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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 136–140 (page: 29)

Updated On: 25-Oct-2026
Page 29 of 82
PDF with 402 Questions

You have a high-traffic ecommerce application that uses a recommendation model deployed on an Agent Platform endpoint. During flash sales, traffic quickly spikes from 1,000 to 50,000 queries per second (QPS). During these spikes, the application returns 503: Service Unavailable errors because the autoscaler cannot provision new resources quickly enough to meet the sudden demand. Your need to eliminate service disruptions during the start of the sale. How should you configure the endpoint?

  1. Change the machine type to an a2-highgpu-1g instance with an NVIDIA A100 GPU.
  2. Increase the min_replica_count to a baseline that covers a significant portion of the expected spike, and set the max_replica_count to 100.
  3. Set the min_replica_count to 1 and the max_replica_count to 100 in the deployed model’s automatic scaling configuration.
  4. Deploy the model to a private Agent Platform endpoint, and autoscale based on GPU utilization.

Answer(s): B

Explanation:

Increasing the minimum replica count pre-provisions enough serving capacity to absorb the initial traffic surge before autoscaling can add more replicas. Setting a sufficiently high maximum replica count then allows the endpoint to continue scaling as demand grows, preventing startup-period 503 errors during flash sales.



You are working with a dataset that contains customer transactions. You need to build an ML model to predict customer purchase behavior. You plan to develop the model in BigQuery ML, and export it to Cloud Storage for online prediction. You notice that the input data contains a few categorical features, including product category and payment method. You want to deploy the model as quickly as possible.
What should you do?

  1. Use the TRANSFORM clause with the ML.ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features.
  2. Use the ML.ONE_HOT_ENCODER function on the categorical features and select the encoded categorical features and non-categorical features as inputs to create your model.
  3. Use the CREATE MODEL statement and select the categorical and non-categorical features.
  4. Use the ML.MULTI_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.

Answer(s): B



You are training an ML model on a large dataset. You are using a TPU to accelerate the training process. You notice that the training process is taking longer than expected. You discover that the TPU is not reaching its full capacity.
What should you do?

  1. Increase the learning rate.
  2. Increase the number of epochs.
  3. Decrease the learning rate.
  4. Increase the batch size.

Answer(s): D



You are training models in Vertex AI by using data that spans across multiple Google Cloud projects. You need to find, track, and compare the performance of the different versions of your models.
Which Google Cloud services should you include in your ML workflow?

  1. Dataplex, Vertex AI Feature Store, and Vertex AI TensorBoard
  2. Vertex AI Pipelines, Vertex AI Feature Store, and Vertex AI Experiments
  3. Dataplex, Vertex AI Experiments, and Vertex AI ML Metadata
  4. Vertex AI Pipelines, Vertex AI Experiments, and Vertex AI Metadata

Answer(s): D



You have created a Vertex AI pipeline that automates custom model training. You want to add a pipeline component that enables your team to most easily collaborate when running different executions and comparing metrics both visually and programmatically.
What should you do?

  1. Add a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Query the table to compare different executions of the pipeline. Connect BigQuery to Looker Studio to visualize metrics.
  2. Add a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Load the table into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.
  3. Add a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Use Vertex AI Experiments to compare different executions of the pipeline. Use Vertex AI TensorBoard to visualize metrics.
  4. Add a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Load the Vertex ML Metadata into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.

Answer(s): C



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