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Google Certified Professional Data Engineer Exam問題集を今すぐダウンロード

質問 53
A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL `dataset.model’, table . How should you create the ML pipeline?
user_features)

• A. Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query.
  Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.
• B. Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
• C. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
• D. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.

正解: A

質問 54
Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the dat
a. Which three machine learning applications can you use? (Choose three.)

• A. Unsupervised learning to predict the location of a transaction.
• B. Unsupervised learning to determine which transactions are most likely to be fraudulent.
• C. Reinforcement learning to predict the location of a transaction.
• D. Supervised learning to predict the location of a transaction.
• E. Supervised learning to determine which transactions are most likely to be fraudulent.
• F. Clustering to divide the transactions into N categories based on feature similarity.

正解: B,D,F

質問 55
A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning.
Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

• A. Implement clustering in BigQuery on the package-tracking ID column.
• B. Re-create the table using data partitioning on the package delivery date.
• C. Tier older data onto Cloud Storage files, and leverage extended tables.
• D. Implement clustering in BigQuery on the ingest date column.

正解: D

質問 56
Which of the following are examples of hyperparameters? (Select 2 answers.)

• A. Number of nodes in each hidden layer
• B. Number of hidden layers
• C. Weights
• D. Biases

正解: A,B

解説:
If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many “hidden” layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.
Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.

質問 57
Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data. They want to improve this performance while minimizing cost. What should they do?

• A. Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.
• B. Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.
• C. The performance issue should be resolved over time as the site of the BigDate cluster is increased.
• D. Redefine the schema by evenly distributing reads and writes across the row space of the table.

正解: D

解説:
https://cloud.google.com/bigtable/docs/performance#troubleshooting
If you find that you’re reading and writing only a small number of rows, you might need to redesign your schema so that reads and writes are more evenly distributed.

質問 58
……