تدريب Shadowing: Netflix product manager mock interview and tips (with ex-Netflix Lead PM) - تعلم التحدث بالإنجليزية عبر الفيديو

اختر وضع التدريب

كل وضع يفتح غرفة تدريب بملء الشاشة.

405 جمل

إذا كانت الجمل قصيرة أو طويلة جدًا، انقر على Edit لتعديلها.

Hello everyone, welcome to this Netflix product manager interview walkthrough.

We have Michael here with us today.

He was an ex Netflix PM.

Thanks for joining us Michael.

Thanks for having me Tom.

I'm honored to be here.

Yeah, it's great to have you on.

Before we get started, do you want to just quickly give people an overview of your experience as a PM?

Absolutely.

Yeah.

So I've been a product senior product manager at Netflix and Meta and Roku,

where I've kind of owned and scaled these customer -facing solutions across web and mobile and hardware device ecosystems as well.

I think my work has focused more on driving measurable impact

and leading these large -scale platform transformations

and overall kind of improving the customer experience as I launched products at all these companies.

Great.

And yeah, obviously now you're a coach on I Got an Offer.

and until recently you were running PM interviews at Netflix, right?

So what better person to show us how to give a really strong answer to a typical Netflix question?

I've got a Product Sense question for you today.

So yeah, when you're ready, you can start showing people exactly how to tackle this question.

Let's do it.

Let's dive in.

Okay.

It's a very typical one.

Michael, how would you improve Netflix's recommendations?

Oh, wow.

This is, I think this is one of the most common questions in PM interviews.

And a lot of candidates kind of jump straight into, you know, solutioning of this, right?

They get excited about creating new solutions like, oh, I would build better filters.

I would create AI recommendations.

And that's not exactly what interviewers and hiring managers are looking for when they ask a question like this.

What we're really trying to understand is, you know, your thought process and your ability to comfortably work through, you know, ambiguity.

Right.

This is such a vague question.

But today I'm going to show you exactly how top candidates are able to structure their answer step by step.

So you can kind of stand out from your competition and hopefully leave that lasting impression to the interviewer.

So for me, I think what makes my response a little bit different is

we're not just going to focus on the typical product management frameworks, which I think you guys are all kind of familiar with.

I want to kind of incorporate this human centered design thinking.

So your answer doesn't just sound smart, but it actually sounds like you sat with the people that you're designing this for.

And that combination of that structure and rigor

and empathy is kind of what separates a good PM candidate from a great one.

So the first thing I would do before I say a single word about Netflix, I want to pause and kind of ask a couple of focused questions.

Right.

I think this for the interviewer signals a sense of PM maturity.

It shows that, you know, you're not going to rush into solutions.

So I would say like, so Tom, before we dive in is, I would love to kind of clarify a few things so

that I'm making sure that we're solving for the right problem here.

When we say improved recommendations, are we focused on a specific goal?

Are we trying to reduce churn

or are we trying to increase the session time for users

or you know improve content discovery for a particular audience yeah

i think we're trying to trying to improve all three of those got it got it

that makes sense okay um is there a specific platform

or device type that we're prioritizing whether it's for mobile users or tv

or are we thinking about all platforms all platforms got it okay um lastly are there any constraints

that i should keep in mind in terms of like budget

or timeline or specific regions yeah you have a tight budget

and you have a tight timeline okay that helps a little bit

so notice these answers tom's responses were very very vague still

pretty ambiguous um tom i'd like to take some time to

kind of think about my solution here um before we kind of move on

step two is kind of anchoring yourself right right now we still have this ambiguity

but it's it helps to kind of anchor yourself um with

the company mission before you begin to brainstorm i think top

candidates you'll notice frame their answers within the company's either mission

or North Star or any initiative that the company may be running.

So an example of this, I would say, great.

So before I jump into solutions, let me quickly ground us on Netflix's vision and goals.

I think any improvement needs to ultimately align.

back with those.

Netflix's mission, as I understand, is to entertain the world, right?

Their business model is subscription -based, which means ultimately I think their north star is around retention, right?

Making sure that subscribers are engaged and enjoy what they watch so they don't consider canceling their subscription.

So when I think about this recommendation system,

I think this is probably the most important or one of the most important product levers that Netflix has, right?

I think they've even said publicly that poor recommendations have this direct impact on subscriber churn.

So Netflix improving recommendations, I think the feature itself, I wouldn't say is broken by any means,

but I think currently the users might be facing more of a, decision problem, right?

I think it's not necessarily optimized for how customers are choosing what to watch.

So I think the real question

that we need to ask ourselves is how can we help users find what they want to watch faster

and feel more satisfied with what they chose, which would lead to them kind of staying subscribed longer.

So I think the goal here is to help users confidently decide what to watch as quickly as possible.

So here, notice I kind of quoted the company mission and initiative.

It was pretty vague, right?

It was pretty rough.

But I think even this kind of shows the interviewer your level of business acumen and your preparation, right?

You don't need to be exact.

You don't need like specific details but I think what matters more is showing

that you understand the business as well as the product.

So now that we've done that, I would kind of narrow this down a little more, right?

So I would break this up into the user segmentation.

Here I would, you know, identify our user groups and then make a deliberate choice,

justified choice, on who to focus on for this interview.

Because right now, it's still a very large scope.

I think a key to defining this user segment is to really bring them to life.

Once you have identified a segment of users, bring them to life with a short narrative.

100

And I think that's the empathy layer that really transforms a competent answer into something really memorable for the interviewers as well.

101

So I'll give you an example.

102

Okay, so let me think about who actually uses Netflix recommendations.

103

I'm going to segment out the users to make sure that I'm solving for the right person here.

104

When I think about Netflix and the users, obviously there's a solo binge watchers, right?

105

These are generally people who watch alone.

106

They know what genres they like,

107

but sometimes they may struggle to find a new series or new show once they've finished what they really like.

108

So the recommendations here is helping them find new shows to watch once they finish the series.

109

We also have family viewers who want to count.

110

They could have multiple profiles, but generally they're spending time watching together.

111

for them i think it's more the recommendations are more around

112

what everyone will enjoy right i think this is always a challenge everyone in the family has a different taste

113

so it's kind of a challenge for them to find what

114

they want to watch we also have casual browsers people who

115

are subscribed they don't watch a whole lot they may open netflix

116

and scroll for 10 to 20 minutes and then they can't decide

117

and then they just close the app

118

so i think the casual browsers is our biggest churn risk

119

that we have um and then lastly i think about lapsed users people who were once active

120

but then they haven't watched in a while so the recommendations

121

that are surfaced to them may feel a little irrelevant or stale

122

because their tastes may have changed um for the purpose of

123

today's interview i want to focus on the casual browser because i think this user demographic represents our highest churn risk.

124

I think they're already on the platform, so their acquisition cost is sunk.

125

And improving their recommendation and discovery experience, I think, has a direct and measurable impact on retention.

126

And the ROI potential is the highest of.. any of these segments.

127

So let me bring this casual user to life because I think it helps to understand who we're solving for.

128

So imagine this.

129

Let's say it's a weeknight.

130

Let's say it's a Wednesday night, 9 .30.

131

Our user, I'll name her Maya, she just put her kids to bed and she has like 45 minutes before she needs to sleep.

132

So she opens Netflix.

133

and she doesn't want anything heavy at the moment.

134

She's pretty tired, middle of the week, but also doesn't want to commit to the first episode of a new series

135

if she's not sure she's going to be able to finish it, right?

136

So she scrolls and scrolls more.

137

She watches some trailers, reads some descriptions, and backs out, tries another row, same thing.

138

Nothing really feels quite right at the moment.

139

So before she knows it, 20 minutes go by.

140

And then she decides, oh, I can't, I can't decide.

141

I'm just going to hop over to YouTube.

142

That moment, that forget it moment for Maya, I think that's the moment that we need to be designing against.

143

So Maya isn't broken, right?

144

She, for her, the product isn't understanding her.

145

So the product experience at that moment for her is what's broken.

146

So here, when I was defining all the user segments, notice how I named this user Maya, and it really helped them kind of come to life, hopefully.

147

Interviewers are humans too, right?

148

I think we've had that same experience.

149

I've had this experience yesterday.

150

And the moment we recognize ourselves in your narrative,

151

I think this is where you make the emotional case for why this problem actually matters, right?

152

It's not just the logical one.

153

So it's really important to frame this demographic and bring this person to life.

154

This shows that you're really thinking about the user experience and you're thinking not just about the user, but also the company outcomes as well.

155

So I think that's the key thing to highlight there.

156

From there, I'll move on to some of the pain points.

157

I'll frame the pain points through the lens of our user Maya. For someone like Maya,

158

she has this limited amount of time right she wants to

159

it's the end of the day she is exhausted she wants

160

to switch off she wants to mentally just switch off feel entertained

161

so she can go to bed feeling like she's had this moment for herself right so with

162

that in mind i think this is where um her pain

163

points lie i think this is where the product failed her

164

the first thing obviously it's decision fatigue right the home page right now it shows

165

so many titles with dozens of rows

166

and for someone like her who came to the product to kind of relax

167

and unwind these choices often creates this decision paralysis right i

168

think it's designed currently for someone who has a little more bandwidth

169

and not someone with this limited amount of time the second pain point i think is around situational context blindness.

170

I think Netflix recommends kind of the same content regardless of time or your mood or energy level.

171

So if you think about Maya, this 9 .30 p .m.

172

Maya might have completely different needs than like a Saturday afternoon Maya, but the product itself doesn't really know that difference.

173

A third pain point, I think there's no way for her to express what she wants at that moment, right?

174

There's no way for her to express her intent.

175

She can't like talk to Netflix and say, hey, Netflix, show me something funny that won't make me think.

176

She can only kind of scroll and hope that the algorithm infers what she wants.

177

So there's no way for her to be heard.

178

And then lastly, I think there's also this content uncertainty, right?

179

I think even if Maya stumbles upon something that may be a good match for her,

180

there aren't any real signals that help her feel confident that she made the right choice.

181

So she keeps on browsing.

182

So all of these factors are actually what drives this like 20 minute through scrolling and abandonment behavior.

183

If I had to rank these, I think decision fatigue and situational context blindness do the most damage when we think about retention and churn.

184

I think both of these are direct causes of that.

185

scroll and abandon behavior, which ultimately puts our subscription at risk.

186

So here I kind of showed the interviewer that, you know, I didn't just list out my problems,

187

but I also picked out the ones that I wanted to build against, right?

188

I think this helps you kind of narrow down your solution path a little bit.

189

And for such an ambiguous question like this, pain points are kind of communicated best when you articulate them through the lens of your user.

190

It kind of shows the interviewer

191

that your solution is going to be grounded in a real user need and not just product assumptions.

192

Okay, from there, I would kind of move on to the fun part, which is the solutioning.

193

I call this the fun part because there's no real right or wrong answer.

194

But this is where you get to ideate your creative solutions.

195

You're coming up with these on the fly,

196

but it's important to kind of lead with that human experience first instead of just describing the features.

197

I would kind of encourage you to describe the moment of experience that it creates for the user.

198

Right.

199

And then you can kind of go into how it works under the hood

200

and make sure your solutions vary from across a spectrum of complexity levels.

201

So we'll take it from there.

202

Okay, so I thought about some ideas and came up with a few different solutions.

203

I think the first one I would call a mood prompt

204

so imagine Maya opens Netflix

205

and instead of these dozens of rows she just sees a single you know warm question

206

that asks like hey what are you feeling tonight with some simple responses of like I'm feeling something light -hearted

207

or something frightening or something funny and she just taps on light -hearted

208

and immediately the entire interface can kind of reorganize itself around that specific intent.

209

So she taps into one show and then she can begin watching.

210

So here, this, I think, can transform this whole browsing experience into more of a guided decision -making process for Maya.

211

Under the hood, I would say this is a pretty strong indicator for us.

212

I think it gives Netflix a real -time intent signal for that session.

213

and it doesn't have to be a permanent change to her long -term profile at all, but it's just for that session duration.

214

I think this is a quick win for Netflix because it's a relatively low complexity build from what I'm thinking.

215

There's no new machine learning that's needed.

216

All we're doing is just adding a new UI prompt and passing this parameter into the existing ranking API model.

217

I think Netflix already kind of tags all of their titles by genre and tone and content duration.

218

So all we're doing is kind of surfacing that up, surfacing those tags to the user for the first time.

219

I think the heavier lift will be on the UI side, the UX UI side to make sure the prompt feels kind of warm

220

and not like a mandatory form that they have to go through.

221

A second idea that I had was creating a time -based filter.

222

So Netflix already has content duration length for all of the titles.

223

And I think adding this filter, which is a time selector, would kind of filter the catalog accordingly.

224

So there's no model changes here, no new infrastructure.

225

And the backend filters by runtime, which already exists for every title.

226

So it's simple, it's fast, and I would say immediately useful for time -constrained users like Maya.

227

A third solution that I thought of was something I would call social signal.

228

So this is kind of tapping into human behavior, a human motivator, right?

229

Which is if you think about our desire to do what other people like us are doing.

230

I think this would be more of a personalized signal based

231

on watch histories of other users who have a similar watch history as Maya

232

so we could surface up some simple one -liners um

233

so for maya she could see like oh hey 92 percent of users like you finished this show

234

or this is trending amongst parents so now she's not guessing anymore

235

so to speak and she feels a lot more confident with you know the the recommendations

236

that are giving to her and i think Thank you.

237

that's the difference between recommendation and just raw popularity.

238

In terms of engineering complexity, I think this one is obviously a lot more extensive.

239

We need to build out this people like you model,

240

cohort model, and kind of cluster the users based on taste and generate these social signals that way,

241

not just based on the raw popularity of the show, which already exists.

242

So I think this would be a longer term kind of solution, but it could also prove to be very valuable for us.

243

Lastly, another kind of lower effort item that I thought of was just creating this dislike signal.

244

Netflix already has the thumbs up, thumbs down system, which lives kind of a couple layers deep on the content detail page.

245

But I think if we surface this up, this could be pretty powerful for casual browsers like Maya who don't have a rich watch history.

246

I think these negative signals, so Maya could click on this is not for me or thumbs down.

247

And then the algorithm would kind of improve instantly for her.

248

I think for her, users like Maya who, you know, Netflix can't really, she can't teach Netflix what she loves if she never really finishes anything

249

but if she is able to click a this is not for me I don't like this

250

she can absolutely this is kind of a shortcut to the algorithm right it gives her the feeling

251

that she's also kind of being her so from here I would kind of prioritize I provided some solutions

252

you'll notice that you know there wasn't like a moonshot crazy solution

253

that was out of this world I kept my examples kind of, I guess, realistic.

254

Not that creative, honestly.

255

But from here, you can narrow down your choices, right?

256

And come up with a clear rationale behind it.

257

You can connect the story directly back to Maya's pain points and her business and our business goals.

258

But don't just say what you will build.

259

You also need to state what you would want to validate first.

260

And bonus points if you're, you know, amazingly creative and able to kind of figure out a way to build out a roadmap with all of your solutions.

261

So I'll kind of provide that example for us. So four solutions.

262

Let me tell you what I would build first and why.

263

So I would kind of think about this in a couple different phases.

264

for phase one I would want to start with our first solution

265

which was the mood prompt and immediately follow

266

that up with our second solution

267

which was time -based filtering I think both of these are relatively low complexity items

268

and they both directly attack Maya's biggest pain points

269

which were decision fatigue and situational context blindness right I think it's a simple UI change with some lightweight API parameters.

270

It has a low engineering cost, which is fast to ship given our tight timeline and budget.

271

And it's easy to A -B test within a few weeks.

272

As a fast follow item, I would also want to build our fourth solution, which was the dislike signal.

273

I think we could test this in a short amount of time as well at almost no additional cost.

274

So for users like Maya who have sparse watch histories, I think these negative signals could provide a lot more value

275

and ultimately teach our algorithm faster than any positive signals ever could, right?

276

Solution three, which was the social proof concept, I would classify this as a phase two.

277

It definitely has a higher impact ceiling, but also comes with a high level of complexity and longer timelines.

278

So I would first want to validate that the data

279

that we receive from the signals from phase one are trending

280

the right direction as expected before we kind of invest into this cohort bottling phase.

281

So I think taken together, this whole roadmap moves from,

282

you know, helping casual users narrow down their recommendations and decision time to really helping them trust the recommendations.

283

And I think that progression maps directly to the stages of Maya's frustrations.

284

So here I presented the four solutions and I prioritized them into a phased roadmap, which is a pretty senior product manager move.

285

I think it shows that you can brainstorm and think sequentially and not just pick a favorite, right?

286

Notice how the prioritization logic kind of mapped back to Maya

287

and her pain points and the engineering complexity all in one breath.

288

And I think that's what makes an answer kind of architecturally sound.

289

and not just improvised.

290

After that, I would go into the metrics.

291

I would lightly want to touch on some metrics to build out some success criteria.

292

So when I think about metrics, I think the obvious primary metric here is looking at the time to play,

293

which is the elapsed time from when a user opens Netflix to pressing play on a given title.

294

I think this is our most direct measure of whether we're solving Maya's problem

295

and our target should be to kind of move this time to play from our current baseline

296

which for casual users I'm assuming to be you know 10

297

to 20 minutes to something under five minutes within the first 60 days of launch.

298

I would also want to look at session completion rate, which is the percentage of users who use our new solutions, making sure they watch at least 70 % of a title.

299

I think this kind of tells us that we're not just getting users to press play faster,

300

but we're also matching them successfully with the content that they will actually enjoy.

301

Another metric, I think, is around churn data, right?

302

We need to make sure that even a one to two percentage reduction in cancellation rate,

303

I think this would be monumental for Netflix.

304

It's a huge, huge number if you think about Netflix's subscriber scale.

305

So those are kind of the metrics that I would kind of monitor.

306

Also, there are some guardrail metrics as well, things that we don't want to break, right?

307

There's obviously the content diversity index,

308

which kind of shows the number of unique genres of a user watches within the first within a 30 day period.

309

And if this drops significantly, then we'll know that users are kind of being funneled into their just comfort zone genre.

310

And that's going to hurt our long term engagement.

311

And then I would also think about the prompt skip rate right

312

if we see that over 60 of our users are just dismissing this prompt entirely i think

313

it's clear that it's just adding friction and not really adding much value

314

so i would rethink the ui of this so

315

if we're able to achieve all of our metrics target here

316

that would be a clear indicator for us to move from this a b test experiment to a full rollout

317

and hopefully a phase two approach so now

318

that we talked about some of the metrics um i also want to touch on some trade -offs here

319

so um you know phase one and phase two

320

obviously with the prompt this is

321

you know adding friction at session start right i think adding

322

this extra step can be pretty annoying for power users right who know exactly what they want to watch

323

i think a mitigation to this is to make sure

324

that it is optional and dismissible we want to make this feel like a a nudge

325

and not like a required form right another risk i would say is um i would call,

326

I guess, suppression of organic discovery.

327

So if this prompt continuously just narrows users into the same genres, it could ultimately hurt Netflix's product content offerings, right?

328

I think Netflix spends a ton of money on marketing their new content.

329

Um, if they're, but if users are just being funneled into their own comfort zone, I think this hurts our long -term engagement and the ability to surface new content.

330

Alongside of that, I think if we think about the dislike thumbs down button,

331

this could also lead to some false preference data.

332

So, you know, if we, you know, if users click this dislike button just so

333

that they could get rid of that content because they've already watched it, then it kind of incorrectly infers the user's taste preference.

334

So I think a mitigation to this is to kind of

335

add a already seen data point as a separate signal category in the back end.

336

For phase two, the social signal, I think building out this cohort of people like you,

337

it's pretty challenging to define well.

338

I think it takes several iteration.

339

And if this model is off, let's say Maya sees social signals from users who have completely different preferences, then this feature would ultimately backfire.

340

So I think...

341

you know it's it's especially vulnerable for new subscribers as well it could lead to some misleading percentages early on

342

so we need to be mindful of that

343

so here i named all of my solutions

344

and then i also came up with the risks and trade -offs

345

and mitigations so when you think about all of that this shows

346

that you kind of have that full lifespan of your proposed solutions And then at the end,

347

this is where I would kind of go into the whole summary, right?

348

I talked a lot.

349

So it's important to kind of recap on what I said and clean, don't trail off, and kind of invite dialogue.

350

So I'll say, hey, let's quickly summarize where I landed, right?

351

Throughout my thinking, I kind of try to stay grounded in Maya's point of view, her actual experience, and not so much what the algorithm does,

352

but what it really feels like to be her in a given moment.

353

I think I focused on the casual browser demographic because this segment had the biggest impact on retention,

354

which is ultimately the core business goal for a subscription model like Netflix.

355

The primary points that I wanted to solve, pain points, were around decision fatigue and situational context blindness,

356

both of which are direct causes of that scroll and abandoned behavior, which puts our subscriptions at risk.

357

So my recommended roadmap was to break this out into a phased approach with phase one, including the mood prompt, time -based filtering and dislike signal,

358

and phase two, building out this social signal indicator layer, once we've kind of proven that users responded well to our signals from phase one.

359

We also talked about, you know, the metrics in terms of tracking the time to play, session completion rates and churn rate deltas,

360

along with some guardrail metrics around content diversity and looking at the prompt skip rate.

361

So we also talked about the risks and trade -offs and mitigations around each.

362

But ultimately, I think the whole solution was really designed to reduce the effort

363

that Maya has to spend on what to watch and increase that feeling that Netflix truly understands her.

364

And that's ultimately what great recommendations should feel like, right?

365

It should be relevant and intuitive.

366

Does that framing kind of make sense?

367

And I'm happy to go in further on any of the solutions.

368

Wow, that's really helpful.

369

That makes so much sense.

370

I actually have a follow -up question for you.

371

Because you mentioned you kind of created a bit of a roadmap there.

372

So how would you get Netflix leadership to actually approve this roadmap?

373

Oh, yeah.

374

That is a very important question.

375

I think this is a stakeholder management and influence kind of discussion.

376

I would frame this as a business case around Netflix,

377

the metric that Netflix cares about the most, subscriber retention.

378

I would lead with the problem size.

379

How many casual browser sessions are ending without a play today?

380

And what percentage of these users churn within 30 days?

381

If we could get an estimate or a rough dollar value on reducing that churn rate by one to two percentage points,

382

I think the investment in a phase one, which is a fast, low risk experiment becomes an obvious yes for leadership.

383

And they would more than likely approve a six week pilot

384

with a clear success plan than showing them a three quarter roadmap.

385

So I would let kind of the data from phase one make the cause for phase two and beyond.

386

Okay, great.

387

Great answer.

388

Okay, Michael, I think that brings us to the end of that interview and interview walkthrough.

389

That was really useful, I think, for people watching.

390

They're going to get loads out of that.

391

And obviously, yeah, whether they're interviewing at Netflix or Meta, Google, any of these big, big top companies that have product sense interviews,

392

I think that's going to be massively helpful. So amazing.

393

Yeah.

394

Thanks so much.

395

Yeah.

396

Thanks for having me.

397

Yeah.

398

No, we'd love to love to have you back on soon, if you will.

399

Absolutely.

400

Yeah.

401

Remember, yeah, please like and subscribe if you found that useful.

402

And obviously you can book a session with Michael if you have interviews coming up and you want some expert feedback.

403

Cool.

404

Thanks.

405

If you want to get help in your career from someone who's done it at the very top, go to igotanoffer .com, choose your coach and book a session.

تنزيل التطبيق

كل ما تحتاجه للتحدث بطلاقة

تقييم النطق بالذكاء الاصطناعيتقييم كل جملة

تدريب IPAأتقن كل صوت

المفرداتابنِ رصيدك من الكلمات

لعبة المفرداتتعلّم أثناء اللعب

امسح رمز QR

Download on theApp Store Get it onGoogle Play

نفس القناة

Google product manager behavioral interview (Top 5 questions)

Interview EnglishUniversity StudentsWorking Adults

FOR YOU

فيديو موصى به

113 جمل

SUPPLY CHAIN MANAGER Interview Questions & Answers! HOW TO PASS a Supply Chain Management Interview!

Interview EnglishWorking Adults

75 جمل

The unfair way I got good at Leetcode

Interview EnglishUniversity StudentsWorking Adults

32 جمل

Senior Leadership Interview: Turning Around a Struggling Team

Business EnglishInterview EnglishWorking Adults

79 جمل

How AI Is Ruining Job Interviews

Interview EnglishWorking Adults

960 جمل

How to explain a project you workede on in 30 Minutes | Business English Learning

Business EnglishInterview EnglishWorking Adults

39 جمل

Nestle Interview Questions with Answer Examples

Interview EnglishWorking Adults

560 جمل

QA Engineer Interview Questions and Answers (UPDATED 2026) AI, Accessibility, Automation.

Interview EnglishWorking Adults

المفردات وملاحظات التحدث لهذا الدرس

يحتوي هذا الفيديو على 405 جملة و5234 كلمة للتدرب على الترديد. مدة الكلام 35:07. يتكلم المتحدث بسرعة ثابتة، نحو 149 كلمة في الدقيقة، وهي سرعة مريحة للترديد. 82% من الكلمات ضمن أكثر 3,000 كلمة شيوعًا في الإنجليزية، ويُستحسن مراجعة الباقي قبل البدء.

أهم المفردات في هذا الفيديو

15 كلمة أقل شيوعًا من الفيديو، مع النطق والمعنى:

recommendation /ˌɹɛkəmɛnˈdeɪʃən/ (اسم) — تَوْصِيَة. An act of recommending.
filter /ˈfɪltɚ/ (اسم) — مِصْفَايَة. A device which separates a suspended, dissolved, or particulate matter from a fluid, solution, or other substance; any device that separates one substance from…
complexity /kəmˈplɛk.sɪ.ti/ (اسم) — تَعْقِيد. The state of being complex; intricacy; entanglement.
solve /sɒlv/ (فعل) — ذَابَ, حَلَّ. To find an answer or solution to a problem or question; to work out.
genre /ˈ(d)ʒɑnɹə/ (اسم) — نَوْع فَنِّيّ, نَوْع أَدَبِيّ. A kind; a stylistic category or sort, especially of literature or other artworks.
algorithm /ˈælɡəɹɪðm̩/ (اسم) — خَوَارِزْمِيَّة. A collection of ordered steps that solve a mathematical problem. A precise step-by-step plan for a computational procedure that possibly begins with an input…
dislike /dɪsˈlaɪk/ (فعل) — كَرِهَ. To have a feeling of aversion or antipathy towards; not to like.
retention /ɹɪˈtɛnʃən/ (اسم) — حِفْظ. The act of retaining or something retained.
percentage /pə(ɹ)ˈsɛntɪd͡ʒ/ (اسم) — النِّسْبَة الْمِئَوِيَّة. The amount, number or rate of something, regarded as part of a total of 100; a part of a whole.
mood /muːd/ (اسم) — مَزَاج. A mental or emotional state, composure.
narrow /ˈnæɹəʊ/ (صفة) — ضَيِّق. Having a small width; not wide; having opposite edges or sides that are close, especially by comparison to length or depth.
subscription /səbˈskɹɪpʃən/ (اسم) — اِشْتِرَاك. Access to a resource for a period of time, generally for payment.
thumb /ˈθʌm/ (اسم) — إِبْهَام. The shortest and thickest digit of the hand that for humans has the most mobility and can be made to oppose (moved to touch) all of the other fingers.
fatigue /fəˈtiːɡ/ (اسم) — إِرْهَاق, تَعَب. A weariness caused by exertion; exhaustion.
scroll /skɹoʊl/ (اسم) — طُومَار. A roll of paper or parchment; a writing formed into a roll.

الأفعال المركبة التي ستسمعها

come up with (فعل) — جَاءَ بِـ, أَتَى بِـ. To manage to produce, deliver, or present (something) by inventing, creating, thinking of, or obtaining it.
look at (فعل) — نَظَرَ. To observe or watch.
come up (فعل) — طَلَعَ. To emerge or become known, especially unexpectedly.
keep on (فعل) — يبقي. To persist or continue.
look for (فعل) — بَحَثَ, فَتَّشَ. To seek.

نطق يحتاج إلى انتباه

يستخدم المتحدث 85 من الصيغ المختصرة، مثل we're, I'm, don't. انطقها بصيغتها القصيرة كما تسمعها.

أصوات “th”: algorithm /ˈælɡəɹɪðm̩/, thumb /ˈθʌm/, empathy /ˈɛmpəθi/
أصوات “sh” و“zh”: recommendation /ˌɹɛkəmɛnˈdeɪʃən/, casual /ˈkæʒ(u)əl/, genre /ˈ(d)ʒɑnɹə/, retention /ɹɪˈtɛnʃən/, subscription /səbˈskɹɪpʃən/
الكلمات الطويلة — انتبه لموضع النبر: recommendation /ˌɹɛkəmɛnˈdeɪʃən/, complexity /kəmˈplɛk.sɪ.ti/, demographic /ˌdɛm.əˈɡɹæf.ɪk/, indicator /ˈɪn.dɪˌkeɪ.tə(ɹ)/, experiment /ɪkˈspɛɹ.ə.mənt/

كيف تتدرب بهذا الفيديو

استمع إلى الفيديو كاملًا مرة واحدة دون أن تتكلم، ودوّن الكلمات التي لا تعرفها.
ردّد جملة جملة بالسرعة العادية، وكرّر كل جملة حتى يتطابق إيقاعك مع المتحدث.
سجّل صوتك وقارنه بالأصل، مع الانتباه إلى كلمات مثل recommendation, filter, complexity.

القواعد في هذا الفيديو

أكثر التراكيب التي يستخدمها المتحدث، مع العبارات نفسها من الفيديو:

التركيب	في الفيديو
المضارع التام have/has + اسم المفعول — حدث ماضٍ ما زال له أثر الآن	I've been · has focused · they've even said
المبني للمجهول be + اسم المفعول — التركيز على ما يحدث لا على من يفعله	is broken · are subscribed · are surfaced
جمل الوصل who / which + جملة — معلومة إضافية عن شخص أو شيء	based, which means · people who watch · people who were

ما هي تقنية التظليل الصوتي؟

التظليل الصوتي (Shadowing) تقنية تعلم لغة مدعومة علمياً، طُورت أصلاً لتدريب المترجمين الفوريين المحترفين. الطريقة بسيطة لكنها قوية: تستمع لصوت إنجليزي أصلي وتكرره فوراً بصوت عالٍ — كظل يتبع المتحدث بتأخير 1-2 ثانية. تُظهر الأبحاث تحسناً كبيراً في دقة النطق والتنغيم والإيقاع وربط الأصوات والاستماع والطلاقة.

تقنية الشادوينغ: اقرأ الدليل الكامل خطوة بخطوة →

تدريب Shadowing: Netflix product manager mock interview and tips (with ex-Netflix Lead PM) - تعلم التحدث بالإنجليزية عبر الفيديو

اختر وضع التدريب

تنزيل التطبيق

Google product manager behavioral interview (Top 5 questions)

Google Interview Tips That Actually Get You Hired

english interview p1

Walk Me Through Your Resume: How to Answer

Answering behavioral interview questions is shockingly uncomplicated

Ace every UX/UI design interview like THIS

SUPPLY CHAIN MANAGER Interview Questions & Answers! HOW TO PASS a Supply Chain Management Interview!

The unfair way I got good at Leetcode

Senior Leadership Interview: Turning Around a Struggling Team

How AI Is Ruining Job Interviews

How to explain a project you workede on in 30 Minutes | Business English Learning

Nestle Interview Questions with Answer Examples

QA Engineer Interview Questions and Answers (UPDATED 2026) AI, Accessibility, Automation.

المفردات وملاحظات التحدث لهذا الدرس

أهم المفردات في هذا الفيديو

الأفعال المركبة التي ستسمعها

نطق يحتاج إلى انتباه

كيف تتدرب بهذا الفيديو

القواعد في هذا الفيديو

ما هي تقنية التظليل الصوتي؟