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NEW QUESTION 37
Which of the following statements about Legacy SQL and Standard SQL is not true?
- A. You need to set a query language for each dataset and the default is Standard SQL.
- B. If you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
- C. Standard SQL is the preferred query language for BigQuery.
- D. One difference between the two query languages is how you specify fully-qualified table names (i.e.
table names that include their associated project name).
You do not set a query language for each dataset. It is set each time you run a query and the default query language is Legacy SQL.
Standard SQL has been the preferred query language since BigQuery 2.0 was released. In legacy SQL, to query a table with a project-qualified name, you use a colon, :, as a separator. In standard SQL, you use a period, ., instead.
Due to the differences in syntax between the two query languages (such as with project-qualified table names), if you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
NEW QUESTION 38
You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?
- A. Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.
- B. Change the data pipeline to use BigQuery for storing stock trades, and update your application.
- C. Use Cloud Dataflow to write summary of each day’s stock trades to an Avro file on Cloud Storage. Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.
- D. Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.
NEW QUESTION 39
You work for an advertising company, and you’ve developed a Spark ML model to predict click-through rates at advertisement blocks. You’ve been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be closing soon, so a rapid lift-and-shift migration is necessary. However, the data you’ve been using will be migrated to migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?
- A. Rewrite your models on TensorFlow, and start using Cloud ML Engine
- B. Use Cloud ML Engine for training existing Spark ML models
- C. Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
- D. Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery
NEW QUESTION 40
You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?
- A. Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.
- B. Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.
- C. Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.
- D. Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.
NEW QUESTION 41
Which of the following is NOT a valid use case to select HDD (hard disk drives) as the storage for Google Cloud Bigtable?
- A. You expect to store at least 10 TB of data.
- B. You will not use the data to back a user-facing or latency-sensitive application.
- C. You will mostly run batch workloads with scans and writes, rather than frequently executing random reads of a small number of rows.
- D. You need to integrate with Google BigQuery.
For example, if you plan to store extensive historical data for a large number of remote- sensing devices and then use the data to generate daily reports, the cost savings for HDD storage may justify the performance tradeoff. On the other hand, if you plan to use the data to display a real-time dashboard, it probably would not make sense to use HDD storage-reads would be much more frequent in this case, and reads are much slower with HDD storage.
NEW QUESTION 42
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