[2026] Google Professional Machine Learning Engineer - Professional-Machine-Learning-Engineer 무료 시험 문제

문제1

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.

정답: B

설명: (Fast2test 회원만 볼 수 있음)

문제2

Your organization's security policy states that prediction traffic for a patient-risk model must never traverse the public internet, and that data exfiltration from the project must be blocked even by users with valid credentials. You need to deploy the model accordingly. What should you do?

A. Deploy to a public endpoint behind Cloud Armor with an IP allowlist for the corporate network range.

B. Deploy to a Private Service Connect endpoint reachable from your VPC, and configure a VPC Service Controls perimeter around the project's Agent Platform and storage services.

C. Deploy to a public endpoint, and encrypt request payloads in the application before sending them.

D. Deploy to a public endpoint, and restrict access with an IAM allow policy limited to the service account of the calling application.

정답: B

설명: (Fast2test 회원만 볼 수 있음)

문제3

You need to quickly build and train a model to predict the sentiment of customer reviews with custom categories without writing code. You do not have enough data to train a model from scratch. The resulting model should have high predictive performance. Which service should you use?

A. AutoML Natural Language

B. AI Hub pre-made Jupyter Notebooks

C. AI Platform Training built-in algorithms

D. Cloud Natural Language API

정답: A

설명: (Fast2test 회원만 볼 수 있음)

문제4

Your team ships weekly prompt changes to a Gemini-based meeting summarization feature.
Reviewers currently read 50 sample summaries by hand after each change, which delays releases and produces inconsistent judgments. You need a repeatable way to compare candidate prompts on quality dimensions such as groundedness and coherence. What should you do?

A. Use the Gen AI evaluation service with pointwise model-based metrics for groundedness and coherence, and run it on a fixed evaluation dataset for each candidate prompt.

B. Measure average output token count and latency per prompt, and select the fastest prompt.

C. Deploy each candidate prompt to 10% of production traffic and compare thumbs-up rates over one week.

D. Compute BLEU scores against a set of reference summaries written by the reviewers.

정답: A

설명: (Fast2test 회원만 볼 수 있음)

문제5

Your company recently migrated several of is ML models to Google Cloud. You have started developing models in Vertex AI. You need to implement a system that tracks model artifacts and model lineage. You want to create a simple, effective solution that can also be reused for future models. What should you do?

A. Use a combination of Vertex AI Pipelines and the Vertex AI SDK to integrate metadata tracking into the ML workflow.

B. Implement a scheduled metadata tracking solution using Cloud Composer and Cloud Run functions.

C. Use Vertex AI Pipelines for model artifacts and MLflow for model lineage.

D. Use Vertex AI Experiments for model artifacts and use Vertex ML Metadata for model lineage.

정답: A

설명: (Fast2test 회원만 볼 수 있음)

문제6

Your company's data science team has developed a complex feature engineering workflow using Python with pandas that runs on a single Agent Platform Workbench instance. Due to a recent increase in dataset size, the script is failing with out of memory (OOM) errors. You need to scale this workload to run across multiple nodes to handle the data volume. The data science team wants to use Python-native APIs and minimize the effort required to rewrite the code or manage infrastructure. What should you do?

A. Use the Agent Platform SDK to create a Ray on Agent Platform cluster. Update the script to use Ray Data libraries for distributed processing, and submit the job to the cluster.

B. Provision a Google Kubernetes Engine (GKE) cluster. Deploy the dask-kubernetes operator.
Update the script to use Dask for distributed dataframe operations that are managed by the team.

C. Update the script to use Apache Beam. Run the job on Dataflow.

D. Containerize the script. Create a pipeline on Agent Platform Pipelines using the Kubeflow Pipelines (KFP) SDK that executes the container on a machine type with higher memory.

정답: A

설명: (Fast2test 회원만 볼 수 있음)

문제7

You need to train an XGBoost model on a small dataset. Your training code requires custom dependencies. You need to set up a Vertex AI custom training job. You want to minimize the startup time of the training job while following Google-recommended practices. What should you do?

A. Use the XGBoost prebuilt custom container. Create a Python source distribution that includes the data and installs the custom dependencies at runtime. In your training application, load the data into a pandas DataFrame and train the model.

B. Store the data in a Cloud Storage bucket, and use the XGBoost prebuilt custom container to run your training application. Create a Python source distribution that installs the custom dependencies at runtime. In your training application, read the data from Cloud Storage and train the model.

C. Store the data in a Cloud Storage bucket, and create a custom container with your training application and its custom dependencies. In your training application, read the data from Cloud Storage and train the model.

D. Create a custom container that includes the data and the custom dependencies. In your training application, load the data into a pandas DataFrame and train the model.

정답: B

설명: (Fast2test 회원만 볼 수 있음)

문제8

You are using Vertex AI to manage your ML models and datasets. You recently updated one of your models. You want to track and compare the new version with the previous one and incorporate dataset versioning. What should you do?

A. Use Vertex AI TensorBoard to visualize the training metrics of the new model version, and use Data Catalog to manage dataset versioning.

B. Use Vertex AI Experiments to track and compare model artifacts and versions, and use Vertex AI managed datasets to manage dataset versioning.

C. Use Vertex AI Experiments to track and compare model artifacts and versions, and use Vertex ML Metadata to manage dataset versioning.

D. Use Vertex AI Model Monitoring to monitor the performance of the new model version, and use Vertex AI Training to manage dataset versioning.

정답: C

설명: (Fast2test 회원만 볼 수 있음)

문제9

You need to build an ML model for a social media application to predict whether a user's submitted profile photo meets the requirements. The application will inform the user if the picture meets the requirements. How should you build a model to ensure that the application does not falsely accept a non-compliant picture?

A. Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that do not meet the profile photo requirements.

B. Use AutoML to optimize the model's recall in order to minimize false negatives.

C. Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that meet the profile photo requirements.

D. Use AutoML to optimize the model's F1 score in order to balance the accuracy of false positives and false negatives.

정답: D

설명: (Fast2test 회원만 볼 수 있음)

Google Professional Machine Learning Engineer - Professional-Machine-Learning-Engineer무료 덤프문제 풀어보기

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