Google Associate-Data-Practitioner 問題集

質問 1：
Your organization sends IoT event data to a Pub/Sub topic. Subscriber applications read and perform transformations on the messages before storing them in the data warehouse. During particularly busy times when more data is being written to the topic, you notice that the subscriber applications are not acknowledging messages within the deadline. You need to modify your pipeline to handle these activity spikes and continue to process the messages. What should you do?
A. Forward unacknowledged messages to a dead-letter topic.
B. Seek back to the last acknowledged message.
C. Retry messages until they are acknowledged.
B Implement flow control on the subscribers
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 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. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
B. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
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.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 3：
Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?
A. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
B. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
C. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
D. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 4：
Your company's ecommerce website collects product reviews from customers. The reviews are loaded as CSV files daily to a Cloud Storage bucket. The reviews are in multiple languages and need to be translated to Spanish. You need to configure a pipeline that is serverless, efficient, and requires minimal maintenance.
What should you do?
A. Load the data into BigQuery using a Cloud Run function. Use the BigQuery ML create model statement to train a translation model. Use the model to translate the product reviews within BigQuery.
B. Load the data into BigQuery using a Cloud Run function. Create a BigQuery remote function that invokes the Cloud Translation API. Use a scheduled query to translate new reviews.
C. Use a Dataflow templates pipeline to translate the reviews using the Cloud Translation API. Set BigQuery as the sink.
D. Load the data into BigQuery using Dataproc. Use Apache Spark to translate the reviews by invoking the Cloud Translation API. Set BigQuery as the sink.U
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 5：
You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional data. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?
A. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
B. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
C. Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.
D. Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 6：
Following a recent company acquisition, you inherited an on-premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort.
What should you do?
A. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
B. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
C. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
D. Create a new Cloud Composer environment and copy DAGs to the Cloud Composer dags/ folder.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 7：
Your company has several retail locations. Your company tracks the total number of sales made at each location each day. You want to use SQL to calculate the weekly moving average of sales by location to identify trends for each store. Which query should you use?
A.

B.

C.

D.

正解：D
解説: (Topexam メンバーにのみ表示されます)