by a downstream system for monitoring.

C : 1. Export your data to Cloud Storage using Dataflow.2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.3. Export the batch prediction job outputs from Cloud Storage and import them into Cloud SQL

D : 1. Export the data to Cloud Storage using the BigQuery command-line tool2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.3. Export the batch prediction job outputs from Cloud Storage and import them into BigQuery.

Answer: C