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

質問 1

You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

（A）Use Vertex Al Platform for distributed training

（B）Create a cluster on Dataproc for training

（C）Create a Managed Instance Group with autoscaling

（D）Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.

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

You are building a TensorFlow model for a financial institution that predicts the impact of consumer spending on inflation globally. Due to the size and nature of the data, your model is long-running across all types of hardware, and you have built frequent checkpointing into the training process. Your organization has asked you to minimize cost. What hardware should you choose?

（A）A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with a non- preemptible v3-8 TPU

（B）A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with an NVIDIA P100 GPU

（C）A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with a preemptible v3-8 TPU

（D）A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with 4 NVIDIA P100 GPUs

正解：C 解答を投票する

質問 3

You are an ML engineer at a travel company. You have been researching customers' travel behavior for many years, and you have deployed models that predict customers' vacation patterns. You have observed that customers' vacation destinations vary based on seasonality and holidays; however, these seasonal variations are similar across years. You want to quickly and easily store and compare the model versions and performance statistics across years. What should you do?

（A）Store the performance statistics of each pipeline run in Kubeflow under an experiment for each season per year. Compare the results across the experiments in the Kubeflow UI.

（B）Create versions of your models for each season per year in Vertex AI. Compare the performance statistics across the models in the Evaluate tab of the Vertex AI UI.

（C）Store the performance statistics of each version of your models using seasons and years as events in Vertex ML Metadata. Compare the results across the slices.

（D）Store the performance statistics in Cloud SQL. Query that database to compare the performance statistics across the model versions.

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 4

You have developed a fraud detection model for a large financial institution using Vertex AI. The model achieves high accuracy, but stakeholders are concerned about potential bias based on customer demographics.
You have been asked to provide insights into the model's decision-making process and identify any fairness issues. What should you do?

（A）Use feature attribution in Vertex AI to analyze model predictions and the impact of each feature on the model's predictions.

（B）Enable Vertex AI Model Monitoring to detect training-serving skew. Configure an alert to send an email when the skew or drift for a model's feature exceeds a predefined threshold. Retrain the model by appending new data to existing training data.

（C）Create feature groups using Vertex AI Feature Store to segregate customer demographic features and non-demographic features. Retrain the model using only non-demographic features.

（D）Compile a dataset of unfair predictions. Use Vertex AI Vector Search to identify similar data points in the model's predictions. Report these data points to the stakeholders.

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 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 6

You work for the AI team of an automobile company, and you are developing a visual defect detection model using TensorFlow and Keras. To improve your model performance, you want to incorporate some image augmentation functions such as translation, cropping, and contrast tweaking. You randomly apply these functions to each training batch. You want to optimize your data processing pipeline for run time and compute resources utilization. What should you do?

（A）Embed the augmentation functions dynamically in the tf.Data pipeline.

（B）Use Dataflow to create the augmentations dynamically per training run, and stage them as TFRecords.

（C）Embed the augmentation functions dynamically as part of Keras generators.

（D）Use Dataflow to create all possible augmentations, and store them as TFRecords.

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 7

You are developing an image recognition model using PyTorch based on ResNet50 architecture Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs What should you do?

（A）Configure a Compute Engine VM with all the dependencies that launches the training Tram your model with Vertex Al using a custom tier that contains the required GPUs.

（B）Create a Vertex Al Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to tram your model.

（C）Package your code with Setuptools and use a pre-built container. Train your model with Vertex Al using a custom tier that contains the required GPUs.

（D）Create a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs Prepare and submit a TFJob operator to this node pool.

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 8

You work for a retailer that sells clothes to customers around the world. You have been tasked with ensuring that ML models are built in a secure manner. Specifically, you need to protect sensitive customer data that might be used in the models. You have identified four fields containing sensitive data that are being used by your data science team: AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE.
What should you do with the data before it is made available to the data science team for training purposes?

（A）Coarsen the data by putting AGE into quantiles and rounding LATITUDE_LONGTTUDE into single precision. The other two fields are already as coarse as possible.

（B）Tokenize all of the fields using hashed dummy values to replace the real values.

（C）Use principal component analysis (PCA) to reduce the four sensitive fields to one PCA vector.

（D）Remove all sensitive data fields, and ask the data science team to build their models using non-sensitive data.

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 9

You are developing an ML model that uses sliced frames from video feed and creates bounding boxes around specific objects. You want to automate the following steps in your training pipeline: ingestion and preprocessing of data in Cloud Storage, followed by training and hyperparameter tuning of the object model using Vertex AI jobs, and finally deploying the model to an endpoint. You want to orchestrate the entire pipeline with minimal cluster management. What approach should you use?

（A）Use Vertex AI Pipelines with Kubeflow Pipelines SDK.

（B）Use Vertex AI Pipelines with TensorFlow Extended (TFX) SDK.

（C）Use Cloud Composer for the orchestration.

（D）Use Kubeflow Pipelines on Google Kubernetes Engine.

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

You are working on a system log anomaly detection model for a cybersecurity organization. You have developed the model using TensorFlow, and you plan to use it for real-time prediction. You need to create a Dataflow pipeline to ingest data via Pub/Sub and write the results to BigQuery. You want to minimize the serving latency as much as possible. What should you do?

（A）Load the model directly into the Dataflow job as a dependency, and use it for prediction.

（B）Containerize the model prediction logic in Cloud Run, which is invoked by Dataflow.

（C）Deploy the model to a Vertex AI endpoint, and invoke this endpoint in the Dataflow job.

（D）Deploy the model in a TFServing container on Google Kubernetes Engine, and invoke it in the Dataflow job.

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 11

You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high- throughput online prediction. Which architecture should you use?

（A）* Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online prediction

（B）* Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue

（C）* Stream incoming prediction request data into Cloud Spanner
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue.

（D）* Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue

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 12

You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?

（A）Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.

（B）Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.

（C）Rewrite your input function to resize and reshape the input images.

（D）Rewrite your input function using parallel reads, parallel processing, and prefetch.

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 13

You work for an online retailer. Your company has a few thousand short lifecycle products. Your company has five years of sales data stored in BigQuery. You have been asked to build a model that will make monthly sales predictions for each product. You want to use a solution that can be implemented quickly with minimal effort. What should you do?

（A）Use Prophet on Vertex Al Training to build a custom model.

（B）Use Vertex Al Forecast to build a NN-based model.

（C）Use TensorFlow on Vertex Al Training to build a custom model.

（D）Use BigQuery ML to build a statistical AR1MA_PLUS model.

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 have been tasked with deploying prototype code to production. The feature engineering code is in PySpark and runs on Dataproc Serverless. The model training is executed by using a Vertex Al custom training job. The two steps are not connected, and the model training must currently be run manually after the feature engineering step finishes. You need to create a scalable and maintainable production process that runs end-to-end and tracks the connections between steps. What should you do?

（A）Create a Vertex Al Workbench notebook Initiate an Apache Spark context in the notebook, and run the PySpark feature engineering code Use the same notebook to run the custom model training job in TensorFlow Run the notebook cells sequentially to tie the steps together end-to-end

（B）Create a Vertex Al Workbench notebook Use the notebook to submit the Dataproc Serverless feature engineering job Use the same notebook to submit the custom model training job Run the notebook cells sequentially to tie the steps together end-to-end

（C）Use the Kubeflow pipelines SDK to write code that specifies two components
- The first is a Dataproc Serverless component that launches the feature engineering job
- The second is a custom component wrapped in the
creare_cusrora_rraining_job_from_ccraponent Utility that launches the custom model training job.

（D）Create a Vertex Al Pipelines job to link and run both components Use the Kubeflow pipelines SDK to write code that specifies two components
- The first component initiates an Apache Spark context that runs the PySpark feature engineering code
- The second component runs the TensorFlow custom model training code Create a Vertex Al Pipelines job to link and run both components

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

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 16

You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company's logo. In the dataset, 96% of examples don't have the logo, so the dataset is very skewed. Which metrics would give you the most confidence in your model?

（A）F-score where precision is weighed more than recall

（B）F1 score

（C）RMSE

（D）F-score where recall is weighed more than precision

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 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)

質問 18

You have built a custom model that performs several memory-intensive preprocessing tasks before it makes a prediction. You deployed the model to a Vertex Al endpoint. and validated that results were received in a reasonable amount of time After routing user traffic to the endpoint, you discover that the endpoint does not autoscale as expected when receiving multiple requests What should you do?

（A）Increase the CPU utilization target in the autoscaling configurations

（B）Decrease the CPU utilization target in the autoscaling configurations

（C）Use a machine type with more memory

（D）Decrease the number of workers per machine

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 19

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)

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

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