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Google Certified Professional Data Engineer Exam Sample Questions (Q299-Q304):

NEW QUESTION # 299
Your financial services company is moving to cloud technology and wants to store 50 TB of financial time- series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.
Which product should they use to store the data?

A. Cloud Bigtable
B. Google Cloud Datastore
C. Google BigQuery
D. Google Cloud Storage

Answer: A

Explanation:
https://cloud.google.com/blog/products/databases/getting-started-with-time-series-trend-predictions-using- gcp

NEW QUESTION # 300
Which of these sources can you not load data into BigQuery from?

A. File upload
B. Google Drive
C. Google Cloud SQL
D. Google Cloud Storage

Answer: C

Explanation:
You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.

NEW QUESTION # 301
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: D

NEW QUESTION # 302
You are developing an Apache Beam pipeline to extract data from a Cloud SQL instance by using JdbclO. You have two projects running in Google Cloud. The pipeline will be deployed and executed on Dataflow in Project A.
The Cloud SQL instance is running jn Project B and does not have a public IP address. After deploying the pipeline, you noticed that the pipeline failed to extract data from the Cloud SQL instance due to connection failure. You verified that VPC Service Controls and shared VPC are not in use in these projects. You want to resolve this error while ensuring that the data does not go through the public internet. What should you do?

A. Set up VPC Network Peering between Project A and Project B. Create a Compute Engine instance without external IP address in Project B on the peered subnet to serve as a proxy server to the Cloud SQL database.
B. Add the external IP addresses of the Dataflow worker as authorized networks in the Cloud SOL instance.
C. Set up VPC Network Peering between Project A and Project B. Add a firewall rule to allow the peered subnet range to access all instances on the network.
D. Turn off the external IP addresses on the Dataflow worker. Enable Cloud NAT in Project A.

Answer: A

Explanation:
Option A is incorrect because VPC Network Peering alone does not enable connectivity to Cloud SQL instances with private IP addresses. You also need to configure private services access and allocate an IP address range for the service producer network1.
Option B is incorrect because Cloud NAT does not support Cloud SQL instances with private IP addresses. Cloud NAT only provides outbound connectivity for resources that do not have public IP addresses, such as VMs, GKE clusters, and serverless instances2.
Option C is correct because it allows you to use a Compute Engine instance as a proxy server to connect to the Cloud SQL database over the peered network. The proxy server does not need an external IP address because it can communicate with the Dataflow workers and the Cloud SQL instance using internal IP addresses. You need to install the Cloud SQL Auth proxy on the proxy server and configure it to use a service account that has the Cloud SQL Client role.
Option D is incorrect because it requires you to assign public IP addresses to the Dataflow workers, which exposes the data to the public internet. This violates the requirement of ensuring that the data does not go through the public internet. Moreover, adding authorized networks does not work for Cloud SQL instances with private IP addresses.

NEW QUESTION # 303
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
B. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
C. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
D. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

Answer: B

NEW QUESTION # 304
......

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