Professional-Machine-Learning-Engineer無料問題集「Google Professional Machine Learning Engineer」

質問 1

You recently used XGBoost to train a model in Python that will be used for online serving Your model prediction service will be called by a backend service implemented in Golang running on a Google Kubemetes Engine (GKE) cluster Your model requires pre and postprocessing steps You need to implement the processing steps so that they run at serving time You want to minimize code changes and infrastructure maintenance and deploy your model into production as quickly as possible. What should you do?

（A）Use the Predictor interface to implement a custom prediction routine Build the custom contain upload the container to Vertex Al Model Registry, and deploy it to a Vertex Al endpoint.

（B）Use FastAPI to implement an HTTP server Create a Docker image that runs your HTTP server and deploy it on your organization's GKE cluster.

（C）Use FastAPI to implement an HTTP server Create a Docker image that runs your HTTP server Upload the image to Vertex Al Model Registry and deploy it to a Vertex Al endpoint.

（D）Use the XGBoost prebuilt serving container when importing the trained model into Vertex Al Deploy the model to a Vertex Al endpoint Work with the backend engineers to implement the pre- and postprocessing steps in the Golang backend service.

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 2

Your team is building a convolutional neural network (CNN)-based architecture from scratch. The preliminary experiments running on your on-premises CPU-only infrastructure were encouraging, but have slow convergence. You have been asked to speed up model training to reduce time-to-market. You want to experiment with virtual machines (VMs) on Google Cloud to leverage more powerful hardware. Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction. Which environment should you train your model on?

（A）AVM on Compute Engine and 8 GPUs with all dependencies installed manually.

（B）A Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-installed.

（C）A Deep Learning VM with more powerful CPU e2-highcpu-16 machines with all libraries pre-installed.

（D）AVM on Compute Engine and 1 TPU with all dependencies installed manually.

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 3

You need to use TensorFlow to train an image classification model. Your dataset is located in a Cloud Storage directory and contains millions of labeled images Before training the model, you need to prepare the dat a. You want the data preprocessing and model training workflow to be as efficient scalable, and low maintenance as possible. What should you do?

（A）1 Create a Dataflow job that creates sharded TFRecord files in a Cloud Storage directory.
2 Reference tf .data.TFRecordDataset in the training script.
3. Train the model by using Vertex Al Training with a V100 GPU.

（B）1 Create a Dataflow job that moves the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label.
2 Reference tfds.fclder_da-asst.imageFclder in the training script.
3. Train the model by using Vertex AI Training with a V100 GPU.

（C）1 Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex Al Workbench instance.
2 Write a Python script that creates sharded TFRecord files in a directory inside the instance
3. Reference tf. da-a.TFRecrrdDataset in the training script.
4. Train the model by using the Workbench instance.

（D）1 Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex Al Workbench instance.
2 Write a Python scnpt that copies the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label.
3 Reference tf ds. f older_dataset. imageFolder in the training script.
4. Train the model by using the Workbench instance.

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 4

You want to migrate a scikrt-learn classifier model to TensorFlow. You plan to train the TensorFlow classifier model using the same training set that was used to train the scikit-learn model and then compare the performances using a common test set. You want to use the Vertex Al Python SDK to manually log the evaluation metrics of each model and compare them based on their F1 scores and confusion matrices. How should you log the metrics?

（A）Option D

（B）Option B

（C）Option C

（D）Option A

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 5

You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers

（A）Decrease the batch size argument in your transformation

（B）Increase the buffer size for the shuffle option.

（C）Use the interleave option for reading data

（D）Reduce the value of the repeat parameter

（E）Set the prefetch option equal to the training batch size

正解：C、E 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 6

You work as an ML researcher at an investment bank and are experimenting with the Gemini large language model (LLM). You plan to deploy the model for an internal use case and need full control of the model's underlying infrastructure while minimizing inference time. Which serving configuration should you use for this task?

（A）Deploy the model on a Vertex AI endpoint using one-click deployment in Model Garden.

（B）Deploy the model on a Vertex AI endpoint manually by creating a custom inference container.

（C）Deploy the model on a Google Kubernetes Engine (GKE) cluster using the deployment options in Model Garden.

（D）Deploy the model on a Google Kubernetes Engine (GKE) cluster manually by creating a custom YAML manifest.

正解：D 解答を投票する

質問 7

Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

（A）1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.

（B）1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.
2 Dispatch an appropriately sized shuttle and indicate the required stops on the map

（C）1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.
2. Dispatch an available shuttle and provide the map with the required stops based on the prediction

（D）1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 8

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?

（A）Create a custom training loop.

（B）Use a TPU with tf.distribute.TPUStrategy.

（C）Distribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset

（D）Increase the batch size.

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 9

You are working on a Neural Network-based project. The dataset provided to you has columns with different ranges. While preparing the data for model training, you discover that gradient optimization is having difficulty moving weights to a good solution. What should you do?

（A）Improve the data cleaning step by removing features with missing values.

（B）Use the representation transformation (normalization) technique.

（C）Change the partitioning step to reduce the dimension of the test set and have a larger training set.

（D）Use feature construction to combine the strongest features.

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 10

While performing exploratory data analysis on a dataset, you find that an important categorical feature has 5% null values. You want to minimize the bias that could result from the missing values. How should you handle the missing values?

（A）Remove the rows with missing values, and upsample your dataset by 5%.

（B）Replace the missing values with a placeholder category indicating a missing value.

（C）Replace the missing values with the feature's mean.

（D）Move the rows with missing values to your validation dataset.

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 11

You are building a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?

（A）Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5r and then use that number to represent the city in the model.

（B）Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.

（C）Create a new view with BigQuery that does not include a column with city information

（D）Use TensorFlow to create a categorical variable with a vocabulary list Create the vocabulary file, and upload it as part of your model to BigQuery ML.

正解：B 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 12

You are developing a model to predict whether a failure will occur in a critical machine part. You have a dataset consisting of a multivariate time series and labels indicating whether the machine part failed You recently started experimenting with a few different preprocessing and modeling approaches in a Vertex Al Workbench notebook. You want to log data and track artifacts from each run. How should you set up your experiments?

（A）

（B）

（C）

（D）

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 13

You have trained a model by using data that was preprocessed in a batch Dataflow pipeline Your use case requires real-time inference. You want to ensure that the data preprocessing logic is applied consistently between training and serving. What should you do?

（A）Perform data validation to ensure that the input data to the pipeline is the same format as the input data to the endpoint.

（B）Batch the real-time requests by using a time window and then use the Dataflow pipeline to preprocess the batched requests. Send the preprocessed requests to the endpoint.

（C）Refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline Share this code with the end users of the endpoint.

（D）Refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline Use the same code in the endpoint.

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 14

You work at an ecommerce startup. You need to create a customer churn prediction model Your company's recent sales records are stored in a BigQuery table You want to understand how your initial model is making predictions. You also want to iterate on the model as quickly as possible while minimizing cost How should you build your first model?

（A）Export the data to a Cloud Storage Bucket Load the data into a pandas DataFrame on Vertex Al Workbench and train a logistic regression model with scikit-learn.

（B）Export the data to a Cloud Storage Bucket Create tf. data. Dataset to read the data from Cloud Storage Implement a deep neural network in TensorFlow.

（C）Prepare the data in BigQuery and associate the data with a Vertex Al dataset Create an AutoMLTabuiarTrainmgJob to train a classification model.

（D）Create a tf.data.Dataset by using the TensorFlow BigQueryChent Implement a deep neural network in TensorFlow.

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 15

You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

（A）Build your custom container to run jobs on Al Platform Training

（B）Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training

（C）Build your custom containers to run distributed training jobs on Al Platform Training

（D）Use a built-in model available on Al Platform Training

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 16

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?

（A）Run a BigQuery ML task to perform logistic regression for the classification

（B）Use Al Platform Notebooks to run the classification model with pandas library

（C）Use Al Platform to run the classification model job configured for hyperparameter tuning

（D）Configure AutoML Tables to perform the classification task

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 17

You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company's historical data as your training set, you created a TensorFlow model and deployed it to AI Platform. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?

（A）Use the AI Explanations feature on AI Platform. Submit each prediction request with the 'explain' keyword to retrieve feature attributions using the sampled Shapley method.

（B）Stream prediction results to BigQuery. Use BigQuery's CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable.

（C）Use the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded. Rank the feature importance in order of those that caused the most significant performance drop when removed from the model.

（D）Use AI Platform notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal.

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 18

You are deploying a new version of a model to a production Vertex Al endpoint that is serving traffic You plan to direct all user traffic to the new model You need to deploy the model with minimal disruption to your application What should you do?

（A）1 Create a new endpoint.
2 Create a new model Set it as the default version Upload the model to Vertex Al Model Registry.
3. Deploy the new model to the new endpoint.
4 Update Cloud DNS to point to the new endpoint

（B）1. Create a new endpoint.
2. Create a new model Set the parentModel parameter to the model ID of the currently deployed model and set it as the default version Upload the model to Vertex Al Model Registry
3. Deploy the new model to the new endpoint and set the new model to 100% of the traffic

（C）1 Create a new model Set the parentModel parameter to the model ID of the currently deployed model Upload the model to Vertex Al Model Registry.
2 Deploy the new model to the existing endpoint and set the new model to 100% of the traffic.

（D）1, Create a new model Set it as the default version Upload the model to Vertex Al Model Registry
2 Deploy the new model to the existing endpoint

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 19

You need to deploy a scikit-learn classification model to production. The model must be able to serve requests 24/7 and you expect millions of requests per second to the production application from 8 am to 7 pm. You need to minimize the cost of deployment What should you do?

（A）Deploy an online Vertex Al prediction endpoint Set the max replica count to 1

（B）Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to 1.

（C）Deploy an online Vertex Al prediction endpoint Set the max replica count to 100

（D）Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to 100.

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 20

You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

（A）Ensure that model performance is monitored

（B）Ensure that feature expectations are captured in the schema

（C）Ensure that training is reproducible

（D）Ensure that all hyperparameters are tuned

正解：A 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

Professional-Machine-Learning-Engineer 無料問題集「Google Professional Machine Learning Engineer」

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