Google Cloud Platform Fundamentals: Core Infrastructure

Big Data and Machine Learning

Q1) Name two use cases for Google Cloud Dataproc (Select 2 answers).

• Data mining and analysis in datasets of known size
• Migrate on-premises Hadoop jobs to the cloud
• Manage data that arrives in realtime
• Manage datasets of unpredictable size

Q2) Name two use cases for Google Cloud Dataflow (Select 2 answers).

• Extract, Transform, and Load (ETL)
• Orchestration
• Manual resource management
• Reserved compute instances

Q3) Name three use cases for the Google Cloud Machine Learning Platform (Select 3 answers).

• Fraud detection
• Data preparation
• Sentiment analysis
• Content personalization
• Query architecture

Q4) Which statements are true about BigQuery? Choose all that are true (2 statements).

• BigQuery is a good choice for data analytics warehousing.
• BigQuery requires that you provision database instances ahead of use.
• Once in BigQuery, data is not accessible from other GCP services.
• BigQuery is a good choice for online transaction processing.
• BigQuery lets you run fast SQL queries against large databases.

Q5) Name three use cases for Cloud Pub/Sub (Select 3 answers).

• Analyzing streaming data
• Storage of binary web content
• Decoupling systems
• Internet of Things applications
• Executing ad-hoc SQL queries

Q6) What is TensorFlow?

• A managed service for building machine learning models
• An open-source software library that’s useful for building machine learning applications
• A hardware device designed to accelerate machine learning workloads
• A managed service for building data pipelines

Q7) What does the Cloud Natural Language API do?

• It analyzes text to reveal its structure and meaning.
• It translates arbitrary strings into any supported language.
• It extracts text in various languages from images.
• It performs sentiment analysis on audio and video content.

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Google Cloud Platform Fundamentals: Core Infrastructure