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Google Professional Machine Learning Engineer Sample Questions (Q148-Q153):

NEW QUESTION # 148
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?

• A. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
• B. Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
• C. Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.
• D. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters

Answer: C

NEW QUESTION # 149
During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it converges?

• A. Decrease the size of the training batch
• B. Increase the size of the training batch
• C. Decrease the learning rate hyperparameter
• D. Increase the learning rate hyperparameter

Answer: D

NEW QUESTION # 150
Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?

• A. Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines API.
• B. Use Al Platform Training to execute the experiments Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
• C. Use Al Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API
• D. Use Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAPI.

Answer: A

Explanation:
https://codelabs.developers.google.com/codelabs/cloud-kubeflow-pipelines-gis Kubeflow Pipelines (KFP) helps solve these issues by providing a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility. Cloud AI Pipelines makes it easy to set up a KFP installation.
https://www.kubeflow.org/docs/components/pipelines/introduction/#what-is-kubeflow-pipelines
"Kubeflow Pipelines supports the export of scalar metrics. You can write a list of metrics to a local file to describe the performance of the model. The pipeline agent uploads the local file as your run-time metrics. You can view the uploaded metrics as a visualization in the Runs page for a particular experiment in the Kubeflow Pipelines UI." https://www.kubeflow.org/docs/components/pipelines/sdk/pipelines-metrics/

NEW QUESTION # 151
You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?

• A. Use the BigQuery console to execute your query and then save the query results Into a new BigQuery table.
• B. Locate the Kubeflow Pipelines repository on GitHub Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipeline. Use the component to execute queries against BigQuery
• C. Write a Python script that uses the BigQuery API to execute queries against BigQuery Execute this script as the first step in your Kubeflow pipeline
• D. Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries

Answer: B

Explanation:
https://linuxtut.com/en/f4771efee37658c083cc/
https://github.com/kubeflow/pipelines/blob/master/components/gcp/bigquery/query/sample.ipynb
; https://v0-5.kubeflow.org/docs/pipelines/reusable-components/

NEW QUESTION # 152
A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.
Which architecture changes would ensure that provisioned resources are being utilized effectively?

• A. Redeploy the model on a P3dn instance.
• B. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.
• C. Redeploy the model as a batch transform job on an M5 instance.
• D. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.

Answer: D

NEW QUESTION # 153
......

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