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NEW QUESTION 29
You are training a deep learning model for semantic image segmentation with reduced training time. While using a Deep Learning VM Image, you receive the following error: The resource 'projects/deeplearning-platforn/zones/europe-west4-c/acceleratorTypes/nvidia-tesla-k80' was not found. What should you do?
- A. Ensure that you have GPU quota in the selected region.
- B. Ensure that you have preemptible GPU quota in the selected region.
- C. Ensure that the selected GPU has enough GPU memory for the workload.
- D. Ensure that the required GPU is available in the selected region.
Answer: A
NEW QUESTION 30
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers
- A. Decrease the maximum number of trials during subsequent training phases.
- B. Decrease the range of floating-point values
- C. Decrease the number of parallel trials
- D. Set the early stopping parameter to TRUE
- E. Change the search algorithm from Bayesian search to random search.
Answer: A,E
NEW QUESTION 31
You are responsible for building a unified analytics environment across a variety of on-premises data marts. Your company is experiencing data quality and security challenges when integrating data across the servers, caused by the use of a wide range of disconnected tools and temporary solutions. You need a fully managed, cloud-native data integration service that will lower the total cost of work and reduce repetitive work. Some members on your team prefer a codeless interface for building Extract, Transform, Load (ETL) process. Which service should you use?
- A. Dataprep
- B. Cloud Data Fusion
- C. Apache Flink
- D. Dataflow
Answer: B
Explanation:
https://cloud.google.com/data-fusion/docs/concepts/overview#using_the_code-free_web_ui
NEW QUESTION 32
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?
- A. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery
- B. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model
- C. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
- D. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
Answer: D
Explanation:
(https://www.kubeflow.org/docs/components/pipelines/overview/pipelines-overview/
https://medium.com/google-cloud/how-to-build-an-end-to-end-propensity-to-purchase-solution-using-bigquery-ml-and-kubeflow-pipelines-cd4161f734d9#75c7
NEW QUESTION 33
Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:
A)
B)
C)
D)
- A. Option C
- B. Option B
- C. Option D
- D. Option A
Answer: C
NEW QUESTION 34
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