Distributed computing concept in Intro to Computing - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a distributed system as a collection of multiple {{BLANK_1}} that work together.

Intro to Computing

A distributed system is a collection of multiple [1] that communicate and coordinate to achieve a common goal.

Drag options to blanks, or click blank then click option'

Acomputers

Bprograms

Cdatabases

Dusers

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'programs' because programs run on computers but the system is made of computers.

Choosing 'users' which are people, not devices.

Distributed computing involves multiple computers working together, not just programs or users.

2fill in blank

medium

Complete the code to show that distributed systems communicate over a {{BLANK_1}}.

Intro to Computing

In distributed computing, nodes exchange data over a [1] to coordinate tasks.

Drag options to blanks, or click blank then click option'

Adatabase

Bprocessor

Cnetwork

Dfilesystem

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'database' which stores data but is not the communication medium.

Choosing 'processor' which is inside a computer, not a communication link.

Nodes in a distributed system communicate over a network, which connects the computers.

3fill in blank

hard

Fix the error in the statement about distributed computing: 'Distributed systems always share a {{BLANK_1}} memory.'

Intro to Computing

Distributed systems do not always share a [1] memory; instead, each node has its own memory.

Drag options to blanks, or click blank then click option'

Ashared

Bvirtual

Clocal

Dcache

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'local' which is true for nodes but the statement is about shared memory.

Choosing 'cache' which is a small fast memory but not shared across nodes.

The correct term is shared memory, but distributed systems usually do not have shared memory; each node has its own local memory.

4fill in blank

hard

Fill both blanks to complete the dictionary comprehension that maps node names to their {{BLANK_1}} and filters nodes with {{BLANK_2}} load.

Intro to Computing

node_loads = {node: info[1] for node, info in nodes.items() if info['load'] [2] 70}

Drag options to blanks, or click blank then click option'

A['load']

B>

C<

D['status']

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' which would filter low load nodes.

Using ['status'] which is not related to load.

The code extracts the 'load' value from each node's info dictionary and filters nodes with load greater than 70.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps node IDs in uppercase to their {{BLANK_1}}, including only nodes with {{BLANK_2}} status and {{BLANK_3}} load less than 50.

Intro to Computing

filtered_nodes = { [1]: data['load'] for node_id, data in nodes.items() if data['status'] == [2] and data['load'] [3] 50}

Drag options to blanks, or click blank then click option'

Anode_id.upper()

B'active'

C<

D'inactive'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'inactive' status which excludes active nodes.

Using '>' instead of '<' for load comparison.

The comprehension maps uppercase node IDs to their load, filtering only active nodes with load less than 50.