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Google Professional-Machine-Learning-Engineer Online Access

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Question 76

You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction. During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery. New training data is added every week. You want to make the process more efficient by minimizing computation time and manual intervention. What should you do?

Options:

A.

Normalize the data using Google Kubernetes Engine

B.

Translate the normalization algorithm into SQL for use with BigQuery

C.

Use the normalizer_fn argument in TensorFlow's Feature Column API

D.

Normalize the data with Apache Spark using the Dataproc connector for BigQuery

Question 77

You work on the data science team at a manufacturing company. You are reviewing the company's historical sales data, which has hundreds of millions of records. For your exploratory data analysis, you need to calculate descriptive statistics such as mean, median, and mode; conduct complex statistical tests for hypothesis testing; and plot variations of the features over time You want to use as much of the sales data as possible in your analyses while minimizing computational resources. What should you do?

Options:

A.

Spin up a Vertex Al Workbench user-managed notebooks instance and import the dataset Use this data to create statistical and visual analyses

B.

Visualize the time plots in Google Data Studio. Import the dataset into Vertex Al Workbench user-managed notebooks Use this data to calculate the descriptive statistics and run the statistical analyses

C.

Use BigQuery to calculate the descriptive statistics. Use Vertex Al Workbench user-managed notebooks to visualize the time plots and run the statistical analyses.

D Use BigQuery to calculate the descriptive statistics, and use Google Data Studio to visualize the time plots. Use Vertex Al Workbench user-managed notebooks to run the statistical analyses.

Question 78

You are an ML engineer in the contact center of a large enterprise. You need to build a sentiment analysis tool that predicts customer sentiment from recorded phone conversations. You need to identify the best approach to building a model while ensuring that the gender, age, and cultural differences of the customers who called the contact center do not impact any stage of the model development pipeline and results. What should you do?

Options:

A.

Extract sentiment directly from the voice recordings

B.

Convert the speech to text and build a model based on the words

C.

Convert the speech to text and extract sentiments based on the sentences

D.

Convert the speech to text and extract sentiment using syntactical analysis

Question 79

You are building a MLOps platform to automate your company's ML experiments and model retraining. You need to organize the artifacts for dozens of pipelines How should you store the pipelines' artifacts'?

Options:

A.

Store parameters in Cloud SQL and store the models' source code and binaries in GitHub

B.

Store parameters in Cloud SQL store the models' source code in GitHub, and store the models' binaries in Cloud Storage.

C.

Store parameters in Vertex ML Metadata store the models' source code in GitHub and store the models' binaries in Cloud Storage.

D.

Store parameters in Vertex ML Metadata and store the models source code and binaries in GitHub.

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Exam Name: Google Professional Machine Learning Engineer
Last Update: Nov 25, 2024
Questions: 285
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