May 16, 2022

Top 20 AWS Lake Formation Interview Questions and Answers


        AWS Lake Formation is a service that allows you to quickly create a secure data lake. A data lake is a centralised, controlled, and secure repository where you may keep all of your data, both raw and processed for analysis. A data lake allows you to mix multiple forms of analytics and break down data silos to acquire insights and make better business decisions.

Defining data sources and the access and security policies you want to apply is all it takes to create a data lake using Lake Formation. Lake Formation then assists you in gathering and cataloguing data from databases and object storage, moving it to your new Amazon Simple Storage Service (S3) data lake, cleaning and classifying your data with machine learning algorithms, and securing access to your sensitive data with granular controls at the column, row, and cell levels. Your users will have access to a centralised data catalogue that lists accessible datasets and how they should be used. They then leverage these datasets with Amazon Redshift, Amazon Athena, Amazon EMR for Apache Spark, and Amazon QuickSight, among other analytics and machine learning services. Lake Formation builds on the capabilities available in AWS Glue.


AWS(Amazon Web Services) Interview Questions and Answers


Ques. 1): Is there an API or a CLI available from Lake Formation?


Yes. To incorporate Lake Formation capabilities into your bespoke apps, Lake Formation provides APIs and a CLI. You can also use Java and C++ SDKs to combine your own data engines with Lake Formation.


AWS Cloud Interview Questions and Answers


Ques. 2): What is a data lake, exactly?


A data lake is a scalable central store for both organised and unstructured data in huge numbers and types. Data lakes allow you to manage your data over its entire lifecycle. Ingestion and classifying data from various sources is the first stage in creating a data lake. Before analysis, the data is enriched, merged, and cleansed. Direct searches, visualisation, and machine learning make it simple to explore and evaluate data (ML). Traditional data warehouses are supplemented by data lakes, which offer greater flexibility, cost-effectiveness, and scalability for data acquisition, storage, transformation, and analysis. The typical issues of building and maintaining data warehouses, as well as constraints in the sorts of analysis that may be performed, can be overcome utilising data lakes.


AWS AppSync Interview Questions and Answers


Ques. 3): What is the AWS Lake Formation Storage API, and why should I use it?


The Lake Formation Storage API gives AWS services, ISV solutions, and application developers a single interface to read and write data in the data lake securely and reliably. To write data, the Storage API supports ACID (atomic, consistent, isolated, and durable) transactions, which allow you to reliably and consistently write data into Governed Tables, a new form of Amazon S3 table. You can query data in Governed Tables and ordinary S3 tables guarded with Lake Formation fine-grained permissions using the Storage API. Before sending the filtered results to the requesting application, the Storage API will automatically enforce permissions. Permissions for access are applied uniformly across a variety of services and tools.


AWS Cloud9 Interview Questions and Answers


Ques. 4): What exactly is the AWS Lake Formation?


Lake Formation is a data lake service that makes it simple to collect, clean, categorise, convert, and secure your data before making it available for analysis and machine learning. Lake Formation provides a central console from which you can discover data sources, set up transformation jobs to move data to an Amazon Simple Storage Service (S3) data lake, remove duplicates and match records, catalogue data for analytic tools, configure data access and security policies, and audit and control access to AWS analytic and machine learning services.

Lake Formation uses Zeppelin notebooks with Apache Spark to automatically control access to the registered data in Amazon S3 using AWS Glue, Amazon Athena, Amazon Redshift, Amazon QuickSight, and Amazon EMR to ensure compliance with your established regulations. Lake Formation configures the flows, centralises their orchestration, and allows you to monitor transformation operations that span AWS services. You may configure and maintain your data lake using Lake Formation instead of manually integrating numerous underlying AWS services.


Amazon Athena Interview Questions and Answers


Ques. 5): Can I utilise Lake Formation with third-party business intelligence tools?


Yes. You can connect to your AWS data sources using services like Athena or Redshift using third-party business applications like Tableau and Looker. The underlying data catalogue manages data access, so you can rest certain that access to your data is authorised and controlled regardless of whatever application you use.


AWS RedShift Interview Questions and Answers


Ques. 6): How does Lake Formation de-duplicate my data?


The FindMatches ML Transform from Lake Formation makes it simple to locate and link records that refer to the same thing but lack a valid identifier. Before FindMatches, data-matching problems were usually solved deterministically by constructing a large number of hand-tuned rules. Behind the scenes, FindMatches uses machine learning algorithms to learn how to match records according to each developer's business requirements. FindMatches selects records for you to categorise as matching or not matching, and then utilises machine learning to generate an ML Transform. You can then use this Transform to find matching records in your database, or you can ask FindMatches to provide you with more records to label in order to improve the accuracy of your ML Transform.


AWS Cloud Practitioner Essentials Questions and Answers


Ques. 7): How does Lake Formation keep my information safe?


Lake Formation safeguards your data by allowing you to define granular data access policies that protect your data regardless of which services are utilised to access it.

To use Lake Formation to consolidate data access policy restrictions, disable direct access to your Amazon S3 buckets so that Lake Formation handles all data access. Then, using Lake Formation, set up data protection and access controls that are enforced across all AWS services that access data in your lake. Users and roles can be configured, as well as the data that these roles have access to, down to the table and column level.

S3 server-side encryption is now supported by Lake Formation (SSE-S3, AES-265). Lake Formation additionally supports private endpoints in your Amazon VPC and logs all activity in AWS CloudTrail, ensuring network isolation and auditability.


AWS EC2 Interview Questions and Answers


Ques. 8): What are Machine Learning Transforms?


ML Transforms is a place where you can create and manage machine-learned transforms. These ML Transforms can be used in ordinary AWS Glue scripts once they've been constructed and trained. You choose an algorithm (for example, the FindMatches ML Transform), then input datasets and training samples, as well as tweaking parameters. These inputs are used by AWS Lake Formation to create an ML Transform that can be integrated into a standard ETL job workflow.


AWS Lambda Interview Questions and Answers


Ques. 9): How can I turn an existing Amazon S3 table into a regulated table?


You can convert existing Amazon S3–based tables in the AWS Glue Data Catalog to controlled tables by running the AWS Glue blueprint available on the AWS Labs Github page. Using the AWS SDK and CLI, you can also create a new governed table and edit the manifest information in Lake Formation. A list of S3 objects and related metadata indicate the current status of your table in the manifest information. You can also use AWS Glue ETL to read data from an existing table and construct a Governed Table duplicate of it. This allows you to migrate your applications and users to the Governed Table at your own pace.


AWS Cloud Security Interview Questions and Answers


Ques. 10): How does Lake Formation relate to other AWS services?


Lake Formation manages data access for registered data that is stored in Amazon S3 and manages query access from AWS Glue, Athena, Redshift, Amazon QuickSight, and EMR using Zeppelin notebooks with Apache Spark through a unified security model and permissions. Lake Formation can ingest data from S3, Amazon RDS databases, and AWS CloudTrail logs, understand their formats, and make data clean and able to be queried. Lake Formation configures the flows, centralizes their orchestration, and lets you monitor the jobs.


AWS Simple Storage Service (S3) Interview Questions and Answers


Ques. 11): What other options do I have for getting data into AWS to utilise with Lake Formation?


With AWS Snowball, AWS Snowball Edge, and AWS Snowmobile, you can transport petabytes to exabytes of data from your data centres to AWS utilising physical equipment. AWS Storage Gateway allows you to link your on-premises apps directly to AWS. You can use AWS Direct Connect to create a dedicated network link between your network and AWS, or Amazon S3 Transfer Acceleration to boost long-distance global data transfers using Amazon's internationally spread edge locations. Amazon Kinesis can also be used to import streaming data into S3. Lake Formation Data Importers can be configured to run ETL processes in the background and prepare data for analysis.


AWS Fargate Interview Questions and Answers


Ques. 12): What is the relationship between Lake Formation and AWS Glue?


With AWS Glue, Lake Formation shares infrastructure such as console controls, ETL code development and job monitoring, blueprints for creating data import workflows, the same data catalogue, and a serverless architecture. Although AWS Glue focuses on these operations, Lake Formation includes all of AWS Glue's functionality as well as extra capabilities for building, securing, and managing a data lake. For additional information, see the AWS Glue features page.


AWS SageMaker Interview Questions and Answers


Ques. 13): How does Lake Formation sanitise my data using machine learning?


Lake Formation offers jobs that use machine learning methods to deduplicate and connect records. Select your source, choose a desired transform, and provide training data for the necessary changes to create ML Transforms. The ML Transforms can be run as part of your regular data movement procedures once they've been trained to your satisfaction.


AWS DynamoDB Interview Questions and Answers


Ques. 14): What is the relationship between Lake Formation and AWS IAM?


Lake Formation works with IAM to automatically map authorised users and roles to data protection policies maintained in the data catalogue. You may also utilise Microsoft Active Directory or LDAP to federate into IAM utilising SAML thanks to the IAM integration.


AWS Cloudwatch interview Questions and Answers


Ques. 15): How does Lake Formation assist me in locating data for my data lake?


Lake Formation detects all AWS data sources to which it has access thanks to your AWS IAM policies. It scans Amazon S3, Amazon RDS, and AWS CloudTrail sources, identifying them as data that can be consumed into your data lake using blueprints. Without your permission, no data is ever moved or made accessible to analytic services. AWS Glue may also consume data from other AWS services, such as S3 and Amazon DynamoDB.

Lake Formation may also use JDBC connections to connect to your AWS databases as well as on-premises databases including Oracle, MySQL, Postgres, SQL Server, and MariaDB.

Lake Formation guarantees that all of your data is documented in a central data catalogue, allowing you to browse and query data that you have authorization to see and query from a single location. Permissions can be specified at the table and column level and are described in your data access policy.

You can add labels (including business attributes like data sensitivity) at the table or column level, as well as field-level comments, in addition to the properties automatically provided by the crawlers.


AWS Elastic Block Store (EBS) Interview Questions and Answers


Ques. 16): What types of issues does the FindMatches ML Transform address?


FindMatches solves record linkage and data deduplication issues in general. When you're trying to find records in a database that are theoretically the same yet have separate records, deduplication is required. If duplicate entries can be identified by a unique key (for example, if products can be uniquely identified by a UPC Code), this problem is straightforward, but it gets exceedingly difficult when you have to execute a "fuzzy match."

Record linkage is essentially the same as data deduplication, however instead of deduplicating a single database, this phrase usually refers to a "fuzzy join" of two databases that don't share a unique key. Consider the difficulty of matching a large consumer database with a limited database of known fraudsters. Both record linkage and deduplication difficulties can be solved with FindMatches.


AWS Amplify Interview Questions and Answers 


Ques. 17): How does Lake Formation organize my data in a data lake?


You can use one of the blueprints available in Lake Formation to ingest data into your data lake. Lake Formation creates Glue workflows that crawl source tables, extract the data, and load it to Amazon S3. In S3, Lake Formation organizes the data for you, setting up partitions and data formats for optimized performance and cost. For data already in S3, you can register those buckets with Lake Formation to manage them.

Lake Formation also crawls your data lake to maintain a data catalog and provides an intuitive user interface for you to search entities (by type, classification, attribute, or free-form text).


AWS Secrets Manager Interview Questions and Answers


Ques. 18): How does Lake Formation assist a data scientist or analyst in determining what data they have access to?


Lake Formation guarantees that all of your data is defined in the data catalogue, providing you with a central area to browse and query the data that you have access to. Permissions can be specified at the table and column level and are described in your data access policy.


AWS Django Interview Questions and Answers


Ques. 19): Why should I build my data lake with Lake Formation?


Building, securing, and managing your AWS data lake is simple with Lake Formation. Lake Formation automatically configures underlying AWS security, storage, analysis, and machine learning services to meet with your centrally set access policies. You can also monitor your jobs, data transformation, and analytic workflows from a single console.

AWS Glue allows Lake Formation to handle data intake. Data is automatically categorised, and the central data catalogue stores pertinent data definitions, schema, and metadata. AWS Glue also cleans your data, removing duplicates and linking entries across datasets before converting it to one of several open data formats for storage in Amazon S3. You can create access restrictions, including table-and-column-level access controls, and enforce encryption for data at rest once your data is in your S3 data lake. You may then access your data lake using a range of AWS analytic and machine learning services. All access is controlled, monitored, and audited.


AWS Cloud Support Engineer Interview Question and Answers


Ques. 20): Can I utilise Lake Formation with my existing data catalogue or Hive Metastore?


You can import your existing catalogue and metastore into the data catalogue using Lake Formation. To provide governed access to your data, Lake Formation requires your metadata to be stored in the data catalogue.


AWS Solution Architect Interview Questions and Answers


More on AWS:


AWS Glue Interview Questions and Answers


AWS Cloud Interview Questions and Answers


AWS VPC Interview Questions and Answers


AWS DevOps Cloud Interview Questions and Answers


AWS Aurora Interview Questions and Answers


AWS Database Interview Questions and Answers


AWS ActiveMQ Interview Questions and Answers


AWS CloudFormation Interview Questions and Answers


AWS GuardDuty Questions and Answers


No comments:

Post a Comment