Shadowing Practice: Uber Data Engineering Mock Interview - Ride-Sharing Data Warehouse Schema - Learn English Speaking with Video

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is designing a data model for a ride -sharing service app.

Hello, everyone.

Welcome to another data engineering mock interview with Exponent.

Today, we are going to talk a little bit about data modeling with our guest, Kajal.

Thank you so much for the job being here, Kajal.

Would you take a moment to introduce yourself to our viewers?

Yeah.

Hey, my name is Kajal.

I work as a data engineer in Uber.

I've been working in that company for a

while we take care of everything from requirement gathering to data modeling to detail design

and then finally the implementation part of it

and of course the data modeling plays a very very key role

when it comes to the pre and the proposed

because eventually you want your users to be able to query your data in the most optimized way.

And data design is actually the core of it.

If your design is not accurate, the query performance will never be accurate at the end of the day, actually.

Exactly.

So we are going to do one such data model today.

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Let's just jump right into our question for today, which is designing a data model for a ride -sharing service app.

So we are creating a ride -sharing app.

So my understanding of this ride -sharing app is

that we want to build data model for basically a service app which allows the users to book gaps via the app.

And basically the driver comes, picks that user, and then drops them to a desired location, right?

That is the expectation is, right?

From this, I have three outstanding questions actually.

What are the use cases?

Second is, who are the users?

What are the impact of these use cases?

Yes, let's tackle it by one.

What are the use cases?

My use case is twofold.

One is going to be more operational specific.

I would like to understand the driver conversion rate.

By driver conversion rate, I'm meaning optimizing the driver onboarding process from sign up through to the activation.

So as you can imagine, there could be a lot of steps in between.

So I would like to know how we can optimize the driver onboarding.

The second is more trip specific week i should be able

to understand the trip requested how many the trip completion rates uh for the user uh

and you can spice it up with more additional details if you want

user actually makes sense okay these are the use cases

and who will be the users for these okay

so the users could be

so for like i said the operational user operational metrics would

be would be useful for you know data used by data analysts machine learning engineers

or operational personnels as well or even for business optimization purposes to

get the driver onboarding time looking at data analysis right data analytics yes okay

and uh could you share the impact like what would you

do what would be the impact of these use cases

so as to um like once you build these data models

and then how yes uh yes that's that's a good question

so the the immediate impact

which i could think of is like a business for the first type of use cases is a business optimization

so i would like to get the driver onboarding time lesser uh the more metrics

and the more pain points we could understand uh the better.

The second is more trip related data that can directly contribute to the profits or improving the business,

understanding where the trips are going well

and more importantly where it's not going well and any other analytical points that we could gather.

basically cost opportunity analysis awesome from these use cases i thing we want to build a data warehouse data model

so I think first would I would like to call out the entities

that I would be building upon the data model first will

be the driver itself right second will be the user I think third I can call out offers

and then we can have trips and

if we have a driver i think we will need vehicle also there could be a partner associated to vehicle

and driver then we can have ratings essentially this will be the feedback which we share

based on this and then of course the most important part of it the payments i think

if you there is every we all work for money

so one quick question sorry the what about the offer seems

to be very interesting to me could you explain what it is sure sure

so essentially what my understanding of a trip is

that um whenever i as a user try to book a ride right

there is a request that goes to each of the drivers

and then once they accept it then only it becomes a trip

so essentially it would be an offer first to the driver

and once they accept it then only it becomes a trip before

that it doesn't make sense to include all of

that request data into a trip

so okay gotcha nice yeah um should i start with the um basically design of how my dimensions

and facts would look like yeah go for it yeah

so firstly i would like to call out my dimensions right

so the first dimension that i would want to look like

start off with this driver itself

so in this case my driver uuid will be the key itself driver uuid is nothing

but the unique identifier of the driver

and then we can take up sign up sign up city

maybe basically this will help us with the demographics of the driver

and then sign up city

and then we can have sign up times time then essentially

now the whole the we can also start with the onboarding columns for example

which will help us with the conversion rates so for example

when we whenever i am assuming whenever we onboard try to onboard the driver we start with pgc

that is the background check

and then uh not background check maybe first we will look for some documents maybe uh pre -start off documents

and then maybe we'll go for background check and then the final documentation

and then uh if everything goes out right uh everything gets approve the the driver gets activated right

100

so i would love to include all of those columns also into this

101

that is bgc approval time then i would actually then first

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go with the first bgc approval time then first oh i'm sorry first document approval time then uh

103

and then first activation time first first activation time time

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and then actually i would want to have latest timestamp of

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each also then we can have all of this in the last

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and then the current status of the driver

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so essentially he could be currently active ineligible it could actually be helpful to analyze

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that like if the driver was active like some time ago uh

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and he's ineligible now it can also help us understand like uh

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when he became last active and then

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if he's inactive then uh it can help the um users

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to deep dive on why is it happening now um should i move ahead

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or do you have any further questions on why i made choices

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so uh you are capturing different times here that is very interesting um

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so could you explain why you would want to do

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that in this in this dimension opposed to using a different tables or anything Okay,

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so the whole idea behind the driver dimension was that this dimension should be,

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basically it's a, we can call it a type 1 dimension

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because we are not maintaining sort of history of like of the driver itself.

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Driver UUID is the key, so it's just simply the same record will be updated.

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The whole idea behind having this is that when we also look at the conversion rate, so for example, from BGC to DOC approval to activation finally,

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we also would want to eventually see,

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that's my observation from my previous experiences is that they can also become ineligible eventually and then become again active, right?

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So, in those cases, you will see a differentiator

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or there could be basically a case wherein the driver tries to become active by re -uploading a document,

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but they become ineligible.

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So, we want to also capture that information.

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Maybe today we need activation, but in future we might need inactivation, reactivation rates

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so these all those things will will be very helpful uh

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when we capture this kind of yeah makes sense yeah yeah

131

that's good that's good the type one type one is really interesting

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because we don't really have to maintain history for that that's a that's a good type yeah

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and the second call out here is that um i think uh what i wanted to call is is that

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having history is also a kind of a burden on the user

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if in in case we are very much clear on what

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the requirement is for me the requirement is very clear

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that we want to look at the activation rate itself uh

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and i just don't want to burden the users to keep

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on aggregating the data based on the timestamps okay yeah

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and moving on to the next uh thing then in this case i think there is some information

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that i would keep which will be kind of replicated

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that is the user uid which will be the key okay the reason

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that i'm using the user uuid user not the rider

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because um maybe a rider would be someone who has actually signed up already

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but there could be a case where without signing up they

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are just requesting rights right we don't want to lose out an opportunity to earn

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so it's better to refer to the user instead of rider

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and then the current status of the user The whole idea behind this is

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that we also want to capture users who maybe we want to blacklist for their certain behaviors

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or how they are behaving over a period of time.

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Maybe we want to blacklist them or make them in an eligible for whatsoever possible.

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So, yeah, we would also want to capture the status of the user.

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This will leverage the platform itself to see the user behavior as well.

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then uh we will have so can this user be both a rider

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and uh and a driver right it can like it's kind of like a no

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or it's like no no there is a user itself it can only be a rider the whole idea is

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that um i want to i always wanted to keep these

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both things separate even though there are some similarities uh

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because they are different entities all together and i assume

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that the users of the data that is the ml

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and they will have separate use cases

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and yeah it is better to keep the data placed yeah okay like

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that yeah then we have vehicle duty

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and then status of the vehicle essentially i want to keep the status of the vehicle also

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because possibly the vehicle itself is not um sorry the vehicle itself is ineligible

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because of maybe expired documents of the vehicle itself itself right

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and the if

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and in those cases we do not want to allow those driver to be making trips

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if they are if their vehicles are uh not available with correct correct documentation

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or they have expired documents yeah then we have partner um in this case partner would be like any fleet partner,

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maybe it could be owning a vehicle or it could be a company which is providing drivers to the platform itself.

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So in those cases, we can have a different partner entity and then the status associated.

173

Then at next, we can have our offers. In this case.

174

So in this case, yeah, of course, like the partner UUID will be the unique key or primary key we can say.

175

Maple UUID will be the primary key and then in case of offers what I would like to see

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what we have discussed in now right offer is something

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that we request to the driver and once

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that offer is converted then it makes to a trip right

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so we will have a key column called is offer uuid unique okay uh and then trip uuid

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so this will also be unique actually because

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when offer can only be converted into a one trip uh

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it cannot one offer cannot can never convert to multiple trips

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so that's the whole idea behind it is that

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and then offer timestamp maybe right timestamp start uh okay yeah start i think

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that is something like maybe we can it can eventually be used to see

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that when was the offer made and finally

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when it got converted to a trip right these kind of

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matrices also can be eventually helpful of uh to see how

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much is the wait time for the owner right from the

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offered point to the time they actually get the right

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so then we have trip right

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so of course it will be like a foreign key for

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with the offers then we have trip uuid then of course

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this will be a unique key for this table okay uh

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till now we have only seen the dimensions uh coming back

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to how the fact will look like i i believe

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that uh it's the best to keep um trip as facts

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because uh trip will also can um actually include the things

199

that uh what would be the earnings

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and all of those things as part of the trip itself like what was a fair

201

and then whether they got any uh additional incentives with for the trip itself right

202

so it would be great that if we include it as a fact So trip UUID, trip, who was the driver involved in the trip,

203

driver UUID, then we can have, of course, the user UUID,

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and of course, these will also be the foreign key for the user

205

and the driver rate

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and then we will have trip start time stamp trip

207

and time stamp and then you can have trip start tip trip

208

and the fare that was shared to the user for this

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and then maybe any additional um incentives that were given to the earner for the trip

210

by the platform itself maybe to incentivize them

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if they're maybe a new driver or something like that

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and then should we include anything else hmm i think this

213

looks good to me as of now right um then then finally i think uh we can have payments

214

and rating right so okay then we can have um right okay so with the trip i think ideally we can have

215

payment uid which will actually act as the foreign key for the payment and then we can have

216

payment uid which will be the primary key here right and then actually the payment amount

217

right essentially what was the amount actually paid by the uh

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driver sorry uh payment found essentially paid by the user okay um

219

and then payment method

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and then we can have payment type right so okay

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and then we went down and then who paid it user id right so essentially we can this is this

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this can actually help us eventually understand

223

that given the geolocations where the platform is earning the most right like where we are getting the highest traction

224

or highest number of rides is one thing maybe you are getting more rides in one geolocation

225

but the payment actually is more because the people are actually traveling more distances hence leading to higher payment amount

226

Kind of touches on our second use case as well where we wanted to do the trip rejections or trip completions.

227

You have a geo -related analysis would be helpful there.

228

And then we have the ratings.

229

Yep.

230

So what I want to do is

231

that associate rating to a trip

232

because a driver rates a user for a trip a user trades a driver for a trip

233

so i want to keep it as one entity

234

and essentially it can help us eventually do the analysis uh

235

in a better way wherein we want to also see um

236

for a given trip uh what the experience has been uh for the user as well as the driver

237

which can actually

238

help us with the cost cost increase our cost opportunities eventually

239

if we look at feedbacks properly from the users or the drivers also so yeah so driver rating user and then user

240

rating driver and then maybe complement if for driver if there is a specific complement that

241

is given by the user to the driver maybe comment or driver and then maybe we can also see

242

uh complement for user right essentially maybe if there is a feedback by the sorry

243

complement for and then maybe we can have complement for sorry comment for user sorry comment

244

right so these are i think the basic and also we would also want to see if

245

the experience for the user has been so good

246

that they have actually went ahead

247

and given a tip right a tip could be directly associated to the rating the better the rating i think

248

if there's a tip involved

249

and of course the experience has been tremendously good for the right so

250

so should we also have a rating uid here for the

251

rating table actually the thing is initially i was not thinking of having a rating uid as such because

252

that will actually be a simple lookup table wherein uh rating

253

uuid would have just like ratings from one to five uh eventually i don't see

254

that changing um i feel that it is going to be something very static what

255

that is my observation do you think otherwise that's why i was not not about the rating

256

so yeah the rating is a column the rating uid what

257

i'm what i was referring to is more of a key for this table oh awesome actually good

258

because the trip by trip

259

so you will have a same i mean the whatever we

260

qualify a driver rating as a separate rating rating id

261

and then a user rating as a separate rating id it will have two different columns

262

but the trip would be same it's right

263

so for the same trip both people both may rate at

264

a different point in time is what i'm like what i'm is

265

that the not sure if that is your understanding as well

266

but that's what i was understanding so curious whether we should have

267

that information as well yeah actually i think is the whole idea behind it was

268

that again this was supposed to be a type one dimension table within in this for the same trip

269

if there is an update for example driver gave the rating to the uh user on day one itself

270

and user is giving rating to the driver or maybe on the 10th day, the data should be updated in this table itself, making the trip with a unique key, right?

271

Okay.

272

Yeah, that's what idea I had it in my mind.

273

Okay.

274

Yeah, if I would have actually planned to do it in a different way, wherein with the trip, I want to make rating UUID,

275

then I can implement it as a key value kind of format, wherein a key could be driver rating user and then the value with it.

276

But in this case, I'm having it as a separate column itself um i think

277

i can um thank you for calling this out it's a good point actually

278

but in this case i'm making this decision to have one row for each trip uh

279

so i think i can live with this kind of yeah yeah i think that i have included all the entities possible

280

I think I should be able to include all the use cases

281

and I've also tried to foresee what future use cases could come and design the data model according to that.

282

Does this look good or do you have any?

283

Yeah, it looks good.

284

I think we are on time.

285

Yeah, let's move on to the next section okay uh

286

so given the data model

287

and uh we would also be of course the ultimate goal is the query capability right um

288

so given the case first is the conversion rate driver conversion rate we also we want to see like uh

289

and given um from the sign of like how much time

290

they are actually taking uh uh from sign up to activation rate right

291

so in this case we can simply go ahead

292

and refer to the driver table and actually do a check where in from the signup,

293

how much time they are actually taking to basically...

294

So this is a high level logic that I'm giving.

295

It's kind of a solo code.

296

Yeah, okay and then this I can call the time actually I can call it the BGC

297

BGC rate because

298

if it is taking more than 10 -15 days then of

299

course there is something we would want to look at in the same way sign up to

300

of course like dogs approval because these things can happen in parallel dogs approval

301

and same way we can call it as talk

302

wait then eventually yes sign up to actual activation time right activation time

303

and then it could be activation rate.

304

This will actually give us the high -level overview of how the sign up to activation rate looks like.

305

Then also, Then we would also want to look at the TRIP request to completion rate.

306

In this case, I think I would like to look at two entities at least.

307

That is the user, offer, and then another third entity,

308

which is the TRIP because we want to see how many offers are actually getting converted to TRIP right?

309

Yeah.

310

Yeah.

311

So second form first trip because no,

312

actually the offer then left will be kind

313

of left join with trip right offer dot trip uuid

314

which is true um trip dot trip uuid

315

and then of course i would also want to join trip with the user to avoid any blacklisted users right

316

Then we should directly have left train user.

317

where the status of the user is actually active,

318

essentially, the whole idea is that we want to avoid blacklisted ones, because then it could actually go ahead and impact our metrics.

319

and then we want to see by the user, right?

320

Then we will have our key as user UUID, which will come.

321

Okay.

322

Actually, no, the user UUID should come from the for itself.

323

And then, oh, I see.

324

Sorry for the miss from my side.

325

It should be the user UID also here because that is the person who is making the request here.

326

So offer .userUID, and then maybe account of offer.

327

Maybe I just want to put a case where there is when trip .uservil .id is not null.

328

If not, now only then,

329

then trip .userUID, else should end and then give me those count only.

330

And then, okay, okay.

331

If I just simply want a percentage, I think I would rather expect to put the offer in the denominator

332

and then this could actually give me a percentage of

333

of the given user what is the percentage of offers to

334

trip conversions yep that's good I think the left joint does the trick here

335

when you have the trip ID so

336

when the offer is not converted into the trip then the

337

trip you are not populating the trip UID in the offer table

338

so that helps you in this college that's good uh i think i think this one we can extend even

339

to not specific to user id uh if we want to uh get metrics of how many offers overall were converted

340

into uh trips and then how many were not that is another use case that we can do yeah correct

341

yes make sense yeah that's good and like say uh

342

if uh just yeah just um just sort of curiosity

343

if you want to

344

if i want to say for example uh you have uh you are you're getting offered

345

and trips um and then i would want to include some uh geo which which i think your the your offer table

346

currently do not have or maybe

347

or maybe i'm missed it uh do you have an option to for example from the location wise

348

if we want to find out how many offers we have got from each location

349

or which has uh got a higher number of trip conversion

350

that is also a use case

351

that we can look at for profitability yeah yeah it's actually something

352

that has been missed here there should be another entity which

353

which i'm actually realized that in between the interview itself

354

that there should be a location entity as well wherein each of the trip can be tied to a location

355

and then based on the geolocation also we can make where the density of the trips is higher

356

and where the opportunity earning opportunities are higher yes of course

357

that should be that be really helpful

358

if you can include the location entities amazing that's good that's

359

good i think this is a good time to pause uh probably um uh

360

if you could share you know given if you

361

if you are an interviewer and then if you could share what went well

362

and what would have would you have done differently

363

so i think what went well was that uh in the start itself yourself we clarified the exact requirements, right?

364

And once we had clarified the requirements,

365

that played the biggest role in helping us design the data model itself because once you know the use case,

366

everything eventually falls right into place because from the use case itself, we were able to identify that we are looking at driver entity, we are looking at trips,

367

We are also looking at users, right?

368

And then if it's a request, then, of course, offers.

369

So I think from the start itself, we had the clarity on however data model will fall into place given the requirements.

370

I think that went well uh

371

and also what what i really like in the interview is

372

that it it is always a collaborative effort uh between the interviewer

373

and the candidate itself which i i think

374

which went well between us since you were always available for the feedbacks

375

and i think

376

which since the feedback was always on time i was able

377

to work on my data model better what i could have improved on

378

is something yeah which which was later called out by you is

379

that a geolocation is actually one of the very key factors

380

when we come to the ride sharing app

381

and i think even though it was not mentioned in the

382

use cases uh this is one of the common asks

383

that once we have clarified the requirements that location is something

384

that which can be looked on next i think we could have foreseen

385

that requirement uh

386

and then build my data model on top of it accordingly

387

you did really well i mean i think um uh the thing uh

388

that stand that stood out is like we're able to uh take the use cases scope it out

389

and then like the icing on the cake was you finished

390

out with the query as well to exemplify how the analytics look like thank you

391

so much for joining us Kajal

392

and thank you everyone to everyone watching until next time good luck on your next interview and thanks for watching

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אוצר מילים והערות הגייה לשיעור הזה

שיעור הדיבור הזה ברמה C1 מבוסס על הסרטון “Uber Data Engineering Mock Interview - Ride-Sharing Data Warehouse Schema”. המילים שחוזרות הכי הרבה: trip, user, driver, offer, rating. בסרטון הזה יש 392 משפטים ו-4784 מילים לתרגול Shadowing. הדיבור נמשך 37:17. הדובר שומר על קצב יציב של כ-128 מילים בדקה, קצב נוח לתרגול Shadowing. 86% מהמילים נמצאות בין 3,000 המילים הנפוצות ביותר באנגלית; כדאי ללמוד את השאר לפני שמתחילים.

אוצר מילים מרכזי בסרטון הזה

15 המילים המתקדמות ביותר בסרטון, עם ההגייה והמשמעות שלהן:

מילה	הגייה	משמעות
dimension שם עצם	/daɪˈmɛn.ʃən/	מֵמַד
rider שם עצם	/ˈɹaɪ̯.dəː/	רוֹכֵב
blacklist שם עצם	/ˈblæklɪst/	רשימה שחורה
metric שם תואר	/ˈmɛt.ɹɪk/	מֶטְרִי
clarify פועל	/ˈklæɹ.ɪ.faɪ/	הבהיר
foresee פועל	/fɔɹˈsi/	צִפָּה
incentive שם עצם	/ɪnˈsɛntɪv/	תמריץ
denominator שם עצם	/dɪˈnɒmɪneɪtə(ɹ)/	מְכַנֶה
similarity שם עצם	/ˌsɪm.ɪˈlæɹ.ɪ.ti/	דִּמְיוֹן
viewer שם עצם	/ˈvju.ɚ/	צוֹפֶה
mock פועל	/mɑk/	סנט
maple שם עצם	/ˈmeɪpl̩/	אדר
leverage שם עצם	/ˈlɛv.(ə.)ɹɪd͡ʒ/	הנפה
clarity שם עצם	/ˈklæɹ.ɪ.ti/	בהירות
demographic שם תואר	/ˌdɛm.əˈɡɹæf.ɪk/	דֵּמוֹגְרָפִי

Phrasal verbs שתשמעו

מילה	משמעות
check out פועל	בדק
come back פועל	שָׁב
go down פועל	ירד
go out פועל	יָצָא

משפטים שכדאי לחזור עליהם

משפטים קצרים ושלמים מהסרטון שאפשר להשתמש בהם בשיחה יומיומית:

Would you take a moment to introduce yourself to our viewers?
Yes, let's tackle it by one.
Does this look good or do you have any?

דקדוק בסרטון הזה

המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:

מבנה	בסרטון
הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו	has actually signed · have expired · have discussed
סביל (Passive voice) be + past participle — הדגש הוא על מה שקורה, לא על מי שעושה את הפעולה	will be updated · is converted · be converted
משפטי זיקה (Relative clauses) who / which + clause — מידע נוסף על אדם או דבר	today, which is · keep which will · uid which will

הגייה ששווה לשים לב אליה

הדובר משתמש ב-23 קיצורים וצורות מקוצרות, כמו I'm, don't, I've. אמרו אותם בצורה הקצרה, כפי שאתם שומעים אותם.

הצלילים של “sh” ו-“zh”: activation /ˌæktəˈveɪʃən/, dimension /daɪˈmɛn.ʃən/, optimization /ˌɑptəmaɪˈzeɪʃən/, documentation /ˌdɑkjəmɛnˈteɪʃən/, differentiator /dɪf.əˈɹɛn.ʃi.eɪ.tə/
מילים ארוכות — שימו לב להטעמה: activation /ˌæktəˈveɪʃən/, optimization /ˌɑptəmaɪˈzeɪʃən/, analytics /ˌæn.əˈlɪt.ɪks/, documentation /ˌdɑkjəmɛnˈteɪʃən/, denominator /dɪˈnɒmɪneɪtə(ɹ)/

צלילים שדוברי עברית מתקשים בהם:

/w/ — שפתיים מעוגלות, לא /v/: wherein /wɛˈɹɪn/, query /ˈkwɪɹ.i/, whatsoever /ˌ(h)wʌtsoʊˈɛvɚ/, warehouse /ˈwɛə(ɹ)haʊs/
/æ/ — פתוח יותר מה-“אֶ” העברית, לא כמו “a” ב-“cup”: activation /ˌæktəˈveɪʃən/, blacklist /ˈblæklɪst/, timestamp /ˈtaɪmˌstæmp/, clarify /ˈklæɹ.ɪ.faɪ/, analytics /ˌæn.əˈlɪt.ɪks/
/r/ — הלשון לא נוגעת בשום מקום, לא ה-“ר” הגרונית: wherein /wɛˈɹɪn/, rider /ˈɹaɪ̯.dəː/, query /ˈkwɪɹ.i/, metric /ˈmɛt.ɹɪk/, clarify /ˈklæɹ.ɪ.faɪ/

איך לתרגל עם הסרטון הזה

הקשיבו לכל הסרטון פעם אחת בלי לדבר, ורשמו את המילים שאתם לא מכירים.
תרגלו Shadowing משפט אחר משפט במהירות רגילה, וחזרו על כל משפט עד שהקצב שלכם תואם את הדובר.
הקליטו את עצמכם והשוו למקור, תוך תשומת לב למילים כמו dimension, rider, blacklist.

מהי טכניקת השאדואינג?

שאדואינג היא טכניקת למידת שפה מבוססת מדע שפותחה במקור להכשרת מתורגמנים מקצועיים ופופולרית על ידי הפוליגלוט ד"ר אלכסנדר ארגואלס. השיטה פשוטה אך עוצמתית: אתה מקשיב לאודיו באנגלית ילידית וחוזר עליו בקול רם מיד — כמו צל העוקב אחרי הדובר עם עיכוב של 1–2 שניות בלבד. בניגוד להאזנה פסיבית או תרגילי דקדוק, שאדואינג מכריח את המוח והשרירים בפה לעבד ולשחזר דפוסי דיבור אמיתיים בו זמנית. מחקרים מראים שזה משפר משמעותית את דיוק ההגייה, האינטונציה, הקצב, הדיבור המחובר, הבנת השמיעה ושטף הדיבור — מה שהופך אותו לאחת השיטות היעילות ביותר להכנה לIELTS Speaking ולתקשורת באנגלית בעולם האמיתי.

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אוצר מילים והערות הגייה לשיעור הזה

אוצר מילים מרכזי בסרטון הזה

Phrasal verbs שתשמעו

משפטים שכדאי לחזור עליהם

דקדוק בסרטון הזה

הגייה ששווה לשים לב אליה

איך לתרגל עם הסרטון הזה

מהי טכניקת השאדואינג?