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Google Professional-Machine-Learning-Engineer Exam Dumps Questions and Answers

Exam Code: Professional-Machine-Learning-Engineer
Exam Name: Google Professional Machine Learning Engineer
Certification: Machine Learning Engineer
Vendor: Google
Last Update: May 10, 2024
Questions: 268
10  Customers Passed Google Professional-Machine-Learning-Engineer
90%  Average Success In Real Exam
87%  Questions came word by word from this dump
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All Machine Learning Engineer Related Certification Exams

Other Google Certification Exams

Google Professional Machine Learning Engineer Questions and Answers

Questions 1

You work for a company that is developing a new video streaming platform. You have been asked to create a recommendation system that will suggest the next video for a user to watch. After a review by an AI Ethics team, you are approved to start development. Each video asset in your company’s catalog has useful metadata (e.g., content type, release date, country), but you do not have any historical user event data. How should you build the recommendation system for the first version of the product?

Options:

A.

Launch the product without machine learning. Present videos to users alphabetically, and start collecting user event data so you can develop a recommender model in the future.

B.

Launch the product without machine learning. Use simple heuristics based on content metadata to recommend similar videos to users, and start collecting user event data so you can develop a recommender model in the future.

C.

Launch the product with machine learning. Use a publicly available dataset such as MovieLens to train a model using the Recommendations AI, and then apply this trained model to your data.

D.

Launch the product with machine learning. Generate embeddings for each video by training an autoencoder on the content metadata using TensorFlow. Cluster content based on the similarity of these embeddings, and then recommend videos from the same cluster.

Questions 2

You have a custom job that runs on Vertex Al on a weekly basis The job is Implemented using a proprietary ML workflow that produces the datasets. models, and custom artifacts, and sends them to a Cloud Storage bucket Many different versions of the datasets and models were created Due to compliance requirements, your company needs to track which model was used for making a particular prediction, and needs access to the artifacts for each model. How should you configure your workflows to meet these requirement?

Options:

A.

Configure a TensorFlow Extended (TFX) ML Metadata database, and use the ML Metadata API.

B.

Create a Vertex Al experiment, and enable autologging inside the custom job

C.

Use the Vertex Al Metadata API inside the custom Job to create context, execution, and artifacts for each model, and use events to link them together.

D.

Register each model in Vertex Al Model Registry, and use model labels to store the related dataset and model information.

Questions 3

You work for an online grocery store. You recently developed a custom ML model that recommends a recipe when a user arrives at the website. You chose the machine type on the Vertex Al endpoint to optimize costs by using the queries per second (QPS) that the model can serve, and you deployed it on a single machine with 8 vCPUs and no accelerators.

A holiday season is approaching and you anticipate four times more traffic during this time than the typical daily traffic You need to ensure that the model can scale efficiently to the increased demand. What should you do?

Options:

A.

1, Maintain the same machine type on the endpoint.

2 Set up a monitoring job and an alert for CPU usage

3 If you receive an alert add a compute node to the endpoint

B.

1 Change the machine type on the endpoint to have 32 vCPUs

2. Set up a monitoring job and an alert for CPU usage

3 If you receive an alert, scale the vCPUs further as needed

C.

1 Maintain the same machine type on the endpoint Configure the endpoint to enable autoscalling based on vCPU usage.

2 Set up a monitoring job and an alert for CPU usage

3 If you receive an alert investigate the cause

D.

1 Change the machine type on the endpoint to have a GPU_ Configure the endpoint to enable autoscaling based on the GPU usage.

2 Set up a monitoring job and an alert for GPU usage.

3 If you receive an alert investigate the cause.

Next Question