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NEW QUESTION 47
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges.
Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?
- A. Create separate views to cover each month, and query from these views
- B. Convert all daily log tables into date-partitioned tables
- C. Convert the sharded tables into a single partitioned table
- D. Enable query caching so you can cache data from previous months
Answer: B
NEW QUESTION 48
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
* Decoupling producer from consumer
* Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
* Near real-time SQL query
* Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?
- A. Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
- B. Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
- C. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
- D. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
Answer: A
NEW QUESTION 49
Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?
- A. Maintain a database table to store the hash value and other metadata for each data entry.
- B. Assign global unique identifiers (GUID) to each data entry.
- C. Compute the hash value of each data entry, and compare it with all historical data.
- D. Store each data entry as the primary key in a separate database and apply an index.
Answer: A
NEW QUESTION 50
Which of the following is NOT true about Dataflow pipelines?
- A. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
- B. Dataflow pipelines can consume data from other Google Cloud services
- C. Dataflow pipelines can be programmed in Java
- D. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
Answer: A
Explanation:
Explanation
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/
NEW QUESTION 51
……
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