Google PROFESSIONAL-MACHINE-LEARNING-ENGINEER Exam Questions & Answers

While monitoring your model training's GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the execution time of your input pipeline. What should you do?

A. Increase the CPU load

B. Add caching to the pipeline

C. Increase the network bandwidth

D. Add parallel interleave to the pipeline

You work for a company that provides an anti-spam service that flags and hides spam posts on social media platforms. Your company currently uses a list of 200,000 keywords to identify suspected spam posts. If a post contains more than a few of these keywords, the post is identified as spam. You want to start using machine learning to flag spam posts for human review. What is the main advantage of implementing machine learning for this business case?

A. Posts can be compared to the keyword list much more quickly.

B. New problematic phrases can be identified in spam posts.

C. A much longer keyword list can be used to flag spam posts.

D. Spam posts can be flagged using far fewer keywords.

You developed a Vertex AI pipeline that trains a classification model on data stored in a large BigQuery table. The pipeline has four steps, where each step is created by a Python function that uses the KubeFlow v2 API. The components have the following names:

You launch your Vertex AI pipeline as the following:

You perform many model iterations by adjusting the code and parameters of the training step. You observe high costs associated with the development, particularly the data export and preprocessing steps. You need to reduce model development costs.

What should you do?

A. Change the components’ YAML filenames to export.yaml, preprocess,yaml, f "train-{dt}.yaml", f"calibrate-{dt).vaml".

B. Add the {"kubeflow.v1.caching": True} parameter to the set of params provided to your PipelineJob.

C. Move the first step of your pipeline to a separate step, and provide a cached path to Cloud Storage as an input to the main pipeline.

D. Change the name of the pipeline to f"my-awesome-pipeline-{dt}".