Google Professional-Data-Engineer問題集

質問 1：
You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?
A. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud Dataprep job.
B. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 0 using a custom script.
C. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataprep job.
D. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 'none' using a Cloud Dataproc job.
正解：C

質問 2：
You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?
A. Feedforward neural network
B. Linear regression
C. Recurrent neural network
D. Logistic classification
正解：B

質問 3：
You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query - -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?
A. Recreate the table with a partitioning column and clustering column.
B. Use the LIMIT keyword to reduce the number of rows returned.
C. Create a separate table for each ID.
D. Use the bq query - -maximum_bytes_billed flag to restrict the number of bytes billed.
正解：B

質問 4：
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 user_features). How should you create the ML pipeline?
A. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
B. 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.
C. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.
D. Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
正解：B

質問 5：
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on non-key columns. What should you do?
A. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
B. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
C. Use Cloud SQL for storage. Add secondary indexes to support query patterns.
D. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
正解：A

質問 6：
You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? (Choose two.)
A. Error handling in the subscriber code is not handling run-time errors properly.
B. The subscriber code cannot keep up with the messages.
C. Total outstanding messages exceed the 10-MB maximum.
D. Publisher throughput quota is too small.
E. The subscriber code does not acknowledge the messages that it pulls.
正解：A,B

一年間無料で問題集をアップデートするサービスを提供します。

弊社の商品をご購入になったことがあるお客様に一年間の無料更新サービスを提供いたします。弊社は毎日問題集が更新されたかどうかを確認しますから、もし更新されたら、弊社は直ちに最新版のProfessional-Data-Engineer問題集をお客様のメールアドレスに送信いたします。ですから、試験に関連する情報が変わったら、あなたがすぐに知ることができます。弊社はお客様がいつでも最新版のGoogle Professional-Data-Engineer学習教材を持っていることを保証します。

弊社は無料でGoogle Cloud Certified試験のDEMOを提供します。

Pass4Testの試験問題集はPDF版とソフト版があります。PDF版のProfessional-Data-Engineer問題集は印刷されることができ、ソフト版のProfessional-Data-Engineer問題集はどのパソコンでも使われることもできます。両方の問題集のデモを無料で提供し、ご購入の前に問題集をよく理解することができます。

簡単で便利な購入方法：ご購入を完了するためにわずか2つのステップが必要です。弊社は最速のスピードでお客様のメールボックスに製品をお送りします。あなたはただ電子メールの添付ファイルをダウンロードする必要があります。

領収書について：社名入りの領収書が必要な場合には、メールで社名に記入して頂き送信してください。弊社はPDF版の領収書を提供いたします。