通過Professional-Data-Engineer考試資料認證考試好像是一件很難的事情。已經報名參加考試的你，現在正在煩惱應該怎麼準備考試嗎？如果是這樣的話，請看下面的內容，我現在告訴你通過Professional-Data-Engineer考試資料考試的捷徑。可以讓你一次就通過考試的優秀的Professional-Data-Engineer考試資料考試資料出現了。 如果你不想因為考試浪費太多的時間與精力，那麼Goldmile-Infobiz的Professional-Data-Engineer考試資料考古題無疑是你最好的選擇。用這個資料你可以提高你的學習效率，從而節省很多時間。 因為這個考古題包括了真實考試中的所有試題，所以只是這樣你也可以通過考試。
而且通過 Google Professional-Data-Engineer考試資料 認證考試也不是很簡單的。
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Google Professional-Data-Engineer考試資料 - 在你使用之後，相信你會很滿意我們的產品的。
Google Professional-Data-Engineer考試資料 認證考試是一個檢驗IT專業知識的認證考試。Goldmile-Infobiz是個能幫你快速通過Google Professional-Data-Engineer考試資料 認證考試的網站，很多參加Google Professional-Data-Engineer考試資料 認證考試的人花費大量的時間和精力，或者花錢報補習班，都是為了通過Google Professional-Data-Engineer考試資料 認證考試。Goldmile-Infobiz可以讓你不需要花費那麼多時間，金錢和精力，Goldmile-Infobiz會為你提供針對性訓練來準備Google Professional-Data-Engineer考試資料認證考試，僅需大約20個小時你就能通過考試。
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Professional-Data-Engineer PDF DEMO:
QUESTION NO: 1
MJTelco is building a custom interface to share data. They have these requirements:
* They need to do aggregations over their petabyte-scale datasets.
* They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?
A. Cloud Datastore and Cloud Bigtable
B. Cloud Bigtable and Cloud SQL
C. BigQuery and Cloud Bigtable
D. BigQuery and Cloud Storage
Answer: C
QUESTION NO: 2
You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? Choose 2 answers.
A. Publisher throughput quota is too small.
B. The subscriber code cannot keep up with the messages.
C. The subscriber code does not acknowledge the messages that it pulls.
D. Error handling in the subscriber code is not handling run-time errors properly.
E. Total outstanding messages exceed the 10-MB maximum.
Answer: B,D
QUESTION NO: 3
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?
A. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
B. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query
BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
C. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
D. Load the data every 30 minutes into a new partitioned table in BigQuery.
Answer: D
QUESTION NO: 4
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?
A. A Dataproc cluster cannot have only preemptible workers.
B. Preemptible workers cannot store data.
C. Preemptible workers cannot use persistent disk.
D. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
Answer: A,B
Explanation
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster:
Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data.
Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
Persistent disk size-As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits.
Reference: https://cloud.google.com/dataproc/docs/concepts/preemptible-vms
QUESTION NO: 5
Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got "-" at [4:11]
SELECT age
FROM
bigquery-public-data.noaa_gsod.gsod
WHERE
age != 99
AND_TABLE_SUFFIX = '1929'
ORDER BY
age DESC
Which table name will make the SQL statement work correctly?
A. 'bigquery-public-data.noaa_gsod.gsod*`
B. 'bigquery-public-data.noaa_gsod.gsod'*
C. 'bigquery-public-data.noaa_gsod.gsod'
D. bigquery-public-data.noaa_gsod.gsod*
Answer: A
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Updated: May 27, 2022