Professional-Machine-Learning-Engineerの無料問題集（290題）、Professional-Machine-Learning-Engineer認定試験参考書

Google Professional-Machine-Learning-Engineer 問題集

試験コード：Professional-Machine-Learning-Engineer

試験名称：Google Professional Machine Learning Engineer

最近更新時間：2025-03-31

問題と解答：全290問

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質問 1：
You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects.
Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?
A. AutoML Vision model
B. AutoML Vision Edge mobile-high-accuracy-1 model
C. AutoML Vision Edge mobile-low-latency-1 model
D. AutoML Vision Edge mobile-versatile-1 model
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 2：
You work at a bank You have a custom tabular ML model that was provided by the bank's vendor. The training data is not available due to its sensitivity. The model is packaged as a Vertex Al Model serving container which accepts a string as input for each prediction instance. In each string the feature values are separated by commas. You want to deploy this model to production for online predictions, and monitor the feature distribution over time with minimal effort What should you do?
A. 1 Refactor the serving container to accept key-value pairs as input format.
2 Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Al endpoint.
3. Create a Vertex Al Model Monitoring job with feature skew detection as the monitoring objective.
B. 1 Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Al endpoint.
2 Create a Vertex Al Model Monitoring job with feature skew detection as the monitoring objective and provide an instance schema.
C. 1 Refactor the serving container to accept key-value pairs as input format.
2. Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Al endpoint.
3. Create a Vertex Al Model Monitoring job with feature drift detection as the monitoring objective.
D. 1 Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Ai endpoint.
2. Create a Vertex Al Model Monitoring job with feature drift detection as the monitoring objective, and provide an instance schema.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 3：
You recently built the first version of an image segmentation model for a self-driving car. After deploying the model, you observe a decrease in the area under the curve (AUC) metric. When analyzing the video recordings, you also discover that the model fails in highly congested traffic but works as expected when there is less traffic. What is the most likely reason for this result?
A. The model is overfitting in areas with less traffic and underfitting in areas with more traffic.
B. Too much data representing congested areas was used for model training.
C. Gradients become small and vanish while backpropagating from the output to input nodes.
D. AUC is not the correct metric to evaluate this classification model.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 4：
You developed a Transformer model in TensorFlow to translate text Your training data includes millions of documents in a Cloud Storage bucket. You plan to use distributed training to reduce training time. You need to configure the training job while minimizing the effort required to modify code and to manage the clusters configuration. What should you do?
A. Create a Vertex Al custom training job with GPU accelerators for the second worker pool Use tf .
distribute.MultiWorkerMirroredStrategy for distribution.
B. Create a Vertex Al custom distributed training job with Reduction Server Use N1 high-memory machine type instances for the first and second pools, and use N1 high-CPU machine type instances for the third worker pool.
C. Create a training job that uses Cloud TPU VMs Use tf.distribute.TPUStrategy for distribution.
D. Create a Vertex Al custom training job with a single worker pool of A2 GPU machine type instances Use tf .distribute.MirroredStraregy for distribution.
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 5：
You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?
A. Replace the NVIDIA P100 GPU with a v3-32 TPU in the training job.
B. Use the tf.distribute.Strategy API and run a distributed training job.
C. Increase the instance memory to 512 GB and increase the batch size.
D. Enable early stopping in your Vertex AI Training job.
正解：B

質問 6：
Your team frequently creates new ML models and runs experiments. Your team pushes code to a single repository hosted on Cloud Source Repositories. You want to create a continuous integration pipeline that automatically retrains the models whenever there is any modification of the code. What should be your first step to set up the CI pipeline?
A. Configure a Cloud Build trigger with the event set as "Push to a branch"
B. Configure a Cloud Build trigger with the event set as "Pull Request"
C. Configure a Cloud Function that builds the repository each time there is a code change.
D. Configure a Cloud Function that builds the repository each time a new branch is created.
正解：A
解説: (Topexam メンバーにのみ表示されます)

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Google Professional-Machine-Learning-Engineer 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Collaborating within and across teams to manage data and models: It explores and processes organization-wide data including Apache Spark, Cloud Storage, Apache Hadoop, Cloud SQL, and Cloud Spanner. The topic also discusses using Jupyter Notebooks to model prototypes. Lastly, it discusses tracking and running ML experiments.
トピック 2	Serving and scaling models: This section deals with Batch and online inference, using frameworks such as XGBoost, and managing features using VertexAI.
トピック 3	Monitoring ML solutions: It identifies risks to ML solutions. Moreover, the topic discusses monitoring, testing, and troubleshooting ML solutions.
トピック 4	Automating and orchestrating ML pipelines: This topic focuses on developing end-to-end ML pipelines, automation of model retraining, and lastly tracking and auditing metadata.
トピック 5	Scaling prototypes into ML models: This topic covers building and training models. It also focuses on opting for suitable hardware for training.

参照：https://cloud.google.com/certification/guides/machine-learning-engineer

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