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15 Machine Learning Interview Questions (with Answers) for Data Scientists

Data science is a progressive field that deals with handling large chunks of data that normal software fails to do. Although machine learning is a vast field in itself, machine learning interview questions are a common occurrence in a job interview of a data scientist. Some very basic data scientist interview questions deal with various aspects of it, including Statistics and programming. Over here, we will focus on the machine learning part of data science. Machine Learning Interview Questions 1. Differentiate between supervised learning and unsupervised learning These are some notable differences between the two. Supervised Learning Unsupervised Learning Trained on labeled dataset Trained on unlabeled dataset Algorithms used: regression and classification Algorithms used: clustering, association and density estimation Suited for predictions Suited for analysis Maps input to the known output labels Finds hidden patterns and discovers the output   2. Define logistic regression with example Also known as the Logit model, it’s used for predicting a binary outcome from predictor variables having a linear combination. For instance, predicting a politician's victory or defeat in an election is binary. The predictor variables would be time spent in the camp and total money used for the camp. 3. How do classification machine learning techniques and regression differ? These are the key differences Classification Regression Target variables can have discrete values Target variables can have continuous values, usually real numbers Evaluated by measuring accuracy Evaluated by measuring root mean square error   4. What is meant by collaborative filtering? The kind of filtering done by recommender systems for fetching information or patterns, by integrating data sources, agents, and viewpoints is called collaborative filtering. For example, predicting a user’s rating based on his recommendations/ratings for other movies. This technique is very commonly used in referring to sites like Bookmyshow, IMDb, Amazon, Snapdeal, Flipkart, Netflix, YouTube, etc. 5. What are the numerous steps in an analytics project? These are the steps taken in an analytics project:- 1. Comprehending the business problems. 2. Transforming the variables, outlier detection for Data preparation for modeling, checking missing values. 3. Analyzing the outcome, using tweaked approaches after running the model, this is done for achieving a good outcome. 4. Validation of the model via a few data sets. Further, implementing the model and analyzing its performance over a specific duration. 6. Explain in brief a few types of ensemble learning There are several types of ensemble learning, below are some of the more common types. Boosting An iterative technique that helps in weight adjustment of a particular observation based on previous classification. In case, the classification is incorrect, then observation weight is increased. This helps in building reliable predictive models, as it reduces the bias error, but there’s also a possibility of overfitting into the training data. Bagging It attempts to implement learners on a particular sample bunch, further taking a mean of the productions. One can implement other learners on varying bunches in generalized bagging, this prevents some of the variance errors. 7. Describe box-cox transformation In a regression analysis, the dependent variable might not be able to satisfy ordinary least square regression assumptions. The residuals could be following the distribution (skewed) or curve, in case the prediction increases. In such scenarios, the transformation of response variable becomes a necessity in order for data to meet specific assumptions. The box-cox transformation relates to Statistical techniques for transforming dependent, non-normal variables to a conventional shape. When the available data is unconventional, then many statistical techniques assume normality. Numerous tests can be run when box-cox transformation is applied, it’s a method for transforming unconventional, dependent variables into a more conventional shape. This transformation gets its name from its developers, who were Statisticians. Sir David Roxbee Cox and George Box collaborated on a paper in 1964 developing this technique. 8. What’s Random Forest, and how does it function? Also, explain it's working. It’s a versatile method for machine learning that can do classification and regression both. It gets used in outlier values, dimensionality reduction, treating missing values. It’s a kind of ensemble learning method, wherein clusters of weak models integrate to build a powerful model. Numerous decision trees are created instead of a single tree in a random forest. For classifying new attribute-based objects, every tree provides a classification, and the one that has maximum votes (total trees in the forest) gets selected by the forest, as for regression average output of varying trees gets considered. Working of Random Forest This technique's main principle is that various weak learners combine to make a strong learner. The steps include:- 1. Randomly pick k records from the dataset. 2. Build a decision tree on these k records. 3. Repeat the above 2 steps for each decision tree you want to build (repeats for #trees to make) 4. Predictions are based on the majority rule. In regression problem, it predicts a value for output whereas, in classification problem, it predicts the class. 9. If you were to train a model using 10 GB of data set and had only 4 GB RAM, then how would you approach this problem? To start, it’s best to ask about the type of ML model that requires training. For SVM (partial fit will suit best) Follow these steps 1. Start by division of a large data set into smaller size sets. 2. Implement SVM's partial fit method, it will need the full data set's subset. 3. Repeat the second step for different subsets. For neural networks (NumPy array plus batch size will do) Follow these measures 1. In NumPy array, load the full data, NumPy array has a tendency to make mapping of the full data set. It doesn’t load into the memory, the full data set. 2. For attaining required data, pass index into the NumPy array. 3. Make use of this data for passing to neural networks. Maintain a smaller batch size. 10. In an analysis, how do missing values get treated? Once the variables having missing values get identified, the extent of the values that are absent also gets discovered. In case any patterns are picked, it becomes necessary for the analyst to pay attention as these could bring about a couple of significant and valuable business-related insights. And, if no patterns are discovered, then median or mean values can take place of the missing values, or it can be ignored. A default value can be allotted as maximum, minimum, or mean value. In case, the variable is categorical, the default value is assigned to the missing value. If data distribution is incoming, then a mean value is assigned for normal distribution. Also, if a variable’s 80% values seem missing, then it’s reasonable to drop the variable than treat the missing values. 11. How to treat outlier values? For detection of outlier values, some graphical analysis or univariate method can be used. If the outliers are large, then either the 1st percentile value or the 99th percentile can replace the values. Also if the outliers are fewer, then the individual assessment can be done. It should be noted that all outlier values are not necessarily extreme values. For treating outlier values, the values can either be modified and brought within range or they can be discarded. 12. Which cross-validation technique can be used on a time-series dataset? Rather than Implementing the K-Fold technique, one should know that time-series have an inherent chronological order, and is not some randomly distributed data. As far as time-series data is concerned, one can implement the forward-chaining technique, where one has to model previous data, then consider data that is forward-facing. fold 1: training[1], test[2] fold 1: training[1 2], test[3] fold 1: training[1 2 3], test[4] fold 1: training[1 2 3 4], test[5] 13. How often an algorithm requires updating? If these requirements call, then it’s suitable for an algorithm to be updated:- 1. Model evolution is a must as data runs through infrastructure. 2. Data source (underlying) is not constant. 3. A non-stationary case shows up. 4. Results don’t have good precision and accuracy as the algorithm doesn’t perform well. 14. List some drawbacks of linear model These are a few drawbacks of the linear model 1. For binary and count outcomes, it is not usable. 2. Error linearity assumptions occur too often. 3. Over-fitting problems that cannot be solved. 15. Describe SVM algorithm SVM (Support Vector Machine) is an algorithm (supervised machine learning) implemented for classification and regression. If one’s training data set has n features, then SVM does their plotting in a space that is n-dimensional, where every feature's value is a specific coordinate's value. SVM implements hyperplanes for the segregation of distinct classes. Want One-On-One session with Instructor to clear doubts and help you clear Interviews? Contact Us on +91-9999201478 or fill the Enquiry Form Data Science Instructor Profile Check Machine Learning Course Content

Top NLP (Natural Language Processing) Interview Question Answers

An Introduction to Natural language processing is fairly a good start for students who wish to bridge the gap between what’s human-like and what’s mechanical. Natural language processing is widely utilized in artificial intelligence and also implemented in machine learning. Its use is expected to go up in the coming years, along with rising job opportunities. Students preparing for natural language processing (NLP) should have a decent understanding of the type of questions that get asked in the interview.  1. Discuss real-life apps based on Natural Language Processing (NLP). Chatbot: Businesses and companies have realized the importance of chatbots, as they assist in maintaining good communication with customers, any queries that a chatbot fails to resolve gets forwarded. They help keep the business moving as they are used 24/7. This feature makes use of natural language processing. Google Translate: Spoken words or written text can be converted into another language, proper pronunciation is also available of words, Google Translate makes use of advanced NLP which makes all of this possible. 2. What is meant by NLTK? Natural language toolkit is a Python library that processes human languages, different techniques including tokenization, stemming, parsing, lemmatization are used for grasping the languages. Also used for classification of text, and assessing documents. Some libraries include DefaultTagger, wordnet, patterns, treebank, etc. 3. Explain parts of speech tagging (POS tagging). POS is also known as parts of speech tagging is Implemented for assigning tags onto words like verbs, nouns, or adjectives. It allows the software to understand the text, then recognize word differences using algorithms. The purpose is to make the machine comprehend the sentences correctly.  Example:- import nltk from nltk.corpus import stopwords from nltk.tokenize import word_tokenize, sent_tokenize stop_words = set (stopwords.Words('english')) txt = "A, B, C are longtime classmates."   ## Tokenized via sent_tokenize tokenized_text = sent_tokenize (txt)   ## Using word_tokenizer to identify a string’s words and punctuation then removing stop words for n in tokenized_text: wordsList = nltk.word_tokenize(i) wordsList = [w for w in wordsList if not w in stop_words]   ## Using POS tagger tagged_words = nltk.pos_tag(wordsList) print (tagged_words)   Output:- [(‘A’, 'NNP'), ('B', 'NNP’), ('C', 'NNP’), ('longtime', 'JJ’), ('classmates', 'NNS')]   4. Define pragmatic analysis In a given data of human language, different meaning exists, in order to understand more, pragmatic analysis is used for discovering different facets of the data or document. Actual meaning of words or sentences can be understood by the systems, and for this purpose pragmatic analysis is deployed. 5. Elaborate on Natural language processing components These are the major NLP components:- 1. Lexical/morphological analysis: word structure is made comprehensible via analysis through parsing. 2. Syntactic analysis: specific text meaning is assessed 3. Entity extraction: information like the place, institution, individual gets retrieved via sentence dissection. Entities present in a sentence get identified. 4. Pragmatic analysis: helps in finding real meaning and relevancy behind the sentences. 6. List the steps in NLP problem-solving The steps in NLP problem-solving include:- 1. Web scraping or collecting the texts from the dataset. 2. For text cleaning, making use of lemmatization and stemming. 3. Use feature engineering 4. Use word2vec for embedding 5. Using machine learning techniques or with neural networks, start training the models. 6. Assess the performance. 7. Do the required model modifications and deploy. 7. Elaborate stemming with examples When a root word is gained by detaining the prefix or suffix involved, then that process is known as stemming. For instance, the word 'playing' can be minimized to ‘play’ by removing the rest. Different algorithms are deployed for implementation of stemming, for example, PorterStemmer which can be imported from NLTK as follows:- from nltk.stem import PorterStemmer pst = PorterStemmer() pst.stem(“running”), pst.stem(“cookies”), pst.stem(“flying”)   Output:- (‘run’, 'cooki', 'fly' )   8. Define and implement named entity recognition For retrieving information and identifying entities present in the data for instance location, time, figures, things, objects, individuals, etc. NER (named entity recognition) is used in AI, NLP, machine learning, implemented for making the software understand what the text means. Chatbots are a real-life example that makes use of NER. Implementing NER with spacy package:- import spacy nlp = spacy.load('en_core_web_sm') Text = "The head office of Tesla is in California" document = nlp(text)  for ent in document.ents: print(ent.text, ent.start_char, ent.end_char, ent.label_)   Output:- Office 9 15 Place Tesla 19 25 ORG California 32 41 GPE   9. Explain checking word similarity with spacy package Spacy library allows the implementation of word similarity techniques for detecting similar words. The evaluation is done with a number between 0 & 1 (where 0 tends towards less similar and 1 tends toward highly similar). import spacy nlp = spacy.load('en_core_web_md') print ("Enter the words:") input_words = input() tokens = nlp(input_words) for i in tokens: print(i.text, i.has_vector, i.vector_norm, i.is_oov) token_1, token_2 = tokens[0], tokens[1] print("Similarity between words:", token_1.similarity(token_2))   Output:- hot  True 5.6898586  False cold True6.5396233 False Similarity: 0.597265 This implies that the similarity between the two words cold and hot is  59%. 10. Describe recall and precision. Also, explain TF-IDF. Precision and recall Precision, F1 and Recall, accuracy are NLP model testing metrics. The ratio of predictions with required output provides for a model's accuracy. Precision: The ratio of positive instances and total predicted instances. Recall: The ratio between real positive instances and total (real + unreal) positive instances. TF-IDF Term frequency-inverse document frequency is used for retrieval of information via numerical Statistics. It helps in identifying keywords present in any document. The real usage of it revolves around getting information from important documents using Statistical data. It’s also useful in filtering out the stop words and text summarizing plus classification in the documents. With TF one can calculate the ratio of term frequency in a document wrt total terms, whereas IDF implies the significance of the term. TF IDF calculation formula: TF = frequency of term 'W' in a document / total terms in document IDF = log( total documents / total documents with the term ‘W’) If TF*IDF appears higher then term frequency is likely less. Google implements TF-IDF for deciding search results index, which helps in optimization or ranking the relevant quality content higher. Want One-On-One session with Instructor to clear doubts and help you clear Interviews? Contact Us on +91-9999201478 or fill the Enquiry Form Data Science Instructor Profile Check Data Science Course Content

Top SQL Interview Questions with Answers for a Data Analyst Interview

Data analysts perform a variety of roles including providing reports using statistical methods and analyzing data, implementing systems for data collection, and developing databases, identifying trends, and interpreting complex data set patterns. SQL is the industry-standard language used by data analysts for providing data insights. In a job interview, SQL being a major component of data analysis features highly in the interrogation. These are some of SQL Query Interview Questions for Data Analyst that are frequently asked. Data Analyst interview questions and answers for freshers Consider the following tables Employee table employee_id full_name manager_id date_of_joining city 121 Shanaya Gupta 321 1/31/2014 Bangalore 321 Snehil Aggarwal 986 1/30/2015 Delhi   Salary table employee_id project salary variable 121 P1 8000 500 321 P2 10000 1000 421 P1 12000 0   1. Write a query fetching the available projects from salary table. Upon looking at the employee salary table, it is observable that every employee has a project value correlated to it. Duplicate values also exist, so a unique clause will be used in this case to get distinct values. SELECT DISTINCT(project) FROM Salary;   2. Write a query fetching full name and employee ID of workers operating under manager having ID 986 Take a look at the employee details table, here we can fetch employee details working under the manager with ID 986 using a WHERE clause. SELECT employee_id, full_name FROM Employee WHERE manager_id=986;   3. Write a query to track the employee ID who has a salary ranging between 9000 and 15000 In this case, we will use a WHERE clause, with BETWEEN operator SELECT employee_id, salary FROM Salary WHERE salary BETWEEN 9000 and 15000;   4. Write a query for employees that reside in Delhi or work with manager having ID 321 Over here, one of the conditions needs to be satisfied. Either worker operating under Manager with ID 321 or workers residing in Delhi. In this scenario, we will require using OR operator. SELECT employee_id, city, manager_id FROM Employee WHERE manager_id='321' OR city='Delhi';   5. Write a query displaying each employee's net salary added with value of variable Now we will require using the + operator. SELECT employee_id, salary+variable AS Net Salary FROM Salary;   6. Write a query fetching employee IDs available in both tables We will make use of subquery SELECT employee_id FROM Employee WHERE employee_id IN (SELECT employee_id FROM Salary);   7. Write a query fetching the employee’s first name (string before space) from the employee table via full_name column First, we will require fetching space character’s location from full_name field, then further extracting the first name out of it. We will use LOCATE in MySQL, then CHARINDEX in SQL server. MID or SUBSTRING method will be utilized for string before space Via MID (MySQL) SELECT MID(full_name, 1, LOCATE(' ', full_name)) FROM Employee;   Via SUBSTRING (SQL server) SELECT SUBSTRING(full_name, 1, CHARINDEX(' ', full_name)) FROM Employee;   8. Write a query fetching the workers who have their hands-on projects except for P1 In this case, NOT operator can be used for fetching rows that do not satisfy the stated condition. SELECT employee_id FROM Salary WHERE NOT project = 'P1';   Also, using not equal to operator SELECT employee_id FROM Salary WHERE project <> 'P1';   9. Write a query fetching name of employees who have salary equating 5000 or more than that, also equating 10000 or less than that Over here, BETWEEN will be used in WHERE for returning employee ID of workers whose remuneration satisfies the stated condition, further using it as subquery for getting the employee full name via the table (employee). SELECT full_name FROM Employee WHERE employee_id IN (SELECT employee_id FROM Salary  WHERE salary BETWEEN 5000 AND 10000);   10. Write a query fetching details of the employees who started working in 2020 from employee details table. For this, we will use BETWEEN for time period ranging 01/01/2020 to 31/12/2020 SELECT * FROM Employee WHERE date_of_joining BETWEEN '2020/01/01' AND '2020/12/31';   Now the year can be extracted from date_of_joining using YEAR function in MySQL SELECT * FROM Employee WHERE YEAR(date_of_joining) = '2020';   11. Write a query fetching salary data and employee names. Display the details even if an employee's salary record isn’t there. Here, the interviewer is trying to gauge your knowledge related to SQL JOINS. Left JOIN will be used here, with Employee table being on the left side of Salary table. SELECT E.full_name, S.salary FROM Employee E LEFT JOIN Salary S ON E.employee_id = S.employee_id;   Advanced SQL, DBMS interview questions These SQL interview questions for 6 years of experience can help you in your job application. 12. Write a query for removing duplicates in a table without utilizing the temporary table Inner join along with delete will be used here. Equality of matching data will be assessed further, the rows with higher employee ID will be discarded. DELETE E1 FROM Employee E1 INNER JOIN Employee E2 WHERE E1.employee_id > E2.employee_id AND E1.full_name = E2.full_name AND E1.manager_id = E2.manager_id AND E1.date_of_joining = E2.date_of_joining AND E1.city = E2.city;   13. Write a query fetching just the even rows in the Salary table If there’s an auto-increment field, for instance, employee_id, then the below-mentioned query can be used. SELECT * FROM Salary WHERE MOD(employee_id,2) = 0;   If the above-stated field is absent (auto-increment field), then these queries can be used. Verifying the remainder is 0 when divided with 2, and by using ROW_NUMBER (in SQL server) SELECT E.employee_id, E.project, E.salary FROM (       SELECT *, ROW_NUMBER()       OVER(ORDER BY employee_id) AS RowNumber       FROM Salary      ) E WHERE E.RowNumber % 2 = 0;   Using variable (user-defined) in MySQL SELECT * FROM (            SELECT *, @rowNumber := @rowNumber+1 RowNo      FROM Salary      JOIN(SELECT @rowNumber := 0) r      ) t WHERE RowNo % 2 = 0;   14. Write a query fetching duplicate data from Employee table without referring to employee_id (primary key) In this case, on all the fields, we will use GROUP BY, further HAVING clause will be used for returning duplicate data that has more than one count. SELECT full_name, manager_id, date_of_joining, city, COUNT(*) FROM Employee GROUP BY full_name, manager_id, date_of_joining, city HAVING COUNT(*) > 1;   15. Write a query creating the same structured empty table as any other Over here, false WHERE condition will be used. CREATE TABLE NewTable SELECT * FROM Salary WHERE 1=0;   The above mentioned are some of the most common SQL data analyst interview questions to prepare for entry-level, intermediate and advanced level jobs. Check the SQL Training Program

How Power BI is Better than Excel?

Analysis of business data is essential to make it big as far as commerce is concerned. Be it a small enterprise or a multinational company looking to widen its reach. Several businesses are waking up and realizing the significance of data analysis. Two of the most common used tools are Power BI and excel, choosing the right one to work with can be a bit cumbersome.   What is Power BI tool? Power BI is a product from Microsoft, which focuses on processing data associated with a business. To be more specific, it is a toolset that caters to the deeper demographics of a business, and its functional operations. It is often compared to Excel, as both are very similar in what they do. There are quite some differences as visualization in Power BI is far more appealing for instance, reports also are more concise. Power BI Advantages Power BI has a couple of advantages over Excel: 1. Exclusive data visualization tool 2. Designed keeping business intelligence as the focus 3. Handles large chunks of data easily 4. Can be used on mobile devices 5. Connects to several data sources 6. Quicker processing 7. Customizable dashboards 8. Better interactivity 9. Appealing visualization 10. In-depth comparison of data files and reports 11. User friendly 12. Actionable insights can be achieved thanks to incredible data visualization. 13. Facilitates the exploring of data via natural language query. Excel and its most common uses Microsoft Excel is ideal in many ways 1. Faster calculations: making formulas in data and doing calculations is quick work with Excel. 2. Versatility: users don’t have to switch to another app due to its versatility. 3. Table creation: complex tables can be created for advanced calculations. Why Power BI is highly preferred? One of the reasons it’s the go-to tool is that Power BI dashboard can be accessed on a mobile device and can be shared among co-workers. Although a dashboard contains a single page, Power BI report allow for more than one page. Data interrogation is possible with dashboards. Power BI uses a combo of dashboards and reports for specific usage. Monitoring a business gets easier as various metrics are available to analyze and look for answers. Integration of cloud and on-premises data gives a compact view regardless of data location. Apart from its appealing looks, the tiles appear dynamic and change alongside the circulating data to facilitate updates. Prebuilt reports are also available for SaaS solutions. Secure environment, quick deployment, and hybrid configuration are a big plus of Power BI. Start Learning Power BI Packed with versatile tools There are a bunch of Power BI tools that allow better interactivity 1. Data gateway: installed via admin, it acts as a bridge between on-premise data sources such as Live Query and Power BI service. 2. Service: an online software service, where admin sharing occurs via cloud. Dashboard, data models, and reports also get hosted. 3. Desktop: Primary tool for publishing and authoring. Used by developers for creating reports and models. 4. Report server: hosts several types of reports including mobile, Power BI, paginated, and KPIs. Gets updated every fourth month, as IT professionals manage it. 5. Mobile apps: made for windows, Android and iOS. On the report server, users can view the dashboard and reports. Power BI filters and Data sorting The filters in Power BI allow for refined results to appear based on value selection. Some commonly used filters are: 1. Report level 2. Visual level 3. Automatic 4. Page-level 5. Drill-through 6. Cross drill What’s better is that users get both basic and advanced modes of utilizing the filters to get the desired results. Check Business Analytics Course Content More factors that make Power BI the first choice 1. Q/A and custom pack 2. Quick spotting of Data trends 3. Available on the go access 4. Scheduling Data refresh 5. Intuitive and better UX features 6. Storing, analyzing, accessing huge amounts of data without hassles 7. Data integration into a centralized dashboard 8. Forecasting via inbuilt predictive models 9. Security features (row-level) 10. Various cloud services integration 11. Access control Apart from the listed plus points, one can also use Power BI API which allows pushing data into a set, rows to a table can be further added. The data then shows up in dashboard tiles as a visual in the reports. Advanced Excel Crash Course Conclusion Power BI is the right choice compared to excel when the target is 1. Maneuvering large data for insights 2. Creating complex, graphically interactive visualizations 3. Making tabular format reports 4. Collaborative teamwork 5. Dealing in business intelligence and profound data analysis

How AWS is a perfect upgrade to start a career in Cloud technology?

Introduction Are you battling with the chicken-and-egg conundrum in trying to launch your Cloud career? For experience, you need a job, but there is no job without experience.  When hiring a cloud professional, most organizations demand years of past experience making the journey of a newbie all the more challenging. So how can you get your first break with zero Cloud experience? Will a Cloud certification appear as your savior? Yes, if you choose the right one and use it in the right way.  Career Scope in Cloud Computing  In today's IT world, starting a career in Cloud technology will give you a promising future. If we go by Statistics, Cloud computing has attained a market size of 250.04 billion dollars in 2021. Fortune Business Insights reveals that the market size for cloud is expected to expand at a compound annual growth rate of 17.9% helping it to reach 791.48 billion dollars by the year 2028.  90% of the companies worldwide are now on the Cloud, and the majority of them are spending one-third of their IT budget on cloud-based services. So Cloud technology is here to stay for many years, and demand for Cloud-related skills will only grow at an exponential rate.  One big step toward starting a cloud career is doing a Cloud certification and the best certification is AWS for Cloud Computing beginners. Why do you need a Cloud certification in the first place? Trying to get a job with knowledge of Cloud computing without certification is akin to knowing how to fly a plane but not having a pilot's license. A Cloud certification will be the best career upgrade for Cloud enthusiasts. I tell you why. 1. It makes your resume stand out Let's assume that you are in an interview. There are hundreds of other applicants having the same qualification as you. Why the interviewer will select you and not the others? This is exactly where Cloud certification comes into play. Talking about your cloud certification will make a big difference. The interviewer will see that you have good knowledge of various cloud services and practiced them well. So if you do well enough in an interview and have Cloud certification, your chances of cracking that interview are high.  2. Shows your dedication to your work Software engineers can make a career switch to Cloud by doing a certification in it. Entry-level IT or Cloud professionals can also expect a promotion or at least a hike in their salary by doing a Cloud certification as it shows a greater commitment to their roles.  Also, a Cloud certification will be helpful to you if you want to get a foreign job as Cloud certification is recognized internationally.  Start Learning AWS Cloud Why choose AWS Cloud Certification? Let us first discuss what is AWS before going into the details of why the best Cloud certification is AWS for the Cloud freshers. Amazon Web Service or AWS is Amazon's cloud platform that supports more than 100 on-demand cloud services. Individuals and organizations can use these web services like computing power, databases, storage, security, and more on a subscription basis.  Features that make AWS the God of Cloud certification are: 1. AWS is cost-effective to individuals as well as businesses.  2. According to Gartner, AWS is growing 10 times faster than its key competitors like Microsoft Azure or Google Cloud platform. So AWS is the Best Cloud certification to start a career in Cloud technology.  3. AWS is far more secure than its competitors since AWS now has several data centers that are routinely inspected and carefully maintained, and they have attempted to make their database centers as inconspicuous as possible. 4. Clearing AWS certification is easier than other Cloud certifications. This is because AWS provides more documentation, white papers, and instructor-led training courses than any other AWS certification.  5. According to PayScale, if you consider salaries across all Cloud roles then AWS certification has the upper hand over Microsoft Azure and Google Cloud Platform. Check Devops Course Content Conclusion To wrap it up, Cloud computing has got a very bright future. A Cloud certification is a must for every Cloud aspirant and AWS is the best in the business which can open up new and lucrative avenues for both Cloud beginners and pros. Read More On:- 5 Reasons for Choosing AWS with Devops

Complete guide to start Career in Salesforce

Salesforce is a business firm that specializes in SaaS, which is Software as a Service (SaaS). They provide CRM services, which deal with customer relationship management. Over the years they have dominated a good share of the market, as they are one of the top companies, assisting businesses to efficiently manage their customers. Several aspirants wish to make their Salesforce careers a reality, and barely have a clue where to begin. Here, you will get a proper idea about the same. Some Salesforce Jobs in high demand Both IT and non-IT profiles are available. The most common ones are: 1. Developer (helps to extend platform functionality via code. Uses API for integration with other systems) 2. Admin (maintaining systems, ensuring proper usage and training the users if required. Also fixing bugs and checking functionality via clicks instead of code) 3. Marketer (responsible for campaigns marketing related and optimizing the performances) 4. Architect (building complex solutions, understanding products, and business processes in full intricacies) 5. Business analyst (assesses the right and wrong in terms of what works for the company, responsible for Salesforce implementation, thereby creating user requirements) 6. Consultant (assists customers with implementing the CRM software) 7. Implementer (doing an implementation for developed apps) 8. Sales (specializing in sales and pre-sales plus after-sale support) For all these roles, one has to be properly trained, getting professional help is the obvious path to take, however aspirants are free to choose their learning resources. Apart from learning from Salesforce Representatives directly as a volunteer, one is recommended to opt for a reliable Salesforce course from reputable institutes. How to get placement in Salesforce? These are some methods you can use to land your first job in the firm. 1. Job portals (searching for Salesforce jobs and applying online) 2. Talent recruitment (recruiters look for capable candidates who are competent enough for the job. Contacting them can be fruitful) 3. Official website (checking for new vacancies) 4. Reference (third-party individuals who can refer you to the company. Connections can help). 5. Internship (volunteering to do real-world projects while learning) How to start your Salesforce journey? Salesforce has provided some ways that allow newbies to learn more about the company, they can dive deeper by: 1. Creating playground account (signing up for playground account via trailhead. Useful for learning features) 2. Joining users network (helps in expanding knowledge through networking, thereby bettering the chances of landing a job) 3. Volunteering: (great for the understanding of real-world projects and the company’s working atmosphere. Also assists in building resumes while getting noticed for your capabilities). Start Learning Salesforce Lightning Course Common Salesforce interview questions These are a couple of Salesforce interview questions for different posts that are frequently asked. 1. Features of Salesforce 2. Object and app difference 3. App types and sharing rule 4. Role and profile difference 5. Audit trail 6. Master-detail relationship 7. Dashboard and workflow 8. Trigger and Apex 9. CRM usage and benefits 10. Dynamic and static dashboard difference 11. Junction object and governor limits 12. Report types and permission sets 13. Force.com platform function 14. Dashboard components and validation rules 15. Data loss causes 16. VisualForce (force.com) 17. Record types 18. Trigger and workflow difference 19. Page layouts and fiscal year 20. Skinny table and considerations 21. Object relationship types and sandbox 22. Wrapper class and record sharing ways 23. Future annotation 24. SOQL and SOSL differences 25. API examples and usage 26. Lightning and available Email templates Check Salesforce Developer Course Future scope in Salesforce Salesforce is one of the hottest companies in the market right now, landing a job here could set one up for years. All the jobs are well paid, which is one obvious reason why they are popular among the aspirants. The most common profile is none other than a Salesforce developer who is expected to improve the functionality of the software using code. In Indian currency, one can expect a monthly salary of above 100k, which only increases with years of experience under your belt, not to mention capabilities. Conclusion Building a career with a company that is sure to transform you into a competent professional is a worthy investment in itself. Choosing the free online resources to learn is always an option, but understanding concepts clearly from the professionals themselves would be a better way to cement your career in Salesforce. Upon completion of the course, you will be presented with Salesforce certification which would only add authentic value to your qualifications, increasing your probability of being hired. Start Career in Salesforce with Admin Profile

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