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Professional-Machine-Learning-Engineer Questions and Answers

Question # 1

You are responsible for building a unified analytics environment across a variety of on-premises data marts. Your company is experiencing data quality and security challenges when integrating data across the servers, caused by the use of a wide range of disconnected tools and temporary solutions. You need a fully managed, cloud-native data integration service that will lower the total cost of work and reduce repetitive work. Some members on your team prefer a codeless interface for building Extract, Transform, Load (ETL) process. Which service should you use?

A.

Dataflow

B.

Dataprep

C.

Apache Flink

D.

Cloud Data Fusion

Question # 2

You are developing a custom TensorFlow classification model based on tabular data. Your raw data is stored in BigQuery contains hundreds of millions of rows, and includes both categorical and numerical features. You need to use a MaxMin scaler on some numerical features, and apply a one-hot encoding to some categorical features such as SKU names. Your model will be trained over multiple epochs. You want to minimize the effort and cost of your solution. What should you do?

A.

1 Write a SQL query to create a separate lookup table to scale the numerical features.

2. Deploy a TensorFlow-based model from Hugging Face to BigQuery to encode the text features.

3. Feed the resulting BigQuery view into Vertex Al Training.

B.

1 Use BigQuery to scale the numerical features.

2. Feed the features into Vertex Al Training.

3 Allow TensorFlow to perform the one-hot text encoding.

C.

1 Use TFX components with Dataflow to encode the text features and scale the numerical features.

2 Export results to Cloud Storage as TFRecords.

3 Feed the data into Vertex Al Training.

D.

1 Write a SQL query to create a separate lookup table to scale the numerical features.

2 Perform the one-hot text encoding in BigQuery.

3. Feed the resulting BigQuery view into Vertex Al Training.

Question # 3

You developed a BigQuery ML linear regressor model by using a training dataset stored in a BigQuery table. New data is added to the table every minute. You are using Cloud Scheduler and Vertex Al Pipelines to automate hourly model training, and use the model for direct inference. The feature preprocessing logic includes quantile bucketization and MinMax scaling on data received in the last hour. You want to minimize storage and computational overhead. What should you do?

A.

Create a component in the Vertex Al Pipelines directed acyclic graph (DAG) to calculate the required statistics, and pass the statistics on to subsequent components.

B.

Preprocess and stage the data in BigQuery prior to feeding it to the model during training and inference.

C.

Create SQL queries to calculate and store the required statistics in separate BigQuery tables that are referenced in the CREATE MODEL statement.

D.

Use the TRANSFORM clause in the CREATE MODEL statement in the SQL query to calculate the required statistics.

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