Trending March 2024 # Most Asked Interview Questions On Apache Spark # Suggested April 2024 # Top 11 Popular

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Introduction

Apache S capabilities make it 100 times faster than Hadoop. It has the ability to process a huge amount of data in such a short period. The most important feature of Spark is in-memory data processing. Here is a list of interview questions on Apache Spark.

This article was published as a part of the Data Science Blogathon.

Apache Spark Interview Questions

Spark is one of the interviewer’s favorite topics in big data interviews, so in this blog, we’ll go over the most important and frequently asked interview questions about Apache Spark. Let’s begin…

1. What is the spark? 2. What is RDD in Apache Spark?

RDDs stand for Resilient Distributed Dataset. It is the most important building block of any spark application. It is immutable. RDD properties are:-

Resilient:- It has fault tolerance properties and can quickly recover lost data.

Distributed:- Data are distributed across multiple nodes for faster processing.

Dataset:- Collection of data points on which we perform operations.

RDD provides fault tolerance through a lineage graph. A lineage graph keeps track of transformations to be executed after an action has been called. The Lineage graph helps to recompute any missing or damaged RDD because of node failures. RDDs are used for low-level transformations and actions.

3. What is the Difference between SparkContext Vs. SparkSession?

In Spark 1.x version, we must create different contexts for each API. For example:-

SparkContext

SQLContext

HiveContext 

4. What is the broadcast variable?

Broadcast variables in Spark are a mechanism for sharing the data across the executors to be read-only. Without broadcast variables, we have to ship the data to each executor whenever they perform any type of transformation and action, which can cause network overhead. While in the case of broadcast variables, they are shipped once to all the executors and are cached over there for future reference.

Broadcast Variables Use case

Suppose we are doing transformations and need to look up a larger table of zipping codes/pin codes. It’s not feasible to send the data to each executor when needed, and we can’t query it each time to the database. So, in this case, we can convert this lookup table into the broadcast variables, and Spark will cache it in every executor.

5. Explain Pair RDD?

The Spark Paired RDDs are a collection of key-value pairs. There is two data item in a key-value pair (KVP). The key is the identifier, while the value is the data corresponding to the key value. A few special operations are available on RDDs of key-value pairs, such as distributed “shuffle” operations, grouping, or aggregating the elements by a key.

val spark = SparkSession.builder()

.appName("PairedRDDCreation")

.master("local")

.getOrCreate()

Output:-

(Germany,1) (India,1) (USA,1) (USA,1) (India,1) (Russia,1) (India,1) (Brazil,1) (Canada,1) (China,1) 6. What is the difference between RDD persist() and cache() methods?

The persistence and caching mechanisms are the optimization techniques. It may be used for Interactive as well as Iterative computation. Iterative means to reuse the results over multiple computations. Interactive means allowing a two-way flow of information. These mechanisms help us to save the results so that upcoming stages can use them. We can save the RDDs either in Memory(most preferred) or on Disk((less Preferred because of its slow access speed).

Persist():- We know that RDDs are re-computable on each action due to their default behavior. To avoid the re-computation, we can persist with the RDDs. Now, whenever we call an action on RDD, no-re-computation takes place.

In persist() method, computations results get stored in its partitions. The persistent method will store the data in JVM when working with Java and Scala. While in python, when we call persist method, the serialization of the data takes place. We can store the data either in memory or on the disk. A combination of both is also possible.

Storage levels of Persisted RDDs:-

1. MEMORY_ONLY(DEFAULT LEVEL)

2. MEMORY_AND_DISK

3. MEMORY_ONLY_SER

4. MEMORY_ONLY_DISK_SER

5. DISC_ONLY

Cache():- It is the same as the persist method; the only difference is cache stores the computations result in the default storage level i.e. Memory. Persist will work the same as a cache when the storage level is set to MEMORY_ONLY.

Syntax to un-persist the RDDs:-

RDD.unpersist( ) 7. What is Spark Core?

Spark Core is the foundational unit of all spark applications. It performs the following functionalities: memory management, fault recovery, scheduling, distributing & monitoring jobs, and interaction with storage systems. Spark Core can be accessed through an application programming interface (APIs) built in Java, Scala, Python, and R. It contains APIs that help to define and manipulate the RDDs. This APIs help to hide the complexity of distributed processing behind simple, high-level operators. It provides basic connectivity with different data sources, like AWS S3, HDFS, HBase, etc.

8. What is RDD Lineage?

RDD Lineage (RDD operator graph or RDD dependency graph) is a graph that contains all the parent RDDs of an RDD.

The following transformations can generate the above graph:-

val r00 = sc.parallelize(0 to 9) val r01 = sc.parallelize(0 to 90 by 10) val r10 = r00.cartesian(r01) val r12 = r00.zip(r01) val r13 = r01.keyBy(_ / 20) val r20 = Seq(r11, r12, r13).foldLeft(r10)(_ union _)

1. The RDD lineage gets created when we apply different types of transformations to an RDD, creating a so-called Logical Execution Plan.

2. The lineage graph contains information on all the transformations that need to be applied when action gets called.

3. A logical execution plan starts with the earliest RDD and finishes with the RDD, producing the final result on which action has been called.

9. What is the difference between RDD and DataFrame?

DataFrame:-

A data frame stores the data in tabular format. It is a distributed collection of data in rows and columns. The columns can store data types like numeric, logical, factor, or character. It makes the processing of larger datasets easier. Developers can impose a structure onto a distributed collection of data with the help of a data frame. It also provides a high-level abstraction over the distributed data.

RDD:-

RDD(Resilient Distributed Dataset) is a collection of elements distributed across multiple cluster nodes. RDDs are immutable and fault tolerant. RDDs, once created, can’t get changed, but we can perform several transformations to generate new RDDs from them.

10. Explain Accumulator shared variables in Spark?

Accumulators are read-only shared variables. They are only “added” through an associative and commutative operation. They can be used to implement counters (as in MapReduce) or sums. Spark natively supports accumulators of numeric types, and you can also add support for new types.

Accumulators are incremental variables. The tasks running on nodes can add to it while the driver program can read the value. Tasks running on different machines can increment their value, and this aggregated information is available back to the driver.

Frequently Asked Questions

Key takeaways from this article are:-

1. We learn the difference between the most used terms in Apache Spark, i.e., RDD, DAG, DataFrame, Dataset, etc.

2. We understood Structured APIs and how they are used to perform different operations on data.

3. We also learn about pair RDD, lineage, broadcast variables, and accumulators.

4. Other learnings are sparkcontext v/s sparksession, RDD v/s DataFrame, and Spark core.

Keep Learning!!!

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Apache Nifi Vs Apache Spark

Difference Between Apache Nifi vs Apache Spark

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Head-to-Head Comparison Between Apache Nifi vs Apache Spark (Infographics)

Key Differences Between Apache Nifi vs Apache Spark

The differences between Apache Nifi and Apache Spark are explained in the points presented below:

Apache Nifi is a data ingestion tool that delivers an easy-to-use, powerful, and reliable system to simplify processing and distributing data over resources. In contrast, Apache Spark is an extremely fast cluster computing technology designed for quicker computation by efficiently using interactive queries in memory management and stream processing capabilities.

Apache Nifi works in standalone and cluster modes, whereas Apache Spark works well in local or standalone modes, Mesos, Yarn, and other big data cluster modes.

Features of Apache Nifi include guaranteed delivery of data, efficient data buffering, Prioritized queuing, Flow Specific QoS, Data Provenance, Roll buffer recovery, Visual command and control, Flow templates, Security, and Parallel Streaming capabilities. In contrast, features of apache spark include Lightning fast speed processing capability, Multilingual, In-memory computing, efficient utilization of commodity hardware systems, Advanced Analytics, and Efficient integration capability.

Apache Nifi provides visualization capabilities and drag-and-drop features for better readability and overall system understanding. Conventional techniques and processes can easily manage and govern the data flow. In contrast, in the case of Apache Spark’s case, a cluster management system like Ambari is needed to view these kinds of visualizations. Apache Spark in itself does not provide visualization capabilities and is only good as far as programming is concerned. It is a very convenient and stable system for processing huge amounts of data.

Apache Nifi vs Apache Spark Comparision Table

Basis of Comparison Apache Nifi Apache Spark

What is provided It provides a graphical user interface like a format for system configuration and monitoring data flows. A large-scale data processing framework is provided with approximately zero latency at the cost of cheap commodity hardware.

Features

Web-based user interface

Highly configurable

Data Provenance

Designed for extension

Secure

Not for windowed computations

No data replications

Extremely high speed

Multilingual

Advanced analytics

Real-Time Stream Processing

Flexible integration capability

Windowed computations

A data replication factor of 3 by default

Architectural Components

Web Server

Flow Controller

Extensions

Flow File Repository

Content repository

Provenance Repository

Spark Core

Spark Streaming

Spark SQL

Spark R

Spark GraphX

Spark MLlib

Use cases

Data Flow management, along with visual control

Arbitrary data size

Data routing between disparate systems

Streaming Data

Machine Learning

Interactive Analysis

Fog computing

Deployment Issues Config and compatibility issues are seen if the most recent version of Java was not used. A well-defined cluster arrangement is required to have a managed environment as an incorrect configuration.

Scalability and Stability issues Generally, no problems are reported related to scalability and Stability Achieving Stability is difficult as a spark is always dependent upon the streamflow.

Benefits provided It allows a great visualization of data flows to organizations, thereby increasing the understandability of the entire system process. A very convenient and stable framework when it comes to big data. The efficiency is automatically increased when the tasks related to batch and stream processing are executed.

Earlier solutions used Apache Flume could be well used as far as data ingestion is concerned. The only drawback with Flume is the lack of graphical visualizations and end-to-end system processing. Other solutions considered previously were Pig, Hive, and Storm. Apache Spark provides the flexibility of utilizing all the features in one tool.

Limitations Majorly the limitation is related to the provenance indexing rate, which becomes the bottleneck when it comes to the overall processing of massive amounts of data. Limitation for Spark comes in terms of Stability in terms of API, as transitioning from RDDs to Data Frames to Data Sets often becomes a complicated task.

Conclusion

To conclude the post, it can be said that Apache Spark is a heavy warhorse, whereas Apache Nifi is a nimble racehorse. Both have their benefits and limitations for use in their respective areas. It would be best if you decided the right tool for your business. Stay tuned to our blog for more articles about newer big data technologies.

Recommended Articles

This has been a guide to Apache Nifi vs Apache Spark. Here we have discussed Apache Nifi vs Apache Spark head-to-head comparison, key differences, and a comparison table. You may also look at the following articles to learn more –

Ai In Content Marketing: 3 Frequently Asked Questions

Artificial intelligence (AI) is one of the current “it” terms in marketing.

Every marketer is thinking about how to best use it and every technology provider is trying to push offerings that infuse it. And all for good reason.

While AI might be the hot ticket item everyone is clamoring to understand and use, it is neither a fad nor going away anytime soon.

What’s more, according to IDC, by 2023, 40 percent of all digital transformation initiatives – and 100 percent of IoT efforts – will be supported by AI capabilities.

Incorporating AI into the content marketing process is essential. Why?

Because AI has the power to transform customer experiences in multiple ways.

AI can:

Improve the content and creative process.

Help personalize experiences at scale.

Improve customer satisfaction through greater efficiencies.

And more.

Sephora is a great example of a company leveraging AI in innovative ways. Sephora offers customers a plethora of options, which is both great and overwhelming at times.

To help, Sephora created a chatbot that provides customers with beauty content they can peruse as well as product suggestions. It’s an innovative and simple use of AI.

There’s no question as to if marketers should start experimenting with AI; it’s a matter of how soon you can start.

To help you feel more comfortable getting started with AI, here are answers to a few of the top questions I get asked most frequently about AI in content marketing.

Question 1: Will AI Replace Me?

It’s no surprise that marketers are worried about AI taking over their jobs, especially with the increase in science fiction movies about robots taking over the world.

My take is that AI will act in service of marketers, not replace them.

Specifically, AI can help marketers:

Discover what’s hidden: There are vast amounts of data available that marketers can leverage to perfect customer experiences. AI helps make sense of this information at a deep level, from understanding the sentiment of documents or the quality of images. Harnessing the power of AI in content marketing can help identify and refine what’s needed in seconds instead of days.

Accelerate what’s slow: There are many tasks marketers do every day that are tedious and time-consuming. AI uses self-learning algorithms to speed these tasks up – driving greater efficiency and effectiveness. Asset tagging is a great example. This task sucks up a lot of time, but AI has the power to insert content-based metadata for thousands of images in seconds – a process that otherwise would have required hours.

Decide when something matters: AI can help marketers make quick decisions to ensure the best experiences are always delivered to customers. A prime example is content personalization. Marketers know that consumers want content and experiences tailored directly to their needs and interests, but it’s daunting. AI can help marketers do this at scale by automatically personalizing content at the individual level and determining which experience a customer should receive and when.

It’s important to remember that amazing customer experiences still require the human element that machines lack.

Just look at Pandora. It uses a combination of machine learning and human curators to bubble up the best recommendations possible for users.

Pandora’s head of research, Oscar Celma, said:

“The human curators can discover an artist, but the machine learning can prevent a ‘fake’ artist from being ingested into the music libraries. It can also find duplicates. Mostly, the process of human versus bot is about scale. Without machine learning, curators would not be able to make it all work.”

Moral of the story: AI and humans work better together.

Question 2: How Do I Need to Shift My Marketing Strategy to Incorporate AI?

Usually, after I establish that AI is meant to aid what marketers are doing rather than take over their roles, the next question I get is, “Well, how does this impact my marketing strategy? How should I shift what I’m doing now to see the most benefit?”

The truth is, there’s no set response to this question because it really depends on what your business priorities are.

In order to get the most benefit of AI-driven marketing, marketers need to fully understand what the business priorities are and then modify their roles and responsibilities to complement the work being done by AI.

There’s also potential that AI could uncover new opportunities for marketers to pursue.

In fact, according to Narrative Science, 61 percent of businesses that have an innovation strategy said they are using AI to identify opportunities in data that would otherwise be missed.

Question 3: How Will AI Actually Impact the End Result for the Consumer? Will They Be Able to Tell a Difference?

Customers won’t see an experience and automatically know AI assisted in creating it.

The difference they see will be evident in the rapidly delivered, personalized experiences they receive.

In a recent survey my team conducted at Adobe, 42 percent of consumers said they get annoyed when content isn’t relevant to them and two-thirds said if they encountered a situation like this, they wouldn’t make a purchase.

It’s clear that content speed and personalization can impact a customer’s experience, and ultimately a company’s bottom line.

AI also provides customers with a more seamless experience throughout their interactions with a brand.

It may begin with online content, but AI also helps ensure the personalization is transitioned to in-store experiences, experiences across different devices and more.

AI is critical to creating experiences that consumer love, even if they can’t directly see it.

What’s Ahead

AI is here to stay. The opportunities to leverage AI in content marketing are becoming more prevalent.

Right now, marketers are focused on using AI to improve efficiency and automating tasks, but it can be used for so much more. Think chatbots, copywriting, and curating only the best assets that drive engagement.

The possibilities are endless, but you need to get started sooner rather than later.

More AI & Marketing Resources:

Top 10 Aws Redshift Interview Questions In 2023

This article was published as a part of the Data Science Blogathon.

Introduction

AWS Redshift is a powerful, petabyte-scale, highly managed cloud-based data warehousing solution. It processes and handles structured and unstructured data in exabytes (1018 bytes). The most common use cases of Redshift include large-scale data migration, log analysis, processing real-time analytics, joining multiple data sources, and many more.

This blog will discuss the frequently asked interview questions that might help you gain knowledge about Redshift and prepare you for the next interview.

RedShift Interview Questions

Q1: What is Redshift in AWS?

Amazon Web Service(AWS) Redshift is a fully managed big data warehouse service in the cloud that is rapid and potent enough to process and manage data in the range of exabytes. Redshift is built to handle large-scale data sets and database migrations by the company ParAccel (later acquired by Actian). It uses massive parallel processing (MPP) technology and provides a cost-effective and efficient data solution. The famous usage of Redshift is acquiring the latest insight for business and customers.

Q2: What are the benefits of using AWS Redshift?

The major benefits provided by AWS Redshift include:

In-built security with end-to-end encryption.

Multiple query support that provides significant query speed upgrades.

It provides an easy-to-use platform that is similar to MySQL and provides the usage of PostgreSQL, ODBC, and JDBC.

It offers Automated backup and fast scaling with fewer complications.

It is a cost-effective warehousing technique.

Q3: Why use an AWS Data Pipeline to load CSV into Redshift? And How?

AWS Data Pipeline facilitates the extraction and loading of CSV(Comma Separated Values) files. Using AWS Data Pipelines for CSV loading eliminates the stress of putting together a complex ETL system. It offers template activities to perform DML(data manipulation) tasks efficiently.

To load the CSV file, we must copy the CSV data from the host source and paste that into Redshift via RedshiftCopyActivity.

Q4: How to list tables in Amazon Redshift?

The ‘Show table’ keyword lists the tables in Amazon Redshift. It displays the table schema along with table and column constraints. Syntax:

SHOW TABLE [schema.]table_name

Q5: How are Amazon RDS, DynamoDB, and Redshift different?

Below are the major differences:

Database Engine

The available Amazon RDS engines include Oracle, MySQL, SQL Server, PostgreSQL, etc., while the DynamoDB engine is NoSQL, and Amazon Redshift supports the Redshift(adapted PostgreSQL) as a database engine.

Data Storage

RDS facilitates 6 terabytes per instance, Redshift supports 16 terabytes per instance, and DynamoDB provides unlimited storage.

Major Usage

RDS is used for traditional databases, while Redshift is famous for data warehousing DynamoDB is the database for dynamically modified data.

Multi-Availability Zone Replication

RDS acts as an additional service while Multi-AZ replication for Redshift is Manual and for DynamoDB is Built-in.

Q6: How far Redshift is better in performance as compared to other data warehouse technologies?

Amazon Redshift is the easiest and fastest cloud data warehouse which facilitates 3 times better price-performance than other data warehouses. Redshift offers fast query performance at a comparatively modest cost to firms where datasets ranging in size from gigabytes to exabytes.

Q7: How do we load data into Redshift?

Several methods are available to load data into Redshift, but the commonly used 3 methods are:

The Copy command is used to load data into AWS Redshift.

Use AWS services to load data into Redshift.

Use the Insert command to load data into Redshift.

Q8: What is Redshift Spectrum? What data formats does Redshift Spectrum support?

Redshift Spectrum is released by AWS(Amazon Web Services) as a companion to Amazon Redshift. It uses Amazon Simple Storage Service (Amazon S3) to run SQL queries against the data available in a data lake. Redshift Spectrum facilitates the query processing against gigabytes to exabytes of unstructured data in Amazon S3, and no ETL or loading is required in this process. Redshift Spectrum is used to produce and optimize a query plan. Redshift Spectrum supports various structured and semi-structured data formats, including AVRO, TEXTFILE, RCFILE, PARQUET, SEQUENCE FILE, RegexSerDe, JSON, Geok, Ion, and ORC. Amazon suggests using columnar data formats like Apache PARQUET to improve performance and reduce cost.

Q9: How will the price of Amazon Redshift vary?

The Amazon Redshift pricing depends upon the type of node chosen by the customer to build his cluster. It mainly offers two types of nodes that differ in terms of storage and computation:

Dense Compute Nodes

These optimized computing nodes offer RAM up to 244GB and SSDs up to 2.5 terabytes. The lowest spec price for dc2.larges varies from 0.25$ to 0.37$ per hour, and the highest spec price for dc2.8x varies from 4.8$ to 7$ per hour.

Dense Storage Nodes

These nodes provide high storage capacity in two versions- a basic version(ds2.xlarge) with up to 2 TB HDDs and a higher version(ds2.8xlarge) with up to 16 TB HDDs. The cost of the basic version varies from 0.85$ to1.4$ per hour, and for the higher version is 6$ to 11$.

Q10: What are the limitations of Amazon Redshift?

It cannot be used as a live app database due to the slow processing speed of web apps.

There is no way to enforce uniqueness in AWS Redshift on inserted data.

It supports the parallel loading only for Amazon EMR, relational DynamoDB, and Amazon S3.

Conclusion

In this blog, we have seen some of the important interview questions that can be asked in AWS Redshift interviews. We had discussed a basic combination of theoretical and practical questions, but that’s not it. This blog will give you a basic understanding of what type of questions you expect. However, it’s recommended apart from these Redshift interview questions; you also practice the SQL commands to develop more understanding of data processing and transformations. The key takeaways from the above AWS Redshift questions are:

We learned about what is Redshift in AWS and how it is beneficial for the user.

We have seen how can we load CSV in Redshift using the data pipeline.

We understand how Redshift differs from RDS and DynamoDB.

We got an understanding of how we can show tables.

We have also discussed the basics of Redshift Spectrum and the limitations of Redshift.

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Cracking The Gcp Interview: Tips And Common Questions

Introduction

Suppose you are appearing in an interview for the GCP beginner role. In that case, it’s important to have a basic understanding of the Google Cloud Platform. Also, it must have the quality to communicate with the team in deployment and communicate effectively with a technical and non-technical persons. So, in this article, you will learn interview questions related to GCP.

You can start by giving the introduction of GCP, like, “Google Cloud Platform is a cloud computing platform that offers a range of services, including computing, storage, networking, data analytics, machine learning, and more.”

Source: Analytics Vidhya

Learning Objectives:

Learn tips to prepare for the interview.

Gain a basic understanding of what computing is.

Learn about the different services offered by GCP benefits of Google Kubernetes Engine (GKE) and Familiarize yourself with Google BigQuery.

Learn about the billing structure for GCP, Cloud Pub/Sub, and the benefits of Cloud SQL.

Understand best practices for securing a GCP project benefits of load balancers in GCP.

Learn about Cloud IAM and snapshots in GCP, and familiarise yourself with Google Cloud CDN.

Note that these questions are just a few examples of the types of questions you might encounter during a GCP interview, and answers may vary from person to person as per their expertise.

Table of Contents Tips to Prepare for Technical Interview

To clear any company interview, you need to follow the right path; here are a few tips to prepare for the technical interview to increase your chances of success.

Research about the company

Review the job description

Brush up on technical skills and projects

Practice coding problems

Be familiar with your resume

Practice communicating technical concepts

Prepare Frequently asked questions

GCP: Beginner-Level Interview Questions Q1. What are the different services offered by GCP?

Here is the list of services which is offered by GCP:

Storage

Databases

Networking

Compute

Operations

Developer Tools

Data Analytics

AI and Machine Learning

Vertex AI, AI Platform, and Accelerators.

Q2. What is Google Kubernetes Engine (GKE)? Q3. What is Google BigQuery?

BigQuery can do all kinds of things, like help us to analyze large amounts of data and predict trends in our data, visualize it in cool ways, and even handle streaming data in real time. So basically, it’s a super handy way to manage all your big data needs!

Source: GCP Docs

Q4. What is Cloud SQL?

Google’s cloud-based solution for managing and storing data. This platform provides us with a secure, reliable database solution to store our data in an efficient way.

Q5. What is Cloud Pub/Sub?

Cloud Pub/Sub works on the principle of publish-subscribe, where publishers can send messages to a topic, and subscribers can receive those messages from the topic. This messaging service can handle a high volume of messages per second and provides various features such as message retention, ordering, and push or pull delivery modes.

GCP: Intermediate-Level Interview Questions Q1. What is the billing structure for GCP? Q2. How do you secure a Google Cloud Platform project?

Securing a Google Cloud Platform project involves implementing a range of security best practices to protect your data and resources from unauthorised access, malicious attacks, and other security threats, like:

Set up strong authentications, strong passwords, and access controls

Encrypt your data implement firewalls and network security

Regularly monitor and audit your project

Implement security best practices

Q3. Describe Cloud IAM.

With Cloud IAM, administrators can grant or revoke access to individual users or groups of users. They can assign specific roles to those users, allowing them to perform specific actions on resources. For example, an administrator could grant a developer the ability to create and manage virtual machines while denying them access to the billing information for the project.

Source: GCP Docs

Q4. What is a load balancer in GCP?

A load balancer is a service that distributes incoming network traffic across multiple backend instances or services to ensure that the workload is evenly distributed and that the system remains highly available and responsive to user requests.

Following load balancers available in GCP,

HTTP(S) Load Balancer

Network Load Balancer

Internal Load Balancer

Q5. What is a snapshot in GCP?

A snapshot is a point-in-time copy of a persistent disk. Snapshots are used for data backup, disaster recovery, and migrating data between regions.

GCP: Advance-Level Interview Questions Q1. What is Google Cloud CDN?

Google Cloud CDN (Content Delivery Network) is a network of servers situated globally that provides the content to users according to their geographical location. It helps in delivering web content, video, and other data quickly and reliably, enhancing the user experience.

Source: GCP Docs

Q2. How can you use the GCP BigQuery API to run a SQL query and fetch the results?

To use the GCP BigQuery API to run a SQL query and fetch the results, you can follow these steps:

from google.cloud import bigquery # Set up the BigQuery client object # Build the query configuration query = ''' SELECT * FROM `bigquery-public-data.samples.natality` LIMIT 100 ''' query_config = bigquery.QueryJobConfig() query_config.query = query # Create and submit the query job query_job = client.query(query, job_config=query_config) # Wait for the job to complete query_job.result() # Retrieve the results and process them results = query_job.result() for row in results: print(row)

First, we need to create a client object using bigquery.Client() constructor. At that point we’ll characterize the SQL query which we need to execute. At that point, we’ll yield the query to the BigQuery benefit utilizing the client object’s query() method. This returns a query_job question that speaks to the job that was made to run the query. Able to at that point call the result() method on the query_job protest to get the comes about of the query.

Q3. Explain the use of VPC Peering in GCP.

VPC peering is a great networking tool that lets you link two virtual private clouds (VPCs) so they can chat with each other using private IP addresses. This can be super useful if you’ve got stuff spread out across different regions or want to keep certain resources isolated but still accessible.

By setting up a VPC peering connection between them, you can create a secure and private connection that doesn’t require any internet access. This way, your VPCs can share resources like databases, storage, or application services without exposing them to the public internet. Pretty nifty, huh?

Source: GCP Docs

Some use cases and benefits of VPC Peering in GCP:

Shared Resources

Cost Savings

Improved Security

High Availability

MCQs

Get GCP-ready with our MCQs! Test your knowledge and see how much you know about Google Cloud Platform.

1. Which of the following options lists the main features of cloud services?

(A). On-premises deployment, high upfront costs, limited scalability.

(B). Shared resources, pay-per-use billing, scalability.

(C). Dedicated infrastructure, resource pooling, limited availability.

(A). Higher upfront costs, dedicated infrastructure, limited accessibility.

(B). Reduced capital expenditure, on-demand scalability, high availability.

(C). Limited flexibility, longer deployment times, reduced security.

3. Which of the following options lists the platforms commonly used for large-scale cloud computing?

(A). Microsoft Office, Adobe Creative Suite, Oracle Database.

(B). Amazon Web Services, Google Cloud Platform, Microsoft Azure.

(C). Dropbox, iCloud, Box. WordPress, Shopify, Wix.

4. Which of the following options lists the different deployment models in cloud computing?

(A). Public cloud, private cloud, hybrid cloud.

(B). Open-source cloud, closed-source cloud, hybrid cloud.

(C). Single-tenant cloud, multi-tenant cloud, community cloud.

5. Which of the following options best describes how a user can benefit from utility computing?

(A). By having complete control over the infrastructure and underlying hardware.

(B). By paying a fixed monthly fee for a set amount of resources, regardless of usage.

(C). Paying only for the number of computing resources used, resulting in cost savings.

6. Which of the following options describes how to ensure the security of data during transfer?

(A). By storing the data on a physical hard drive and shipping it to the recipient.

(B). By transmitting the data over a public network without any encryption.

(C). By transmitting the data over a private network with end-to-end encryption.

7. What is the purpose of Google Cloud Functions?

(A). To provide a fully managed, serverless compute service that can be used for event-driven, on-demand functions.

(B). To provide a managed platform for building and deploying containerized applications.

(C). To provide a managed, scalable NoSQL document database

Answer: A) To provide a fully managed, serverless compute service that can be used for event-driven, on-demand functions.

Important Questions

Here are a few more important questions that can be encountered during the interview.

Q1. Have you ever worked with GCP’s Cloud Functions? If so, can you give me an example of how you used them in a project and how they helped streamline your application’s architecture?

Q2. How does cloud computing provide on-demand functionality?

Q3. What are the various layers in the cloud architecture?

Q4. What are the libraries and tools for cloud storage on GCP?

Q5. What are some of the popular open-source cloud computing platforms?

Q6. Explain what the different modes of software are as a service (SaaS)?

Q7. How do you handle infrastructure as code in GCP?

Q8. What is the benefit of API in the cloud domain?

Q9. What is your experience with GCP networking, including VPNs, subnets, firewall rules, and load balancing?

Q10. What is the Function of a Bucket in Google Cloud Storage?

Q11. What is eucalyptus?

Q12. Explain “Google Cloud Machine Images”?

Q13. Can you walk me through a project you worked on that involved using GCP’s Pub/Sub service for real-time data streaming? What was your role in the project, and what challenges did you face?

Q14. Describe the security aspects that the cloud offers.

Conclusion

I trust that you comprehended today’s reading material. If you were able to answer all the questions, I applaud you! You are making excellent progress in your preparation. However, if you weren’t able to answer them all, there’s no cause for alarm. The true benefit of today’s blog will arise when you can assimilate these ideas and use them to tackle the interview questions that lie ahead.

To summarize for you, the key takeaways from this article would be:

Cloud IAM is used to grant or revoke access to users or groups and assign specific roles to those users.

GCP charges customers based on the resources they consume, and new users can receive $300 in free credits.

Securing a GCP project involves implementing security best practices, such as setting up strong authentication, encrypting data, implementing firewalls, and monitoring the project regularly.

If you go through these thoroughly and understand the concepts in this blog, you’ll have a solid foundation in GCP. You can feel confident in your ability to answer related questions in the future. I’m glad this blog was helpful, and I hope it added value to your knowledge. Best of luck with your interview preparation and future endeavors!

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Top 10 Iot Interview Questions And Answers For 2023

Introduction

Top IoT Interview Questions and answers frequently asked in interviews have been covered in this post. This served as a reference for the list of top IoT interview questions and answers so applicants could quickly understand them.

Top 10 IoT Interview Questions and Answers

Following are the top 10 IoT (Internet of Things) interview questions and answers you should know:

1. What is the Internet of Things (IoT)?

The Internet of Things includes sensors, electronics, networks, and software that enable data collection. Only through IoT can mobile phones, laptops, and other gadgets connect to the internet.

2. What are Different Layers of IoT Protocol Stack?

The levels of IoT protocol are as follows:

1. Network connection

2. Sensing and information

3. Application

4. Data processing

3. What is Arduino?

In a nutshell, Arduino is a free electronics development platform, and both the hardware and software are simple to use. It has a microcontroller that can receive information from sensors and control motors.

4. Mostly used sensors in IoT?

Here we have listed some of the most used sensors in IoT.

Temperature sensor

Gas sensor

IR sensor

Smoke sensor

Proximity sensor

Pressure sensor

5. What is Raspberry Pi?

Raspberry Pi can do all kinds of computer-like tasks around us. Also, it has Bluetooth and onboard WiFi to communicate with many things.

6. What are the components of IoT?

There are mainly four types of components in IoT.

Device – The gadget is the primary tool for gathering all the info surrounding you. You can find every detail in the data. It may be a temperature sensor or another type.

Data pressing – Once the data is collected, it goes to the cloud, usually on devices like heaters. However, it is sometimes very complex, such as object recognition or video vision tasks.

Connection – All received data is sent to Katamo in the cloud. All sensors should be connected to this cloud and can be connected through mobile, Wi-Fi or Bluetooth, etc.

7. Benefits from IoT?

Here are some of the benefits of IoT –

Technology Optimization – Helping improve technologies and thereby improve them. For example, data from many vehicle sensors can be collected very easily manufacturers can further enhance its design or efficiency.

Improved customer engagement – improving the customer experience by finding and solving problems and improving things.

8. Common IoT applications?

Below are listed common IoT applications:

Smart Thermostat – Saves heating bills by knowing your usage patterns.

Connected car – Automobile companies can get help in billing, packing, insurance, and other related things through it.

Activity Tracker – Helps measure your body’s heart rate, activity level, calories burned, and skin temperature of a healthy person.

Parking sensor – accurate real-time detection of a parking space by just one iot user.

Connect Health – Real-time patient care is provided through the idea of a healthcare system. Currently, a patient cannot heal without cutting-edge care, assisting the patient in selecting the best course of action.

Privacy – Privacy is a big issue in IoT. IoT exposes any personal information without the user’s presence, which is very problematic.

Security – There is an ecosystem in IoT devices. But even though it has a lot of cyber security, it still provides authentication.

Complexity – To be honest, IoT is very difficult to deploy and maintain because IoT is very complex by design.

Flexibility – It usually integrates with other systems to make things more versatile. So there is an initiative with iot flexibility

Compliance – This can be a very challenging issue. But anyway, it has its neon control.

10. Types of testing in IoT

Below are listed types of testing in IoT:

Usability – Users have many devices of different sizes. Its users have changed from others, and that’s why system usability testing is very useful in IoT.

Compatibility Testing – IoT systems enable connecting devices containing hardware and software. As a result, compatibility testing is crucial for IoT systems.

Security Testing – it is accessing large amounts of critical data. So the most important thing is to verify the user through authentication; data privacy is possible as part of the security test.

Data Integrity Testing – Data integrity testing is very useful in IoT testing because it requires a lot of data.

Reliability and Scalability Testing – Reliability and scalability are essential to building an IoT test environment. It is an important virtualization tool.

Performance Testing – Performance testing is important because the strategic approach to the IoT testing plan is to be realized.

Conclusion

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