Google ADP 시험 개요:

Google ADP 시험 요강 주제:

최신 Google Cloud Certified ADP 무료샘플문제

1. You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

A) Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
B) Use Vertex Al Workbench to develop a custom model for purchase prediction.
C) Use BigQuery ML to create a logistic regression model for purchase prediction.
D) Use Colab Enterprise to develop a custom model for purchase prediction.

2. You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A) Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
B) Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
C) Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
D) Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.

3. You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?

A) Evaluate the data skewness.
B) Evaluate data drift.
C) Compare the two different models.
D) Compare the confusion matrix.

4. You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A) Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
B) Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
C) Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
D) Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.

5. You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

A) Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
B) Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
C) Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.
D) Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.

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