Cloud Platforms interview questions

1. A.the AWS managed service for running Spark, Hive, and other big-data frameworks on provisioned or serverless clusters
2. B.the Azure unified SaaS analytics platform that brings data engineering, warehousing, and BI together on OneLake
3. C.the BigQuery capability to train and run machine-learning models using SQL directly on warehouse data
4. D.the single, tenant-wide data lake underlying Microsoft Fabric where all workloads store data in open Delta format

Answer + AI explanation with Pro

1. A.Amazon EMR
2. B.Dataproc Serverless
3. C.BigLake
4. D.Amazon Athena

Answer + AI explanation with Pro

1. A.Amazon EMR — the AWS S3 bucket type providing managed Apache Iceberg storage with built-in maintenance like compaction and snapshot expiration
2. B.Amazon EMR — a unit of BigQuery compute capacity that the engine allocates to execute the stages of a query
3. C.Amazon EMR — the AWS managed service for running Spark, Hive, and other big-data frameworks on provisioned or serverless clusters
4. D.Amazon EMR — the AWS option that runs Spark and Hive jobs on automatically provisioned capacity without managing clusters

Answer + AI explanation with Pro

1. A.the Google Cloud storage engine unifying governance and fine-grained access over open table formats across BigQuery and external engines
2. B.the AWS serverless ETL service with a managed Spark runtime and a central data catalog for schema discovery
3. C.the in-memory acceleration layer for BigQuery that caches data to deliver sub-second responses for dashboards
4. D.the AWS serverless query service that runs Trino-based SQL directly over data in S3 with no clusters to manage

Answer + AI explanation with Pro

1. A.Azure Synapse Analytics
2. B.Athena
3. C.AWS Glue
4. D.S3 Tables

Answer + AI explanation with Pro

1. A.AWS Glue — the AWS serverless ETL service with a managed Spark runtime and a central data catalog for schema discovery
2. B.AWS Glue — the GCP fully managed service for running Apache Beam batch and streaming pipelines with autoscaling
3. C.AWS Glue — the GCP serverless data warehouse that separates storage from compute and queries petabytes with standard SQL
4. D.AWS Glue — the Azure unified SaaS analytics platform that brings data engineering, warehousing, and BI together on OneLake

Answer + AI explanation with Pro

1. A.the AWS Glue component that scans data stores, infers schemas, and populates table definitions in the Glue Data Catalog
2. B.the GCP managed service for running Spark and Hadoop clusters, including ephemeral and serverless options
3. C.the AWS serverless query service that runs SQL directly over data in S3 using Presto and Trino engines
4. D.the Google Cloud service that runs Spark batch and interactive workloads without provisioning or managing clusters

Answer + AI explanation with Pro

1. A.Trino
2. B.OneLake shortcut
3. C.Google BigQuery
4. D.Amazon Athena

Answer + AI explanation with Pro

1. A.Amazon Athena — the GCP managed service for running Spark and Hadoop clusters, including ephemeral and serverless options
2. B.Amazon Athena — the AWS serverless query service that runs SQL directly over data in S3 using Presto and Trino engines
3. C.Amazon Athena — the AWS managed service for running Spark, Hive, and other big-data frameworks on provisioned or serverless clusters
4. D.Amazon Athena — the AWS option that runs Spark and Hive jobs on automatically provisioned capacity without managing clusters

Answer + AI explanation with Pro

1. A.the GCP serverless data warehouse that separates storage from compute and queries petabytes with standard SQL
2. B.the GCP fully managed service for running Apache Beam batch and streaming pipelines with autoscaling
3. C.the Google Cloud service that runs Spark batch and interactive workloads without provisioning or managing clusters
4. D.a unit of BigQuery compute capacity that the engine allocates to execute the stages of a query

Answer + AI explanation with Pro

1. A.Google BigQuery
2. B.Fabric shortcut
3. C.BigQuery BI Engine
4. D.EMR Serverless

Answer + AI explanation with Pro

1. A.Google BigQuery — the GCP serverless data warehouse that separates storage from compute and queries petabytes with standard SQL
2. B.Google BigQuery — the Google Cloud storage engine unifying governance and fine-grained access over open table formats across BigQuery and external engines
3. C.Google BigQuery — the GCP fully managed service for running Apache Beam batch and streaming pipelines with autoscaling
4. D.Google BigQuery — the AWS S3 bucket type providing managed Apache Iceberg storage with built-in maintenance like compaction and snapshot expiration

Answer + AI explanation with Pro

1. A.the Google Cloud service that runs Spark batch and interactive workloads without provisioning or managing clusters
2. B.the in-memory acceleration layer for BigQuery that caches data to deliver sub-second responses for dashboards
3. C.the AWS metadata store, compatible with the Hive metastore, that many AWS analytics services share for schema information
4. D.the GCP managed service for running Spark and Hadoop clusters, including ephemeral and serverless options

Answer + AI explanation with Pro

1. A.Glue Data Catalog
2. B.BigQuery BI Engine
3. C.Google Dataflow
4. D.Google Dataproc

Answer + AI explanation with Pro

1. A.Google Dataproc — the AWS option that runs Spark and Hive jobs on automatically provisioned capacity without managing clusters
2. B.Google Dataproc — the Google Cloud service that runs Spark batch and interactive workloads without provisioning or managing clusters
3. C.Google Dataproc — the AWS serverless query service that runs SQL directly over data in S3 using Presto and Trino engines
4. D.Google Dataproc — the GCP managed service for running Spark and Hadoop clusters, including ephemeral and serverless options

Answer + AI explanation with Pro

1. A.the GCP fully managed service for running Apache Beam batch and streaming pipelines with autoscaling
2. B.the BigQuery capability to train and run machine-learning models using SQL directly on warehouse data
3. C.the distributed SQL query engine that federates queries across data lakes and databases without moving the data
4. D.the AWS serverless ETL service with a managed Spark runtime and a central data catalog for schema discovery

Answer + AI explanation with Pro

1. A.Google Dataflow
2. B.Glue crawler
3. C.Google BigQuery
4. D.AWS Glue

Answer + AI explanation with Pro

1. A.Google Dataflow — the Amazon Redshift feature that queries external data in S3 directly, joining it with tables stored in the warehouse
2. B.Google Dataflow — a unit of BigQuery compute capacity that the engine allocates to execute the stages of a query
3. C.Google Dataflow — the GCP fully managed service for running Apache Beam batch and streaming pipelines with autoscaling
4. D.Google Dataflow — the single unified, tenant-wide data lake in Microsoft Fabric where all workloads store data in open Delta and Parquet formats

Answer + AI explanation with Pro

1. A.a unit of BigQuery compute capacity that the engine allocates to execute the stages of a query
2. B.the Azure platform unifying data warehousing and big-data analytics over dedicated and serverless SQL and Spark pools
3. C.the Google Cloud service that runs Spark batch and interactive workloads without provisioning or managing clusters
4. D.a Microsoft Fabric reference that virtualizes external data into OneLake without copying it

Answer + AI explanation with Pro

1. A.Amazon Athena
2. B.BigQuery ML
3. C.AWS Glue
4. D.Azure Synapse Analytics

Answer + AI explanation with Pro

1. A.Azure Synapse Analytics — the Azure platform unifying data warehousing and big-data analytics over dedicated and serverless SQL and Spark pools
2. B.Azure Synapse Analytics — a unit of BigQuery compute capacity that the engine allocates to execute the stages of a query
3. C.Azure Synapse Analytics — the AWS metadata store, compatible with the Hive metastore, that many AWS analytics services share for schema information
4. D.Azure Synapse Analytics — the distributed SQL query engine that federates queries across data lakes and databases without moving the data

Answer + AI explanation with Pro

1. A.the single, tenant-wide data lake underlying Microsoft Fabric where all workloads store data in open Delta format
2. B.the AWS option that runs Spark and Hive jobs on automatically provisioned capacity without managing clusters
3. C.the AWS serverless ETL service with a managed Spark runtime and a central data catalog for schema discovery
4. D.the Azure unified SaaS analytics platform that brings data engineering, warehousing, and BI together on OneLake

Answer + AI explanation with Pro

1. A.EMR Serverless
2. B.Glue Data Catalog
3. C.Trino
4. D.Microsoft Fabric

Answer + AI explanation with Pro

1. A.Microsoft Fabric — the GCP fully managed service for running Apache Beam batch and streaming pipelines with autoscaling
2. B.Microsoft Fabric — the Azure unified SaaS analytics platform that brings data engineering, warehousing, and BI together on OneLake
3. C.Microsoft Fabric — the distributed SQL query engine that federates queries across data lakes and databases without moving the data
4. D.Microsoft Fabric — the Azure platform unifying data warehousing and big-data analytics over dedicated and serverless SQL and Spark pools

Answer + AI explanation with Pro

1. A.the Google Cloud service that runs Spark batch and interactive workloads without provisioning or managing clusters
2. B.the AWS serverless ETL service with a managed Spark runtime and a central data catalog for schema discovery
3. C.the single, tenant-wide data lake underlying Microsoft Fabric where all workloads store data in open Delta format
4. D.the Google Cloud storage engine unifying governance and fine-grained access over open table formats across BigQuery and external engines

Answer + AI explanation with Pro

1. A.EMR Serverless
2. B.BigQuery BI Engine
3. C.OneLake
4. D.S3 Tables

Answer + AI explanation with Pro

1. A.OneLake — the AWS serverless query service that runs SQL directly over data in S3 using Presto and Trino engines
2. B.OneLake — the AWS serverless ETL service with a managed Spark runtime and a central data catalog for schema discovery
3. C.OneLake — the single, tenant-wide data lake underlying Microsoft Fabric where all workloads store data in open Delta format
4. D.OneLake — the in-memory acceleration layer for BigQuery that caches data to deliver sub-second responses for dashboards

Answer + AI explanation with Pro

1. A.the GCP storage engine that lets BigQuery and open engines query data lake tables, including Iceberg, with unified governance
2. B.the AWS serverless query service that runs SQL directly over data in S3 using Presto and Trino engines
3. C.a Microsoft Fabric reference that virtualizes external data into OneLake without copying it
4. D.the in-memory acceleration layer for BigQuery that caches data to deliver sub-second responses for dashboards

Answer + AI explanation with Pro

1. A.BigLake
2. B.Glue crawler
3. C.AWS Glue
4. D.Google Dataproc

Answer + AI explanation with Pro

1. A.BigLake — the Google Cloud storage engine unifying governance and fine-grained access over open table formats across BigQuery and external engines
2. B.BigLake — the GCP storage engine that lets BigQuery and open engines query data lake tables, including Iceberg, with unified governance
3. C.BigLake — the AWS option that runs Spark and Hive jobs on automatically provisioned capacity without managing clusters
4. D.BigLake — the AWS serverless query service that runs SQL directly over data in S3 using Presto and Trino engines

Answer + AI explanation with Pro