When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?

• A.Your gcloud does not have access to the BigQuery resources
• B.BigQuery cannot be accessed from local machines
• C.You are missing gcloud on your machine
• D.Pipelines cannot be run locally

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Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them.
Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?

• A.Specify the Tableobject in the code.
• B.Use .fromQuery operation to read specific fields from the table.
• C.Use of both the Google BigQuery TableSchema and TableFieldSchema classes.
• D.Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.

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Which TensorFlow function can you use to configure a categorical column if you don't know all of the possible values for that column?

• A.categorical_column_with_vocabulary_list
• B.categorical_column_with_hash_bucket
• C.categorical_column_with_unknown_values
• D.sparse_column_with_keys

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You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

• A.Use the TABLE_DATE_RANGE function
• B.Use the WHERE_PARTITIONTIME pseudo column
• C.Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
• D.Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD

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You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time. Which two methods can accomplish this? Choose 2 answers.

• A.Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.
• B.Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
• C.Use managed exportm, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
• D.Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
• E.Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.

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