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| Section | Objectives |
|---|---|
| Topic 1: ML pipeline automation and orchestration | - Pipeline design
|
| Topic 2: Designing ML solutions | - Framing ML problems
|
| Topic 3: Data preparation and processing | - Data ingestion and pipelines
|
| Topic 4: Deployment and operations | - Model deployment
|
| Topic 5: ML model development | - Evaluation
|
>> Professional-Machine-Learning-Engineer基礎問題集 <<
GoogleのProfessional-Machine-Learning-Engineer試験に受かることを通じて現在の激しい競争があるIT業種で昇進したくて、IT領域で専門的な技能を強化したいのなら、豊富なプロ知識と長年の努力が必要です。GoogleのProfessional-Machine-Learning-Engineer試験に受かるのはあなたが自分をIT業種にアピールする方法の一つです。でも、試験に合格するために大量な時間とエネルギーを費やすことはなく、JpshikenのGoogleのProfessional-Machine-Learning-Engineer試験トレーニング資料を選んだらいいです。Jpshikenのトレーニング資料はIT認証試験に受かるために特別に研究されたものですから、この資料を手に入れたら難しいGoogleのProfessional-Machine-Learning-Engineer認定試験に気楽に合格することができるようになります。
質問 # 320
Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?
正解：A
質問 # 321
You work for a telecommunications company. You're building a model to predict which customers may fail to pay their next phone bill. The purpose of this model is to proactively offer at-risk customers assistance such as service discounts and bill deadline extensions. The data is stored in BigQuery and the predictive features that are available for model training include:
- Customer_id
- Age
- Salary (measured in local currency)
- Sex
- Average bill value (measured in local currency)
- Number of phone calls in the last month (integer)
- Average duration of phone calls (measured in minutes)
You need to investigate and mitigate potential bias against disadvantaged groups, while preserving model accuracy.
What should you do?
正解：A
質問 # 322
You are working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?
正解：A
解説：
https://towardsdatascience.com/time-series-nested-cross-validation-76adba623eb9
質問 # 323
You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?
正解：B
解説：
AutoML Tables is a service that allows you to automatically build and deploy state-of-the-art machine learning models on structured data without writing code. You can use AutoML Tables to perform the following steps for the classification task:
Exploratory data analysis: AutoML Tables provides a graphical user interface (GUI) and a command-line interface (CLI) to explore your data, visualize statistics, and identify potential issues.
Feature selection: AutoML Tables automatically selects the most relevant features for your model based on the data schema and the target column. You can also manually exclude or include features, or create new features from existing ones using feature engineering.
Model building: AutoML Tables automatically builds and evaluates multiple machine learning models using different algorithms and architectures. You can also specify the optimization objective, the budget, and the evaluation metric for your model.
Training and hyperparameter tuning: AutoML Tables automatically trains and tunes your model using the best practices and techniques from Google's research and engineering teams. You can monitor the training progress and the performance of your model on the GUI or the CLI.
Serving: AutoML Tables automatically deploys your model to a fully managed, scalable, and secure environment. You can use the GUI or the CLI to request predictions from your model, either online (synchronously) or offline (asynchronously).
Reference:
[AutoML Tables documentation]
[AutoML Tables overview]
[AutoML Tables how-to guides]
質問 # 324
You recently developed a deep learning model using Keras, and now you are experimenting with different training strategies. First, you trained the model using a single GPU, but the training process was too slow.
Next, you distributed the training across 4 GPUs using tf.distribute.MirroredStrategy (with no other changes), but you did not observe a decrease in training time. What should you do?
正解：D
解説：
* Option A is incorrect because distributing the dataset with
tf.distribute.Strategy.experimental_distribute_dataset is not the most effective way to decrease the training time. This method allows you to distribute your dataset across multiple devices or machines, by creating a tf.data.Dataset instance that can be iterated over in parallel1. However, this option may not improve the training time significantly, as it does not change the amount of data or computation that each device or machine has to process. Moreover, this option may introduce additional overhead or complexity, as it requires you to handle the data sharding, replication, and synchronization across the devices or machines1.
* Option B is incorrect because creating a custom training loop is not the easiest way to decrease the training time. A custom training loop is a way to implement your own logic for training your model, by using low-level TensorFlow APIs, such as tf.GradientTape, tf.Variable, or tf.function2. A custom training loop may give you more flexibility and control over the training process, but it also requires more effort and expertise, as you have to write and debug the code for each step of the training loop, such as computing the gradients, applying the optimizer, or updating the metrics2. Moreover, a custom training loop may not improve the training time significantly, as it does not change the amount of data or computation that each device or machine has to process.
* Option C is incorrect because using a TPU with tf.distribute.TPUStrategy is not a valid way to decrease the training time. A TPU (Tensor Processing Unit) is a custom hardware accelerator designed for high-performance ML workloads3. A tf.distribute.TPUStrategy is a distribution strategy that allows you to distribute your training across multiple TPUs, by creating a tf.distribute.TPUStrategy instance that can be used with high-level TensorFlow APIs, such as Keras4. However, this option is not feasible, as Vertex AI Training does not support TPUs as accelerators for custom training jobs5. Moreover, this option may require significant code changes, as TPUs have different requirements and limitations than GPUs.
* Option D is correct because increasing the batch size is the best way to decrease the training time. The batch size is a hyperparameter that determines how many samples of data are processed in each iteration of the training loop. Increasing the batch size may reduce the training time, as it reduces the number of iterations needed to train the model, and it allows each device or machine to process more data in parallel. Increasing the batch size is also easy to implement, as it only requires changing a single
* hyperparameter. However, increasing the batch size may also affect the convergence and the accuracy of the model, so it is important to find the optimal batch size that balances the trade-off between the training time and the model performance.
References:
* tf.distribute.Strategy.experimental_distribute_dataset
* Custom training loop
* TPU overview
* tf.distribute.TPUStrategy
* Vertex AI Training accelerators
* [TPU programming model]
* [Batch size and learning rate]
* [Keras overview]
* [tf.distribute.MirroredStrategy]
* [Vertex AI Training overview]
* [TensorFlow overview]
質問 # 325
......
競争が常に激化している世界では、特定の分野で優れた能力と深い知識を所有することで、高い社会的地位を獲得し、社会での地位を確立することができます。JpshikenテストProfessional-Machine-Learning-Engineer認定に合格すると、目標を実現し、理想的な仕事を見つけるのに役立ちます。 Professional-Machine-Learning-Engineerの最新の質問を購入すると、Professional-Machine-Learning-Engineer試験に合格できます。 Professional-Machine-Learning-Engineer試験問題の無料デモを試してみてください。Professional-Machine-Learning-Engineer学習資料を気に入っていただけることでしょう。
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