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NEW QUESTION 43
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?

A. Load the data into BigQuery and read the data from BigQuery.B. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)C. Load the data into Cloud Bigtable, and read the data from BigtableD. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage

Answer: D

 

NEW QUESTION 44
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

A. The training channel identifying the location of training data on an Amazon S3 bucket.B. Hyperparameters in a JSON array as documented for the algorithm used.C. The output path specifying where on an Amazon S3 bucket the trained model will persist.D. The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.E. The validation channel identifying the location of validation data on an Amazon S3 bucket.F. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.

Answer: A,C,F

Explanation:
Explanation

 

NEW QUESTION 45
You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

A. Export the model to BigQuery ML.B. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.C. Use Dataflow with the SavedModel to read the data from BigQueryD. Deploy and version the model on Al Platform.

Answer: A

 

NEW QUESTION 46
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