Free Google Professional-Machine-Learning-Engineer Exam Dumps Questions & Answers

You are in the exploratory phase of development of a demand forecasting model. Several terabytes of historical sales and inventory records are stored in a BigQuery table. You need to create a new, clean table to perform preliminary analyses on metrics, such as moving averages of sales figures and weekly aggregates, without additional data movement. You want to use the simplest approach and minimize overhead. What should you do?

• A.  Write a SQL query in the BigQuery console. Use the ML.TRANSFORM function in BigQuery ML to compute the moving averages and extract time-based features. Write the processed data to a new destination table in BigQuery.
• B.  Read the data from BigQuery into a Dask DataFrame using Agent Platform Workbench. Perform exploratory data analysis (EDA) and feature engineering transformations, and write the processed data back to a new destination table in BigQuery.
• C.  Write a SQL query in the BigQuery console. Use standard SQL functions like CAST, EXTRACT, and window functions with OVER() clauses to perform the transformations and calculations.
  Materialize the results into a new destination table in BigQuery.
• D.  Create a PreprocessingOp component in the Agent Platform Pipelines workflow to compute the moving averages and extract time-based features and write the processed data to a new destination table in BigQuery.

You work for a healthcare organization and need to automate the patient triage process. You want to build a telephony agentic solution using the Gemini Live API to perform human-like conversations with patients and gather necessary patient information. You plan to pilot the solution in Japanese. You need to minimize latency and development time, and ensure high, native-like fluency in the model's responses. What should you do?

• A.  Use Gemini Live API Speech-to-Text functionality to capture the user's input followed by Text-to- Speech streaming processing with Chirp to communicate with the patient in their language.
• B.  Use the Gemini Pro model to automatically select the appropriate language.
• C.  Fine-tune the Gemini Flash model on a dataset of Japanese medical transcripts to ensure native fluency. Deploy this custom model behind the Gemini Live API.
• D.  Use the Gemini Live API, and configure the language_code field to ja-JP. Include a clear system instruction to respond in the patient's language.

You are part of a team of ML engineers building a new churn prediction model using PyTorch.
Your team's raw data is stored in BigQuery. For the initial prototyping phase, your team needs a collaborative, cloud-based environment that meets the following criteria:
- It must provide access to GPU accelerators.
- It must allow seamless authentication to BigQuery without manual
management of service account keys.
- It must be pre-configured with common ML frameworks.
You need to choose the most secure solution with the least setup overhead for your team to begin model development and experimentation. What should you do?

• A.  Configure a JupyterHub server on a cluster of Compute Engine instances. Install the necessary GPU drivers and other required libraries on each instance. Instruct each team member to create and download a service account key for BigQuery access.
• B.  Create Agent Platform Workbench instances for your team members. Select instance types with attached GPUs and the preinstalled PyTorch kernel. Use the BigQuery client library to access the data, and use the instances' managed identity for authentication.
• C.  Instruct team members to use the free version of Agent Platform Colab Enterprise. Use the Agent Platform SDK for Python to load the data into a shared Agent Platform Colab Enterprise notebook. Have each member perform analysis in the shared notebook.
• D.  Package the PyTorch model code in a Docker container, and upload it to Artifact Registry. Use Agent Platform Serverless Training to run a custom training job that runs on a GPU-enabled machine type. Instruct the team to analyze the training logs to iterate on the model.

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

You are developing a classification model to support predictions for your company's various products. The dataset you were given for model development has class imbalance You need to minimize false positives and false negatives What evaluation metric should you use to properly train the model?

• A.  Accuracy
• B.  Recall
• C.  F1 score
• D.  Precision