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NEW QUESTION 41
You have a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. You need to quickly build, test, and deploy a service that will automatically classify future written requests into one of the categories. How should you configure the pipeline?
- A. Create a TensorFlow model using Google's BERT pre-trained model. Build and test a classifier, and deploy the model using Vertex AI.
- B. Use BigQuery ML to build and test a logistic regression model to classify incoming requests. Use BigQuery ML to perform inference.
- C. Use the Cloud Natural Language API to obtain metadata to classify the incoming cases.
- D. Use AutoML Natural Language to build and test a classifier. Deploy the model as a REST API.
Answer: D
NEW QUESTION 42
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?
- A. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
- B. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
- C. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
- D. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
Answer: D
NEW QUESTION 43
You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano. Scikit-team, and custom libraries. What should you do?
- A. Create a library of VM images on Compute Engine; and publish these images on a centralized repository
- B. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
- C. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJob
- D. Use the Al Platform custom containers feature to receive training jobs using any framework
Answer: B
NEW QUESTION 44
You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?
- A. Build a collaborative-based filtering model
- B. Build a knowledge-based filtering model
- C. Build a regression model using the features as predictors
- D. Build a classification model
Answer: A
Explanation:
Reference:
https://developers.google.com/machine-learning/recommendation/collaborative/basics
https://cloud.google.com/architecture/recommendations-using-machine-learning-on-compute-engine#filtering_the_data
NEW QUESTION 45
You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an Al Platform notebook. What should you do?
- A. Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance
- B. Use Al Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe
- C. Download your table from BigQuery as a local CSV file, and upload it to your Al Platform notebook instance Use pandas. read_csv to ingest the file as a pandas dataframe
- D. From a bash cell in your Al Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutii cp to copy the data into the notebook Use pandas. read_csv to ingest the file as a pandas dataframe
Answer: B
Explanation:
Refer to this link for details: https://cloud.google.com/bigquery/docs/bigquery-storage-python-pandas First 2 points talks about querying the data.
Download query results to a pandas DataFrame by using the BigQuery Storage API from the IPython magics for BigQuery in a Jupyter notebook.
Download query results to a pandas DataFrame by using the BigQuery client library for Python.
Download BigQuery table data to a pandas DataFrame by using the BigQuery client library for Python.
Download BigQuery table data to a pandas DataFrame by using the BigQuery Storage API client library for Python.
https://googleapis.dev/python/bigquery/latest/magics.html#ipython-magics-for-bigquery
https://cloud.google.com/bigquery/docs/bigquery-storage-python-pandas
NEW QUESTION 46
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