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NEW QUESTION 51
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 need to integrate with Google BigQuery.
• B. You will mostly run batch workloads with scans and writes, rather than frequently executing random reads of a small number of rows.
• C. You expect to store at least 10 TB of data.
• D. You will not use the data to back a user-facing or latency-sensitive application.

Answer: A

Explanation:
Explanation
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.
Reference: https://cloud.google.com/bigtable/docs/choosing-ssd-hdd

NEW QUESTION 52
You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?

• A. Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.
• B. Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.
• C. Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.
• D. Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.

Answer: B

Explanation:
Avro is compressed format and dataflow for parallel pipeline and bigquery for storage.

NEW QUESTION 53
Which Java SDK class can you use to run your Dataflow programs locally?

• A. DirectPipelineRunner
• B. LocalPipelineRunner
• C. MachineRunner
• D. LocalRunner

Answer: A

Explanation:
Explanation
DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization.
Useful for small local execution and tests
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRun

NEW QUESTION 54
You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

• A. Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
• B. Create a table in BigQuery, and append the new samples for CPU and memory to the table
• C. Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
• D. Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.

Answer: A

Explanation:
A tall and narrow table has a small number of events per row, which could be just one event, whereas a short and wide table has a large number of events per row. As explained in a moment, tall and narrow tables are best suited for time-series data. For time series, you should generally use tall and narrow tables. This is for two reasons: Storing one event per row makes it easier to run queries against your data. Storing many events per row makes it more likely that the total row size will exceed the recommended maximum (see Rows can be big but are not infinite). https://cloud.google.com/bigtable/docs/schema-design-time-series#patterns_for_row_key_design

NEW QUESTION 55
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