# data[].attributes.body
Question 1:
A movie you love  watching in Christmas: i love watching "mi pobre angelito"

Question 2:
A type of food your doctor has suggested eating: my doctor hasn't suggested eating anything.

Question 3:
Somewhere you look forward to going on vacation: i love to travel in Amsterdam

Question 4:
A job you would never consider doing: I never job in nursing

Question 5:
A hobby  you’ve considered taking up: practice skating

# data[].attributes.headline
Defining and non defining relative clauses

# data[].attributes.subject
Defining and non defining relative clauses

# data[].attributes.body
Point 1:
1. D
2. D
3. N-D
4. D
5. N-D
6. N-D
7. D

Point 2:
1. Burj Khalifa, which was completed in 2010, is the tallest building in the world.
2. Coca - cola, which was also called Coke is the sold in over 200 countries.
3. Barack Obama, was born in Hawaii is the 44 President of the USA.
4. The moon was has water under its surface is the might be a future home for humans.

Point 3:
1. That
2. Which
3. Who
4. That

Point 4:
1. Which
2. That
3. That
4. Who
5. Wich
6. Wich

# data[].attributes.body
Point 1:
1. A
2. B
3. B
4. A
5. B

Point 2:
1. Broken
2. Been looking
3. Cooked
4. Been baking
5. Helped
6. Finished

Point 3:
1.  Last year
2. Never
3. Yet
4. Already
5. All weekend

# data[].attributes.body
Point 1:
1. Have won
2. Has made
3. Travelled
4. Circled
5. Has done
6. Visited
7. Also went
8. Has ever visited
9. Developed
10. Drove

Point 2:
1. Has - ever won
2. Has - followed
3. Have visited
4. Ever - visited
5. Has - developed

Point 3:
Conversation 1:
1. Have you phoned Dad yed
2. He has sent - already bought - went

Conversation 2:
1. Promised
2. Been

Conversation 3:
1. Have packed - didn't call
2. We already booked

# data[].attributes.body
Point 1:
1. Lot
2. Many
3. Any
4. Little
5. Much
6. Lots
7. Some
8. Any

Point 2:
1. Much
2. Any
3. Lots
4. Some
5. Much
6. Few
7. Many
8. Littñe
9. Any
10. A few
11. Many

# data[].attributes.body
Point 1:
1. A
2. E
3. B
4. F
5. J
6. C
7. G
8. H
9. D
10. I

Point 2:
1. Aisle
2. Connecting flight
3. Seat belts
4. Direct flights
5. Turbulence
6. Long-haul flights
7. Jet lag
8. Cabin crew

Point 3:
1. Trip
2. Journey
3. Journey
4. Trip

Point 4:
1. Dropped - off
2. Check in
3. Fill in
4. Get on
5.  Pick up
6. Take off
7. Get on

# data[].attributes.body
1. Few / a lot of
2. Much
3. A lot of / much a
4. Any / many
5. Some
6. Much / a lot of
7. Many
8. Much
9. Many /a lot of
10. Much / little

# data[].attributes.body
1. B
2. C
3. B
4. A
5. C
6. A
7. A
8. C

# data[].attributes.body
Point 1:
1. 10
2. 1
3. 6
4. 2
5. 4
6. 3
7. 8
8. 5
9. 7
10. 9

Point 2:
1. Syat
2. Take
3. Buy
4. Sunbathe
5. Have
6. Spend
7. Rent
8. Hire
9. Book

Point 3:
1. Warm, sunny
2. Comfortable, luxurious
3. Beautiful, lovely
4. Friendly, helpful
5. Delicious

# data[].attributes.body
Point A:
1. B
2. A
3. C
4. A
5. B

Point B:
1. b
2. e
3. c
4. d
5. a
6. f

Point C:
1. Working
2. To answer
3. To call
4. Offering
5. To tell
6. Making

# data[].attributes.body
Point 1:
1.  enjoy + play
2. start + work
3. prevent + brwak
4. need + move
5. mind + stay
6. plan + stay
7. can't help + think
8. make + work

Point 2:
1. Taking
2. Preferer
3. Being
4. Remembered
5. Making
6. Tried
7. Asked
8.Showing

Point 3:
1. To read
2. Taking
3. To like
4. Collecting
5. To ask
6. To see
7. Tidying
8. Breaking
9. Wait
10. Studying

# data[].attributes.body
Point 1:
1.
A: to leave
B: to do
2.
A: taking
B: falling
3.
A: winner
B: tall
4.
A: Giving
B: Be
5.
A: Hits
B: informed
6.
A: Thinking
B: Bothering
7.
A: Press
B: Learning

Point 2:
1. Went on to become
2. Stop so much
3. Regret i lied to you
4. Remember was sure he had checked the lock before he went home
5. Meant i had less money to spend

# data[].attributes.body
Point 1:
A.
1. Do
2. Resign
3. Was made
4. Applied for
5. Work
6. Got promoted
7. Do
8. Was sacked
9. Set up
10. Retire

Point 2:
A.
1. Unemployed
2. Self-employed
3. Well qualified
4. Temporary
5. Part-time

Point 3:
A.
1. Promote: promotion
2. Apply: applyment
3. Retire: retired
4. Employ: employed
5. Qualify: qualified
6. Resign: resignation

B.
1. Scient: scientific
2. Law: lawyer
3. Music: music
4. Pharmacy: pharmacist
5. Farm: farmer
6. Translate: translator

# data[].attributes.body
Point 1:
A.
1. Manufactured
2. Produce
3. Expand
4. Export
5. Launch
6. Become
7. Drop
8. Set Up
9. Take over
10. Merge
11. Market
12. Close down - grows

C.
Do:
Business
Market research
Money
A decision

Make
A job
A loss
Somebody redudant

Point 2:
A.
1. A chain
2. A business
3. A multinational
4. A branch
5. Head office

B.
1. The staff
2. A client
3. A colleague
4. A manager
5. A customer
6. The CEO
7. The owner

Point 3:
A.
1. Set up
2. Become
3. Export
4. Expand
5. Import
6. Launch
7. Market
8. Take over

B.
1. Does
2. Make
3. Make
4. Does
5. Do
6. Make
7. Does
8. Make

# data[].attributes.body
Point 1:
1. Rita and I going to on
2. It's ok, i will play lunch
3. This year I will learn

Point 2:
1. I'm having
2. I'm paid
3. You got
4. You went
5. I lent
6. W waited

Point 3:
1. I'll pay for it.
2. I''' have a holiday in Florida.
3. I'm going to help you.
4. I'll be a doctor like my father.
5. I'm going to get it.
6. I'm going to have a party tonight.

Point 4:
1. I'm going to.
2. I'm going to.
3. I think i'll.
4. I think i'll.
5. I think i'll.

# data[].attributes.body
Point 1:
1. Grew
2. Produced
3. Used
4. Ate
5. Used
6. Introduced
7. Bought
8. Have caused

Point 2:
1. Bought
2. Ate
3. Throw
4. Start - "Love Food Hate Waste"
5. Persuade

Point 3:
2. Pasta is now eaten by people all over the world

4. The first cookbook with tomato recipes was written in Naples by a writer.
6. The cheese is made by cheese makers with cow's milk.

# data[].attributes.body
Point a:

1. E
2. F
3. J
4. A
5. C
6. H
7. B
8. I
9. G

Point b:

1. hadn't taken - miss
2. hadn't won - had'n gave
3. enjoy - would come
4. wouldn´t bought - been
5.  forgot - wouldn't remind
6. arrive - wouldn't caught
7. hadn't lent - wouldn't be able
8. Hurt - Fell
9. hadn't found - hadn't seen
10. knew - apply

# data[].attributes.body
Point 1:

1. Would
2. wouldn't
3. could
4. mightn't
5. couldn't
6. would
7. was
8. gave

Point 2:

1. Mistake
2. i'd
3. were
4. might
5. saw
6. tried

Point 3:

1. had
2. would bring
3. done
4. been
5. would accept
6. would offer
7. known
8. been
9. would be

# data[].attributes.body
Point a:

1. F
2. G
3. E
4. A
5. D
6. B

Point b:

1. Tell - don´t count
2. won´t write - won´t remember
3. If - call - will get
4. miss help - will ask
5. phone - will hear
6. Miss - will movie
7. listen - will understand
8. won´t be - will be
9. drive - will give

Point c: Circle the correct form

2. Will see
3. Use
4. Don´t get
5. Won´t take
6. Won´t work
7. Will be
8. isn´t

# data[].attributes.body
Point 1:

1. was built
2. was made
3. were cut
4. was discovered
5. is waking
6. was played
7. was record
8. was educated

Order the words to make sentences:

1. The film The Iron Lady is based on the life of Margaret Thatcher
2. Mobile phones weren´t by invented Apple
3. Petrol isn´t lead in used nowadays
4. Low-cost flights are sold online
5. The were Argentinian architect designed an by Petronus Towers
6. Avatar wasn´t directed by Steven Spielberg

Rewrite the sentences in the passive:

1. Jeans are wore people of all
2. Laptop computers were invented did not by Microsoft
3. Is heating controlled by does a computer?
4. The Millennium Trilogy was written by Stieg Larsson
5. The Scream was did not painted by Picasso

# data[].attributes.body
Point 1

2. for
3. about
4. At
5. back
6. In
7. about
8. as
9. to
10. in

Point b

- Paragraph: 3
- Paragraph: 1
- Paragraph: 4
- Paragraph: 2

Point c

Movie Cruella
- It was directed by Craig Gillespie
- It is set in shot in the outskirts of London
- It is based on the movie "101 Dalmatians"
- It's about Teenager "Estella" has a very big dream and that is to become a fashion designer in London, but she has challenges and adventures to achieve it.
- It stars is Emma Stone who plays the character of “Estella and Cruella” and Emma Thompson who is “The Baroness”.

# data[].attributes.body
Point 1:

1. 5
2. 3
3. 12
4. 1
5. 11
6. 5
7. 2
8. 10
9. 9
10. 7
11. 4
12. 8

Point 2:

1. cast
2. start
3. soundtrack
4. plot
5. scene
6. audience
7. sequel
8. special effects
9. script
10. extra
11. subtitles
12. review

Point 3:

1. B
2. D
3. C
4. A
5. E
6. F