Senior Google Cloud Solutions Architect Interview Questions and Answers

16 min readJan 25, 2023

Q)Write python code use case: want to filter best user articles which are posted in the telegram group daily basis top three best articles users want to filter from all how to do that?

A) Here is an example of Python code that filters the top three best articles posted in a Telegram group on a daily basis:

import requests
import json
# Telegram API endpoint for getting updates
url = "https://api.telegram.org/bot<your_bot_token>/getUpdates"
# Get all updates from the Telegram group
response = requests.get(url)
# Parse the JSON response
data = json.loads(response.text)
# Initialize a list to store the articles
articles = []
# Iterate over the updates
for update in data["result"]:
    # Check if the update is a message with text
    if "message" in update and "text" in update["message"]:
        # Append the text to the list of articles
        articles.append(update["message"]["text"])
# Sort the articles by their length
articles.sort(key=len, reverse=True)
# Print the top three articles
print("Top 3 articles:")
for i in range(3):
    print(f"{i+1}. {articles[i]}")

This code uses the Telegram Bot API to get all updates from the Telegram group. It then parses the JSON response and iterates over the updates to filter out only the updates that are messages with text. The text of these messages is then added to a list of articles. The list of articles is then sorted by their length in descending order and the top three articles are printed.

You will need to replace <your_bot_token> with your Telegram bot token and adjust the code according to your requirement.

Note that this code is just an example and you may need to adjust it according to your specific requirements, such as handling errors, pagination, filtering specific user and so on.

Q)Write code in python on how to reverse text paragraphs?

def reverse_paragraph(paragraph):
    # split the paragraph into a list of words
    words = paragraph.split()
    # reverse the order of the words
    words.reverse()
    # join the words back into a single string
    reversed_paragraph = " ".join(words)
    return reversed_paragraph
# Test the function
original_text = "This is a test paragraph."
reversed_text = reverse_paragraph(original_text)
print("Original text:", original_text)
print("Reversed text:", reversed_text)

This code defines a function reverse_paragraph that takes a paragraph as an input and returns the reversed text. The function first splits the paragraph into a list of words using the split() method. Then it reverses the order of the words using the reverse() method. Finally, it joins the words back into a single string using the join() method.

The function is then tested by passing a sample paragraph to it and the original and reversed text are printed.

You can use this function to reverse any given text, it can be a paragraph or a sentence.

Note that this code does not handle punctuation and special characters, if you want to handle that as well, you can use a list comprehension to filter out any non-alphanumeric characters before reversing the words.

Q)What is the array and matrix difference in python?

In Python, an array is a data structure that stores a collection of items,
 all of the same type. It is used to store numerical data and is often used 
for mathematical operations. 

A matrix, on the other hand, is a two-dimensional 
array, with rows and columns. It is often used to represent linear 
transformations in mathematics. Both arrays and matrices can be created 
and manipulated using the NumPy library in Python.

Q)What is the difference between the shared table and the partition table in BigQuery?

In BigQuery, a shared table is a table that can be accessed and queried 
by multiple projects within the same organization. This means that 
the table is not tied to a specific

 project and can be shared across multiple projects.
A partitioned table, on the other hand, is a table that is divided 
into smaller, more manageable units called partitions. Each partition
 is a portion of the table that is stored separately, and can be accessed 
and queried independently. 

This allows for more efficient querying and improves performance, 
especially when working with large datasets. Partitioned tables 
can be partitioned on a specific column, such as a timestamp column,
 which allows for easy querying of data within a specific time range.
Shared table is a way of sharing the table across projects, 
and partitioned table is the way of data organization and 
efficient querying.

Q)How to save costs for the same query execution again and again in BigQuery?

There are several ways to save cost when executing the same query multiple 
times in BigQuery:

Caching: BigQuery automatically caches query results for 24 hours. 
If you run the same query multiple times within 24 hours, the 
results will be retrieved from the cache rather than re-executed, 
which can save cost.

Persistent query: BigQuery allows you to save the results of a 
query as a new table. Once you have done this, you can query the 
new table instead of running the original query, which can save cost.
Materialized View: BigQuery allows you to create materialized

 views, which are pre-aggregated or pre-computed tables that are 
populated by running a query. You can then query the materialized 
view instead of running the original query, which can save cost.
Use Partitioned table : If you are querying a large table and only 
need a subset of the data, you can partition the table based on 
the column that you will use to filter the data. This allows
 BigQuery to scan only the partitions that contain the relevant 
data, which can save cost.

Use Billing-tier: BigQuery allows you to set a per-query 
billing tier, which allows you to control how much you spend on each query. 
You can set a lower billing tier for queries that are less critical, 
which can save cost.

Query optimization: Some of the best practices of query optimization 
is to avoid using SELECT *, use column aliases, use filtering conditions 
in the where clause , group by and order by on the smaller dataset.
By using the above methods, you can significantly reduce the cost of 
executing the same query multiple times in BigQuery.

Q)What is the difference between legacy and standard ANSI SQL?

BigQuery supports two SQL dialects: Legacy SQL and Standard SQL.
Legacy SQL is the original dialect of SQL used by BigQuery. 

It is a SQL-like language that is similar to the SQL that is used by 
other data warehousing systems. Legacy SQL is more flexible and has 
more features than Standard SQL, but it also has some limitations, 
such as not being fully compatible with the SQL standard.

Standard SQL is a more recent dialect of SQL that is based on the 
SQL standard. It is more powerful and expressive than Legacy SQL 
and has a more consistent syntax. Standard SQL also supports features 
that are not available in Legacy SQL, such as window functions, 

standard data types, and the ability to join tables with arbitrary conditions.
In summary, the main difference between Legacy SQL and 
Standard SQL is that Legacy SQL is more flexible and 

has more features, but it is not fully compatible with the
 SQL standard, while Standard SQL is more powerful and expressive, 
and is based on the SQL standard.

Q)What data type is supported in BigQuery?

BigQuery supports several data types for storing and querying data:
Numeric: INTEGER, FLOAT, NUMERIC
String: STRING, BYTES
Boolean: BOOL
Temporal: DATE, TIME, DATETIME, TIMESTAMP
Spatial: GEOGRAPHY, POINT
Array: ARRAY
Struct: STRUCT
Legacy SQL only: RECORD
Standard SQL only: INT64, FLOAT64

Q)What data format is supported BigQuery?

BigQuery supports several data formats for loading and exporting data:
CSV (Comma-separated values)
JSON (JavaScript Object Notation)
Avro (Apache Avro)
Parquet (Apache Parquet)
ORC (Optimized Row Columnar)
AVRO (Apache Avro)
Datastore Backup
Newline-delimited JSON (NDJSON)
Cloud Firestore export

Q)Can we convert Avro to CSV data format for BigQuery?

Yes, it is possible to convert Avro data to CSV format for BigQuery. 
There are several ways to do this:
Using the bq command-line tool: You can use the bq extract command to extract 
data from a BigQuery table in Avro format, and then use a tool such as Apache
 Avro tools to convert the Avro data to CSV.

Using the BigQuery API: You can use the BigQuery API to extract data 
from a table in Avro format and then use a tool such as Apache Avro tools 
to convert the Avro data to CSV.

Using a third-party tool: There are several third-party tools available
 that can be used to convert Avro data to CSV, such as Apache Nifi, Talend, 
Apache Beam, and more.

The process of conversion will be different depends on the tool you use, 
in general, you will need to load the Avro data into the tool, 
then specify the CSV format as the output format and then export 
the data into CSV.

It's worth noting that, Avro is a row-based format and CSV is a 
column-based format, the conversion process may not be trivial 
and may require additional transformation steps. Therefore, 
depending on the complexity of the data, it might be best 
to use a specialized tool, or a data pipeline that can handle 
the conversion process, instead of doing it manually.

Q)if one customer wanted to migrate their workload on-prem to the cloud which cloud would be better for customers how can we suggest to them which public cloud would be better for customers?

Answers:-It depends on the specific requirements of the customer and the workload they are looking to migrate. Each cloud provider has its own strengths and weaknesses, and the best choice will depend on the customer’s needs. Here are a few things to consider when suggesting a cloud provider to a customer:

Scalability: Consider the customer’s current and future scalability needs. Some cloud providers offer more flexible and customizable scalability options than others.
Cost: Compare the cost of the different cloud providers and consider the customer’s budget. Some cloud providers may offer more cost-effective solutions for certain workloads.
Services and features: Consider the customer’s specific requirements, such as data storage, analytics, machine learning, and IoT services. Each cloud provider offers a different set of services and features, and it’s important to choose one that offers the services that best fit the customer’s needs.
Compliance and security: Some cloud providers have certifications for specific compliance and security standards that might be required by the customer.
Support: Consider the customer’s level of technical expertise and the level of support they may require. Some cloud providers offer more comprehensive support and consulting services than others.

Based on the above factors, the three major cloud providers are:

Amazon Web Services (AWS)
Microsoft Azure
Google Cloud Platform (GCP)

AWS is known for its wide range of services, flexibility, and scalability. Azure is known for its strong integration with other Microsoft products and its focus on hybrid cloud solutions. GCP is known for its powerful data analytics and machine learning capabilities, and its strong focus on big data and AI.

It’s important to note that it’s not just a one-time decision, the customer should also consider the future growth and the ability to integrate with other products and services they might use. I would suggest conducting a thorough assessment of the customer’s workload and requirements and then comparing the offerings of different cloud providers to determine which one best meets the customer’s needs.

Q)What are the prerequisites for the Google Cloud Platform setup checklist?

The checklist includes 10 tasks that have step-by-step procedures. Some tasks can be accomplished multiple ways; we describe the most common path. If you need to deviate from the recommended path, keep track of your choices because they may be important later in the checklist.

Cloud identity and organization: This involves setting up an organization and creating an organization policy. This is typically the first step in setting up a Google Cloud account and is used to manage access and permissions for users and groups within the organization.
Users and groups: This involves creating and managing user accounts and groups within the organization. This includes setting up roles and permissions for different users and groups, as well as managing access to resources.
Administrative access: This involves setting up administrative access for users and groups within the organization. This includes setting up roles and permissions for different users and groups, as well as managing access to resources.
Set up billing: This involves setting up a billing account and configuring budgets and alerts. This is used to manage costs associated with the organization’s usage of Google Cloud resources.
Resource hierarchy: This involves creating and configuring a resource hierarchy for the organization. This includes setting up projects, folders, and other resources.
Access: This involves configuring access to resources within the organization. This includes setting up roles and permissions for different users and groups, as well as managing access to resources.
Networking: This involves configuring networking for the organization. This includes setting up VPC networks and subnets, as well as configuring load balancing and VPN connections.
Monitoring and logging: This involves setting up monitoring and logging for the organization. This includes configuring Cloud Monitoring and Stackdriver logging.
Security: This involves setting up security for the organization. This includes configuring firewall rules, identity and access management, and other security measures.
Support: This involves setting up support for the organization. This includes configuring support plans and accessing support resources.

It’s important to note that this is a general checklist and additional steps may be needed depending on the specific requirements of your project and organization. It’s also important to follow best practices for security and compliance, and to regularly review and update your organization’s policies and configurations.

Q)How to manage organization policy in the google cloud platform?

Managing organization policies in Google Cloud Platform (GCP) involves several steps:

Sign in to the GCP Console with an account that has the Organization Administrator role.
In the GCP Console, navigate to the “Organization policies” page by clicking the hamburger menu in the top left corner, and then selecting “Organization policies”.
On the “Organization policies” page, you can view and edit the existing policies for your organization. For example, you can set policies that enforce specific resource settings, such as minimum firewall rules, or that restrict certain types of resources from being created.
To create a new policy, click the “Create policy” button. This will open a form where you can specify the type of policy, the resource it applies to, and the conditions that trigger the policy.
Once you’ve finished creating your policy, click the “Create” button to save it. The policy will be immediately enforced for all existing and future resources in the organization.
To edit an existing policy, click on the policy in the list to open the policy details page, then click the “Edit” button. Make the necessary changes and then click the “Save” button.
To delete a policy, click on the policy in the list to open the policy details page, then click the “Delete” button.

It’s important to review and test your organization policies before deploying them to ensure they align with your organization’s needs and policies. Also, regularly review your policies to ensure they still align with your organization’s needs.

Q)How to manage access management in GCP?

In Google Cloud Platform (GCP), you can manage access management by using Identity and Access Management (IAM) to control who has access to your resources and what actions they can perform. The basic steps for managing access management in GCP are:

Create and manage projects: A project is the top-level container for all resources in GCP. You can create and manage projects in the GCP Console, or using the gcloud command-line tool.
Create and manage service accounts: A service account is a special type of account that belongs to your application or a virtual machine (VM) instance, rather than to an individual user. You can create and manage service accounts in the GCP Console, or using the gcloud command-line tool.
Define roles and permissions: Roles define what actions a user or service account can perform on a resource. You can define custom roles or use predefined roles provided by GCP. You can set the roles and permissions using the GCP Console, or using the gcloud command-line tool.
Assign roles to users or service accounts: You can assign roles to specific users or service accounts, either individually or as a group, using the GCP Console, or using the gcloud command-line tool.
Monitor and audit access: GCP provides a number of tools for monitoring and auditing access to resources, such as Stackdriver Logging and Stackdriver Monitoring.
Use Access Context Manager to define and enforce access policies based on identity, location, and other attributes.

These are the basic steps for managing access management in GCP, but there are many more options and details depending on the specific requirements of the project.

Q)Can we upload data from Cloudsql to BigQuery?

Yes, it is possible to upload data from Cloud SQL to BigQuery in Google Cloud Platform (GCP). There are a few ways to achieve this:

One way to upload data from Cloud SQL to BigQuery is by using the Cloud SQL export feature to automatically export data from Cloud SQL to a Cloud Storage bucket and then using the BigQuery load job feature to load the data from the Cloud Storage bucket into BigQuery.
Another way is to use the BigQuery Data Transfer Service to schedule and manage recurring data transfers from Cloud SQL to BigQuery. This can be done by creating a transfer configuration in the BigQuery web UI or using the GCP Console or the gcloud command-line tool.
Another way to upload data from Cloud SQL to BigQuery is by using the Cloud SQL Proxy, which allows you to connect to a Cloud SQL instance from a local client, and then use the BigQuery connector for Apache Nifi, Apache Beam or Apache Airflow to move data from Cloud SQL to BigQuery.
Additionally, you can use a third-party ETL tool, such as Talend, to connect Cloud SQL to BigQuery and move data between the two services.

It’s important to note that the above options require that your Cloud SQL instances and BigQuery datasets are in the same project or in the same organization, and that your Cloud SQL instances are in the same region as the BigQuery datasets.

It’s also important to keep in mind that BigQuery is a data warehouse, optimized for scanning large amount of data while Cloud SQL is a relational database, optimized for transactional workloads. Each product has it’s own use cases, so it’s important to understand what you want to achieve and the best technology for that goal.

The Cloud SQL export feature in Google Cloud Platform (GCP) allows you to automatically export data from a Cloud SQL for MySQL or Cloud SQL for PostgreSQL instance to a Cloud Storage bucket in the same project. The export process creates a SQL dump file containing the data and schema of the database.

Q)What are the option to use CloudSQL Export?

Here are the basic steps to use the Cloud SQL export feature:

Create a Cloud Storage bucket in the same project as the Cloud SQL instance that you want to export data from.
In the GCP Console, navigate to the Cloud SQL instances page and select the instance that you want to export data from.
Under the “Backups” section, click the “Export” button.
In the export dialog, select the Cloud Storage bucket that you want to export the data to, and configure the export options. You can choose to export all databases or a specific database, and you can choose to include only the data or both the data and the schema.
Click “Export” to start the export process. The export process may take some time depending on the size of the data.
Once the export process is complete, you will find the SQL dump file in the Cloud Storage bucket that you specified.

It’s important to note that the export process will create a new SQL dump file each time it runs, so you will need to manage the files in the Cloud Storage bucket to prevent filling up the storage.

Also, if you want to import the data back into Cloud SQL, you can use the Cloud SQL import feature which will import the data from the exported SQL dump file back into the Cloud SQL instance.

Q)What is the difference between Bigtable and BigQuery?

Bigtable and BigQuery are both Google-developed technologies, but they serve different purposes and have different use cases.

Bigtable is a distributed, column-family store designed to handle very large amounts of data across many commodity servers. It is a low-level, NoSQL database that is designed to scale horizontally and handle high write and read loads. It’s used to power several Google services, including Google Search and Google Analytics.

BigQuery, on the other hand, is a fully-managed, cloud-native data warehouse that enables super-fast SQL queries using the processing power of Google’s infrastructure. It is built on top of Bigtable and is used for analyzing large sets of data. It provides an SQL-like interface for querying data stored in Bigtable and allows for advanced analysis, such as JOINs, aggregate functions, and more.

In summary, Bigtable is a NoSQL database, designed for handling large amounts of data and high write/read loads, whereas BigQuery is a data warehousing and SQL analysis service, built on top of Bigtable, that allows for advanced data analysis using SQL like syntax.

At a high level, Bigtable is a NoSQL wide-column database. It’s optimized for low latency, large numbers of reads and writes, and maintaining performance at scale. Bigtable use cases are of a certain scale or throughput with strict latency requirements, such as IoT, AdTech, FinTech, and so on. If high throughput and low latency at scale are not priorities for you, then another NoSQL database like Firestore might be a better fit.

Bigtable is a NoSQL wide-column database optimized for heavy reads and writes.

On the other hand, BigQuery is an enterprise data warehouse for large amounts of relational structured data. It is optimized for large-scale, ad-hoc SQL-based analysis and reporting, which makes it best suited for gaining organizational insights. You can even use BigQuery to analyze data from Cloud Bigtable.

BigQuery is an enterprise data warehouse for large amounts of relational structured data

Can you explain the main components of Google Cloud Platform and their respective use cases?
How would you design a highly available and scalable architecture on GCP for a web application?
What are the differences between Google Compute Engine and Google Kubernetes Engine? When would you choose one over the other?
Explain the concept of virtual private clouds (VPC) in GCP and how you would design network architecture to ensure secure communication between resources.
How do you handle data storage and database requirements on GCP? Can you explain the various storage options available?
What is the purpose of Cloud Pub/Sub in GCP, and how would you use it in an architecture?
Describe your experience with deploying and managing containers on GCP using tools like Docker and Kubernetes.
How would you design a disaster recovery and backup strategy for a critical application running on GCP?
Explain the concept of Identity and Access Management (IAM) in GCP and how you would set up proper access controls for a project.
Have you worked with BigQuery on GCP? Can you explain its features and use cases?
What are some best practices for optimizing cost and resource utilization on GCP?
How do you ensure the security of data and applications on GCP? What security measures and practices would you implement?
Have you worked with GCP’s serverless offerings, such as Cloud Functions or Cloud Run? Can you describe their benefits and use cases?
Can you explain how GCP integrates with other cloud providers or on-premises infrastructure?
Have you worked with GCP’s machine learning services, such as AutoML or TensorFlow? Can you describe a use case where you applied machine learning on GCP?

All the best for interviews :)