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GCP > Data Analytics, AI/ML > Flashcards

Data Analytics, AI/ML Flashcards

1
Q

Vertex AI

A

AutoML
- Train ML models without code

AI Platform
- Train ML models using custom training code

Features:

  • Data labeling (human assistance in labeling training data)
  • Feature store (repo for ML features)
  • Workbench (Jupyter notebook IDE)
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2
Q

Cloud TPU

A

Tensor Processing Units

Google designed circuits for training deep learning models

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3
Q

Dataflow

A

Serverless, batch and stream data processing service
Develop (Apache Beam) and execute (on Dataflow instances) ETL, batch and continuous computation (map and reduce) pipelines

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4
Q

Dataproc

A

Managed service for running Hadoop, Spark, Hive, and Pig jobs/clusters on Compute Engine VMs

Use Spark and Spark SQL for data analysis
Use Spark ML libraries to run classification algorithms
Analyze data stored in Cloud Storage

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5
Q

Cloud Workflows

A

Serverless orchestration platform that executes services based on YAML or JSON defined workflows
Workflows - combine steps for GCP API services, Cloud Functions, Cloud Run

*NOT for large volume of data or complex sequence of jobs

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6
Q

Data Fusion

A

Fully managed service based on CDAP for building ETL pipelines without code

*Pre-built connectors and transformations

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7
Q

Cloud Composer

A

Fully managed workflow orchestration service for Apache Airflow DAG workflows.

Open source - supports on prem and multicloud

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8
Q

Dataprep

A

Visually explore, clean, prepare structured and unstructured data for analysis

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9
Q

Dataproc

A

Runs Apache Spark and Hadoop clusters

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10
Q

Data Fusion

A

Data integration service to build and manage ETl/ELT pipelines
Preconfigured connectors and transformations

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11
Q

Cloud Composer

A

Workflow orchestration service to author, schedule, monitor pipelines that span clouds and on prem

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12
Q

Data Catalog

A

Metadata management

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13
Q

Google Data Studio

A

Visual analytics, interactive dashboards

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14
Q

Dataform

A

Develop data workflows in SQL and collaborate with Git.

Schedule data workflows with incremental updates to downstream datasets.

Define data quality checks and get alets.

**Works with BQ

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15
Q

Ingestion tools (5)

A 
Pub/sub
Storage Transfer Service
Transfer Appliance
Cloud IoT Core
BQ
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16
Q

Data analytics storage (4)

A

Cloud Storage
Bigtable
Memorystore
BQ

17
Q

Services for ingesting data from other clouds (3)

A

Cloud Data Fusion
Storage Transfer Service
BQ Transfer Service

18
Q

Services for ingesting data from on prem

A

Data Fusion + Connector - for low-code, graphical UI

Transfer Appliance or Storage Transfer Service - large volumes

19
Q

Recommended method for ingesting batch workloads

A

gsutil or STS to ingest into Cloud Storage

20
Q

Services to ingest data via streaming

A

Pub/Sub - global, low latency

BQ - for analytics and reporting

Apache Kafka on prem or other clouds - Kafka to BQ Dataflow template

21
Q

Service to use to ingest data from multiple sources

A

Dataflow

22
Q

Cloud Data Loss Prevention

A

Service to inspect and transform structured and unstructured data from anywhere in Google Cloud
Classify, mask, tokenize sensitive info.
Scan BQ data
De-identify and re-identify PII in large data sets

23
Q

Smart Analytics suite consists of (3)

A

BQ
Data Studio
Cloud Composer

24
Q

Ways to send data to Cloud Logging

A

App Engine - auto record data to Cloud Logging

Logging agent

Custom logging messages to stdout and stderr

25
Q

Ways to ingest app data into GCP for analysis

A

Write data to file - store in Cloud Storage - BQ import function

Write data to database - Cloud SQL, Bigtable, Firestore/Datastore

Stream data via pub/sub

26
Q

Datastream

A

Captures change data from Oracle, MySQL, others

Data replicate using Dataflow templates to create replicated table in BQ

27
Q

Services for processing data (3)

A

Dataproc
Dataprep
Dataflow

28
Q

Data analytics services spanning pipeline (4)

A

Data Fusion
Data Catalog
Cloud Composer
Datastream

29
Q

Kubeflow

A

Library and tools for ML workflow deployment in K8

30
Q

TensorFlow Enterprise

A

Development environment for ML

31
Q

AI Platform Prediction

A

serverless, autoscaling service to host ML models

Used to serve trained models for online inference

GCP (8 decks)

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