さらに、Japancert Professional-Machine-Learning-Engineerダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1d3OSwaxUoT1ZDtoCUJY9NcpHKOkzb8gI
現在の社会で人材があちこちいます。IT領域でも同じです。コンピュータの普及につれて、パソコンを使えない人がほとんどいなくなります。ですから、IT業界で勤めているあなたはプレッシャーを感じていませんか。学歴はどんなに高くてもあなたの実力を代表できません。学歴はただ踏み台だけで、あなたの地位を確保できる礎は実力です。IT職員としているあなたがどうやって自分自身の実力を養うのですか。IT認定試験を受験するのは一つの良い方法です。Professional-Machine-Learning-Engineer試験を通して、あなたは新しいスキルをマスターすることができるだけでなく、Professional-Machine-Learning-Engineer認証資格を取得して自分の高い能力を証明することもできます。最近、Google Professional-Machine-Learning-Engineer試験の認証資格がとても人気があるようになりましたが、受験したいですか。
| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified - Professional Machine Learning Engineer |
| Exam Number: | Professional-Machine-Learning-Engineer |
| Passing Score: | Not publicly disclosed (Pass/Fail) |
| Exam Price: | $200 USD |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 120 minutes |
| Available Languages: | Japanese, English |
| Exam Format: | Multiple select, Multiple choice |
| Real Exam Qty: | 50-60 |
| Related Certifications: | Google Cloud Certified - Professional Data Engineer |
| Sample Questions: | Google Professional-Machine-Learning-Engineer Sample Questions |
| Exam Way: | Online (proctored) or Test center (Kryterion) |
| Pre Condition: | Recommended 3+ years of industry experience with ML models and 1+ year of experience using Google Cloud. |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/machine-learning-engineer |
>> Professional-Machine-Learning-Engineer必殺問題集 <<
さまざまな人々がさまざまな学習習慣を持っているという事実を踏まえて、3つのProfessional-Machine-Learning-Engineerトレーニング質問バージョンをご案内します。さらに、Professional-Machine-Learning-Engineer学習教材のデモを自由にダウンロードして検討することもできます。そのような試用に追加料金は発生しないことをお約束します。逆に、Professional-Machine-Learning-Engineer試験問題のデモを試して、十分な内容を選択することを心からお勧めします。 Professional-Machine-Learning-Engineerトレーニングガイドは、時間とお金をかけて購入する価値があります。
Googleのプロフェッショナル機械学習エンジニア試験は、多肢選択問題とシナリオベースの問題の組み合わせになっています。この試験は、データ準備、モデルトレーニングと評価、最適化技術、展開戦略など、幅広いトピックをカバーしています。候補者は、TensorFlow、Keras、Scikit-learnをはじめとするさまざまなツールやフレームワークを使用して機械学習モデルを設計、構築、展開する能力を証明する必要があります。この試験に合格するには、機械学習の概念を徹底的に理解し、機械学習ソリューションを設計、実装する実務経験が必要です。
質問 # 283
You work for a large retailer and you need to build a model to predict customer churn. The company has a dataset of historical customer data, including customer demographics, purchase history, and website activity. You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do?
正解：C
質問 # 284
You are building a custom image classification model and plan to use Vertex Al Pipelines to implement the end-to-end training. Your dataset consists of images that need to be preprocessed before they can be used to train the model. The preprocessing steps include resizing the images, converting them to grayscale, and extracting features. You have already implemented some Python functions for the preprocessing tasks. Which components should you use in your pipeline'?
正解：C
質問 # 285
You are going to train a DNN regression model with Keras APIs using this code:
How many trainable weights does your model have? (The arithmetic below is correct.)
正解：C
解説：
The number of trainable weights in a DNN regression model with Keras APIs can be calculated by multiplying the number of input units by the number of output units for each layer, and adding the number of bias units for each layer. The bias units are usually equal to the number of output units, except for the last layer, which does not have bias units if the activation function is softmax1. In this code, the model has three layers: a dense layer with 256 units and relu activation, a dropout layer with 0.25 rate, and a dense layer with 2 units and softmax activation. The input shape is 500. Therefore, the number of trainable weights is:
* For the first layer: 500 input units * 256 output units + 256 bias units = 128256
* For the second layer: The dropout layer does not have any trainable weights, as it only randomly sets some of the input units to zero to prevent overfitting2.
* For the third layer: 256 input units * 2 output units + 0 bias units = 512 The total number of trainable weights is 128256 + 512 = 161024. Therefore, the correct answer is B.
References:
* How to calculate the number of parameters for a Convolutional Neural Network?
* Dropout (keras.io)
質問 # 286
You are training a deep learning model for semantic image segmentation with reduced training time. While using a Deep Learning VM Image, you receive the following error: The resource
'projects/deeplearning-platforn/zones/europe-west4-c/acceleratorTypes/nvidia-tesla-k80' was not found. What should you do?
正解：C
解説：
The error message indicates that the selected GPU type (nvidia-tesla-k80) is not available in the selected region (europe-west4-c). This can happen when the GPU type is not supported in the region, or when the GPU quota is exhausted in the region. To avoid this error, you should ensure that the required GPU is available in the selected region before creating a Deep Learning VM Image. You can use the following steps to check the GPU availability and quota:
* To check the GPU availability, you can use the gcloud compute accelerator-types list command with the --filter flag to specify the GPU type and the region. For example, to check the availability of nvidia-tesla-k80 in europe-west4-c, you can run:
gcloud compute accelerator-types list --filter="name=nvidia-tesla-k80 AND zone:europe-west4-c"
* If the command returns an empty result, it means that the GPU type is not supported in the region. You can either choose a different GPU type or a different region that supports the GPU type. You can use the same command without the --filter flag to list all the available GPU types and regions. For example, to list all the available GPU types in europe-west4-c, you can run:
gcloud compute accelerator-types list --filter="zone:europe-west4-c"
* To check the GPU quota, you can use the gcloud compute regions describe command with the --format flag to specify the region and the quota metric. For example, to check the quota for nvidia-tesla-k80 in europe-west4-c, you can run:
gcloud compute regions describe europe-west4-c --format="value(quotas.NVIDIA_K80_GPUS)"
* If the command returns a value of 0, it means that the GPU quota is exhausted in the region. You can either request more quota from Google Cloud or choose a different region that has enough quota for the GPU type.
References:
* Troubleshooting | Deep Learning VM Images | Google Cloud
* Checking GPU availability
* Checking GPU quota
質問 # 287
You work for a gaming company that has millions of customers around the world. All games offer a chat feature that allows players to communicate with each other in real time. Messages can be typed in more than
20 languages and are translated in real time using the Cloud Translation API. You have been asked to build an ML system to moderate the chat in real time while assuring that the performance is uniform across the various languages and without changing the serving infrastructure.
You trained your first model using an in-house word2vec model for embedding the chat messages translated by the Cloud Translation API. However, the model has significant differences in performance across the different languages. How should you improve it?
正解：D
解説：
The problem with the current approach is that it relies on the Cloud Translation API to translate the chat messages into a common language before embedding them with the in-house word2vec model. This introduces two sources of error: the translation quality and the word2vec quality. The transla tion quality may vary across different languages, depending on the availability of data and the complexity of the grammar and vocabulary. The word2vec quality may also vary depending on the size and diversity of the corpus used to train it. These errors may affect the performance of the classifier that moderates the chat messages, resulting in significant differences across the languages.
A better approach would be to train a classifier using the chat messages in their original language, without relying on the Cloud Translation API or the in-house word2vec model. This way, the classifier can learn the nuances and subtleties of each language, and avoid the errors introduced by the translation and embedding processes. This would also reduce the latency and cost of the moderation system, as it would not need to invoke the Cloud Translation API for every message. To train a classifier using the chat messages in their original language, one could use a multilingual pre-trained model such as mBERT or XLM-R, which can handle multiple languages and domains. Alternatively, one could train a separate classifier for each language, using a monolingual pre-trained model such as BERT or a custom model tailored to the specific language and task.
:
Professional ML Engineer Exam Guide
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate Google Cloud launches machine learning engineer certification
[mBERT: Bidirectional Encoder Representations from Transformers]
[XLM-R: Unsupervised Cross-lingual Representation Learning at Scale]
[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]
質問 # 288
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P.S. JapancertがGoogle Driveで共有している無料かつ新しいProfessional-Machine-Learning-Engineerダンプ：https://drive.google.com/open?id=1d3OSwaxUoT1ZDtoCUJY9NcpHKOkzb8gI