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Exactprep Professional-Machine-Learning-Engineer Questions

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Question 40

You work for an online publisher that delivers news articles to over 50 million readers. You have built an AI model that recommends content for the company’s weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletter’s published date and the user remains on the page for at least one minute.

All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the model’s performance is above the acceptable baseline while minimizing cost. How should you monitor the model to determine when retraining is necessary?

Options:

A.

Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days.

B.

Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is created.

C.

Schedule a weekly query in BigQuery to compute the success metric.

D.

Schedule a daily Dataflow job in Cloud Composer to compute the success metric.

Question 41

You are using Kubeflow Pipelines to develop an end-to-end PyTorch-based MLOps pipeline. The pipeline reads data from BigQuery,

processes the data, conducts feature engineering, model training, model evaluation, and deploys the model as a binary file to Cloud Storage. You are

writing code for several different versions of the feature engineering and model training steps, and running each new version in Vertex Al Pipelines.

Each pipeline run is taking over an hour to complete. You want to speed up the pipeline execution to reduce your development time, and you want to

avoid additional costs. What should you do?

Options:

A.

Delegate feature engineering to BigQuery and remove it from the pipeline.

B.

Add a GPU to the model training step.

C.

Enable caching in all the steps of the Kubeflow pipeline.

D.

Comment out the part of the pipeline that you are not currently updating.

Question 42

You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

Options:

A.

Use a built-in model available on Al Platform Training

B.

Build your custom container to run jobs on Al Platform Training

C.

Build your custom containers to run distributed training jobs on Al Platform Training

D.

Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training

Question 43

You are developing an ML model to identify your company s products in images. You have access to over one million images in a Cloud Storage bucket. You plan to experiment with different TensorFlow models by using Vertex Al Training You need to read images at scale during training while minimizing data I/O bottlenecks What should you do?

Options:

A.

Load the images directly into the Vertex Al compute nodes by using Cloud Storage FUSE Read the images by using the tf .data.Dataset.from_tensor_slices function.

B.

Create a Vertex Al managed dataset from your image data Access the aip_training_data_uri

environment variable to read the images by using the tf. data. Dataset. Iist_flies function.

C.

Convert the images to TFRecords and store them in a Cloud Storage bucket Read the TFRecords by using the tf. ciata.TFRecordDataset function.

D.

Store the URLs of the images in a CSV file Read the file by using the tf.data.experomental.CsvDataset function.

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Exam Name: Google Professional Machine Learning Engineer
Last Update: Nov 1, 2024
Questions: 270
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