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Google Professional-Machine-Learning-Engineer試験: Google Professional Machine Learning Engineer &認証の成功を保証,簡単なトレーニング方法

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今日では、柔軟な学習方法が電子製品の開発でますます一般的になっています。最新の技術は、同様に、我々はこの分野で最も主導的な地位にあることから、当社GoogleのProfessional-Machine-Learning-Engineer実際の試験に適用されています。また、あなたは私たちのProfessional-Machine-Learning-Engineer練習材料の3つのバージョンが存在するために多様な選択肢があります。同時に、Professional-Machine-Learning-Engineer試験に合格し、Professional-Machine-Learning-Engineer学習教材の有効性と正確性について希望のProfessional-Machine-Learning-Engineer認定を取得する必要があります。

Googleのプロフェッショナルマシンラーニングエンジニア認定試験は、機械学習分野の専門家のスキルを検証する包括的なテストです。認定試験は、Google Cloud Platformを使用してスケーラブルな機械学習モデルを設計、構築、展開する能力をテストするように設計されています。試験に合格した人は、Google Cloud Platformによって認められた証明書を受け取り、機械学習分野でのキャリアを進めるために使用することができます。

Google Professional Machine Learning Engineer認定は、業界で非常に尊敬されており、機械学習の卓越性のベンチマークとして認識されています。この認定を達成することは、雇用主と仲間に、候補者がGoogleクラウドプラットフォームに機械学習モデルを設計、構築、展開するために必要なスキルと知識を持っていることを示しています。この認定は、データサイエンティスト、機械学習エンジニア、ソフトウェアエンジニア、および機械学習のスキルを高め、この分野でのキャリアを促進しようとしている他の専門家に最適です。

この試験では、データの準備、モデル開発、モデルの展開、機械学習ソリューションの監視とメンテナンスなど、幅広いトピックをカバーしています。 Google Cloudを使用して機械学習ソリューションを設計、実装、および維持するために必要な知識とスキルをテストするように設計されています。この試験は、機械学習、データサイエンス、または関連分野に強いバックグラウンドを持ち、潜在的な雇用主に対する専門知識を実証しようとしている専門家を対象としています。

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Professional-Machine-Learning-Engineer関連受験参考書 & Professional-Machine-Learning-Engineer日本語版テキスト内容

Professional-Machine-Learning-Engineer試験はIT業界でのあなたにとって重要です。あなたはProfessional-Machine-Learning-Engineer試験に悩んでいますか？試験に合格できないことを心配していますか？我々の提供した一番新しくて全面的なGoogleのProfessional-Machine-Learning-Engineer問題集はあなたのすべての需要を満たすことができます。資格をもらうのはあなたの発展の第一歩で、我々のProfessional-Machine-Learning-Engineer日本語対策はあなたを助けて試験に合格して資格をもらうことができます。

Google Professional Machine Learning Engineer 認定 Professional-Machine-Learning-Engineer 試験問題 (Q17-Q22):

質問 # 17
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?

• A. Define a fairness metric that is represented by accuracy across the sensitive features Train a BigQuery ML boosted trees classification model with all features Use the trained model to make predictions on a test set Join the data back with the sensitive features, and calculate a fairness metric to investigate whether it meets your requirements.
• B. Determine whether there is a meaningful correlation between the sensitive features and the other features Train a BigQuery ML boosted trees classification model and exclude the sensitive features and any meaningfully correlated features
• C. Train a BigQuery ML boosted trees classification model with all features Use the ml. global explain method to calculate the global attribution values for each feature of the model If the feature importance value for any of the sensitive features exceeds a threshold, discard the model and tram without this feature
• D. Train a BigQuery ML boosted trees classification model with all features Use the ml. exflain_predict method to calculate the attribution values for each feature for each customer in a test set If for any individual customer the importance value for any feature exceeds a predefined threshold, discard the model and train the model again without this feature.

正解：B

質問 # 18
You work for a company that manages a ticketing platform for a large chain of cinemas. Customers use a mobile app to search for movies they're interested in and purchase tickets in the app. Ticket purchase requests are sent to Pub/Sub and are processed with a Dataflow streaming pipeline configured to conduct the following steps:
1. Check for availability of the movie tickets at the selected cinema.
2. Assign the ticket price and accept payment.
3. Reserve the tickets at the selected cinema.
4. Send successful purchases to your database.
Each step in this process has low latency requirements (less than 50 milliseconds). You have developed a logistic regression model with BigQuery ML that predicts whether offering a promo code for free popcorn increases the chance of a ticket purchase, and this prediction should be added to the ticket purchase process.
You want to identify the simplest way to deploy this model to production while adding minimal latency. What should you do?

• A. Export your model in TensorFlow format, deploy it on Vertex AI, and query the prediction endpoint from your streaming pipeline.
• B. Export your model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline.
• C. Run batch inference with BigQuery ML every five minutes on each new set of tickets issued.
• D. Convert your model with TensorFlow Lite (TFLite), and add it to the mobile app so that the promo code and the incoming request arrive together in Pub/Sub.

正解：B

解説：
The simplest way to deploy a logistic regression model with BigQuery ML to production while adding minimal latency is to export the model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline. This option has the following advantages:
* It allows the model prediction to be performed in real time, as part of the Dataflow streaming pipeline that processes the ticket purchase requests. This ensures that the promo code offer is based on the most recent data and customer behavior, and that the offer is delivered to the customer without delay.
* It leverages the compatibility and performance of TensorFlow and Dataflow, which are both part of the Google Cloud ecosystem. TensorFlow is a popular and powerful framework for building and deploying machine learning models, and Dataflow is a fully managed service that runs Apache Beam pipelines for data processing and transformation. By using the tfx_bsl.public.beam.RunInference step, you can easily integrate your TensorFlow model with your Dataflow pipeline, and take advantage of the parallelism and scalability of Dataflow.
* It simplifies the model deployment and management, as the model is packaged with the Dataflow pipeline and does not require a separate service or endpoint. The model can be updated by redeploying the Dataflow pipeline with a new model version.
The other options are less optimal for the following reasons:
* Option A: Running batch inference with BigQuery ML every five minutes on each new set of tickets issued introduces additional latency and complexity. This option requires running a separate BigQuery job every five minutes, which can incur network overhead and latency. Moreover, this option requires storing and retrieving the intermediate results of the batch inference, which can consume storage space and increase the data transfer time.
* Option C: Exporting the model in TensorFlow format, deploying it on Vertex AI, and querying the prediction endpoint from the streaming pipeline introduces additional latency and cost. This option requires creating and managing a Vertex AI endpoint, which is a managed service that provides various tools and features for machine learning, such as training, tuning, serving, and monitoring. However, querying the Vertex AI endpoint from the streaming pipeline requires making an HTTP request, which can incur network overhead and latency. Moreover, this option requires paying for the Vertex AI endpoint usage, which can increase the cost of the model deployment.
* Option D: Converting the model with TensorFlow Lite (TFLite), and adding it to the mobile app so that the promo code and the incoming request arrive together in Pub/Sub introduces additional challenges and risks. This option requires converting the model to a TFLite format, which is a lightweight and optimized format for running TensorFlow models on mobile and embedded devices. However, converting the model to TFLite may not preserve the accuracy or functionality of the original model, as some operations or features may not be supported by TFLite. Moreover, this option requires updating the mobile app with the TFLite model, which can be tedious and time-consuming, and may depend on the user's willingness to update the app. Additionally, this option may expose the model to potential
* security or privacy issues, as the model is running on the user's device and may be accessed or modified by malicious actors.
References:
* [Exporting models for prediction | BigQuery ML]
* [tfx_bsl.public.beam.run_inference | TensorFlow Extended]
* [Vertex AI documentation]
* [TensorFlow Lite documentation]

質問 # 19
You work for a bank with strict data governance requirements. You recently implemented a custom model to detect fraudulent transactions You want your training code to download internal data by using an API endpoint hosted in your projects network You need the data to be accessed in the most secure way, while mitigating the risk of data exfiltration. What should you do?

• A. Download the data to a Cloud Storage bucket before calling the training job
• B. Create a Cloud Run endpoint as a proxy to the data Use Identity and Access Management (1AM) authentication to secure access to the endpoint from the training job.
• C. Enable VPC Service Controls for peering's, and add Vertex Al to a service perimeter
• D. Configure VPC Peering with Vertex Al and specify the network of the training job

正解：B

質問 # 20
You are creating a deep neural network classification model using a dataset with categorical input values. Certain columns have a cardinality greater than 10,000 unique values. How should you encode these categorical values as input into the model?

• A. Convert each categorical value into a run-length encoded string.
• B. Map the categorical variables into a vector of boolean values.
• C. Convert each categorical value into an integer value.
• D. Convert the categorical string data to one-hot hash buckets.

正解：D

解説：
Option A is incorrect because converting each categorical value into an integer value is not a good way to encode categorical values with high cardinality. This method implies an ordinal relationship between the categories, which may not be true. For example, assigning the values 1, 2, and 3 to the categories "red", "green", and "blue" does not make sense, as there is no inherent order among these colors1.
Option B is correct because converting the categorical string data to one-hot hash buckets is a suitable way to encode categorical values with high cardinality. This method uses a hash function to map each category to a fixed-length vector of binary values, where only one element is 1 and the rest are 0. This method preserves the sparsity and independence of the categories, and reduces the dimensionality of the input space2.
Option C is incorrect because mapping the categorical variables into a vector of boolean values is not a valid way to encode categorical values with high cardinality. This method implies that each category can be represented by a combination of true/false values, which may not be possible for a large number of categories. For example, if there are 10,000 categories, then there are 2

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