Associate-Data-Practitioner試験無料問題集（108題）「Google Cloud Associate Data Practitioner 認定」

出題：1

Your organization plans to move their on-premises environment to Google Cloud. Your organization's network bandwidth is less than 1 Gbps. You need to move over 500 ## of data to Cloud Storage securely, and only have a few days to move the data. What should you do?

A. Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.

B. Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.

C. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.

D. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.

正解：B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：2

You are storing data in Cloud Storage for a machine learning project. The data is frequently accessed during the model training phase, minimally accessed after 30 days, and unlikely to be accessed after 90 days. You need to choose the appropriate storage class for the different stages of the project to minimize cost. What should you do?

A. Store the data in Nearline storage during the model training phase. Transition the data to Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

B. Store the data in Standard storage during the model training phase. Transition the data to Durable Reduced Availability (DRA) storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

C. Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

D. Store the data in Nearline storage during the model training phase. Transition the data to Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：3

You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

A. Export the data from BigQuery to a local machine. Use scikit-learn in a Jupyter notebook to build the churn prediction model.

B. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.

C. Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.

D. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：4

You work for a financial services company that handles highly sensitive data. Due to regulatory requirements, your company is required to have complete and manual control of data encryption. Which type of keys should you recommend to use for data storage?

A. Use customer-supplied encryption keys (CSEK).

B. Use a dedicated third-party key management system (KMS) chosen by the company.

C. Use customer-managed encryption keys (CMEK).

D. Use Google-managed encryption keys (GMEK).

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：5

Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

A. Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.

B. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.

C. Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.

D. Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting

正解：B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：6

Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

A. Create a BigQuery table that uses the SUM( ) function and the _PARTITIONDATE filter.

B. Create a BigQuery table that uses the SUM( ) function and the DATE_DIFF( ) function.

C. Create a saved query in the BigQuery console that uses the SUM( ) function and the DATE_SUB( ) function. Re-run the saved query every month, and save the results to a BigQuery table.

D. Create a materialized view in BigQuery that uses the SUM( ) function and the DATE_SUB( ) function.

正解：D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：7

Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

A. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;

B. CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ' except(churned) FROM customer data;

C. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype='logisric_reg') AS SELECT * from cusromer_data;

D. CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select churned as label FROM customer_data;

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：8

Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

A. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.

B. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.

C. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.

D. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.

正解：C 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：9

Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

A. Use a Transfer Appliance.

B. Use Storage Transfer Service.

C. Use Cloud Storage FUSE.

D. Use gcloud storage commands.

正解：B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：10

Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

A. Create a native table.

B. Create an object table.

C. Create a temporary table.

D. Create an external table.

正解：B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

Associate-Data-Practitioner試験無料問題集「Google Cloud Associate Data Practitioner 認定」