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NEW QUESTION 52
You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?
- A. Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).
- B. Convert the images to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().
- C. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training
- D. Create a tf.data.Dataset.prefetch transformation
Answer: A
NEW QUESTION 53
You are building an ML model to detect anomalies in real-time sensor dat a. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?
- A. 1 = BigQuery, 2 = Al Platform, 3 = Cloud Storage
- B. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions
- C. 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable
- D. 1 = Dataflow, 2 – Al Platform, 3 = BigQuery
Answer: B
NEW QUESTION 54
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. 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
- B. 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
- C. Use Al Platform Notebooks’ BigQuery cell magic to query the data, and ingest the results as a pandas dataframe
- D. 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
Answer: B
NEW QUESTION 55
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