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NEW QUESTION 45
You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps.
You have the following requirements:
* You will batch-load the posts once per day and run them through the Cloud Natural Language API.
* You will extract topics and sentiment from the posts.
* You must store the raw posts for archiving and reprocessing.
* You will create dashboards to be shared with people both inside and outside your organization.
You need to store both the data extracted from the API to perform analysis as well as the raw social media posts for historical archiving. What should you do?

• A. Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.
• B. Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.
• C. Store the social media posts and the data extracted from the API in BigQuery.
• D. Store the social media posts and the data extracted from the API in Cloud SQL.

Answer: A

Explanation:
Explanation

NEW QUESTION 46
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

• A. Add capacity (memory and disk space) to the database server by the order of 200.
• B. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
• C. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
• D. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.

Answer: C

Explanation:
It provides the least amount of inconvenience over using pre-specified date ranges or one table per clinic while also increasing performance due to avoiding self-joins.

NEW QUESTION 47
After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You've loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison.
What should you do?

• A. Select random samples from the tables using the RAND() function and compare the samples.
• B. Create stratified random samples using the OVER() function and compare equivalent samples from each table.
• C. Select random samples from the tables using the HASH() function and compare the samples.
• D. Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sorting. Compare the hashes of each table.

Answer: C

NEW QUESTION 48
Which of these operations can you perform from the BigQuery Web UI?

• A. Upload a 20 MB file.
• B. Load data with nested and repeated fields.
• C. Upload multiple files using a wildcard.
• D. Upload a file in SQL format.

Answer: B

Explanation:
You can load data with nested and repeated fields using the Web UI.
You cannot use the Web UI to:
- Upload a file greater than 10 MB in size
- Upload multiple files at the same time
- Upload a file in SQL format
All three of the above operations can be performed using the "bq" command.

NEW QUESTION 49
You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?

• A. Update the Cloud Dataflow pipeline inflight by passing the --updateoption with the --jobNameset to a new unique job name
• B. Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code
• C. Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code
• D. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name

Answer: D

Explanation:
Explanation/Reference: https://cloud.google.com/dataflow/docs/guides/updating-a-pipeline

NEW QUESTION 50
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