최신 Associate-Data-Practitioner 무료덤프 - Google Cloud Associate Data Practitioner

문제1

You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

A. Update dataset privileges by using the SQL GRANT statement.

B. Encrypt the data by using customer-managed encryption keys (CMEK).

C. Enable access control by using IAM roles.

D. Export the data to Cloud Storage, and use signed URLs to authorize access.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제2

You manage an ecommerce website that has a diverse range of products. You need to forecast future product demand accurately to ensure that your company has sufficient inventory to meet customer needs and avoid stockouts. Your company's historical sales data is stored in a BigQuery table. You need to create a scalable solution that takes into account the seasonality and historical data to predict product demand. What should you do?

A. Use Colab Enterprise to create a Jupyter notebook. Use the historical sales data to train a custom prediction model in Python.

B. Use the historical sales data to train and create a BigQuery ML time series model. Use the ML.
FORECAST function call to output the predictions into a new BigQuery table.

C. Use the historical sales data to train and create a BigQuery ML linear regression model. Use the ML.
PREDICT function call to output the predictions into a new BigQuery table.

D. Use the historical sales data to train and create a BigQuery ML logistic regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table.

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제3

You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) Gemini. You need to plan and execute this analysis using the most efficient approach. What should you do?

A. Use a BigQuery ML model to pre-process the text data, export the results to Cloud Storage, and use the Gemini API to summarize the pre- processed data.

B. Query the BigQuery table from within a Python notebook, use the Gemini API to summarize the data within the notebook, and store the summaries in BigQuery.

C. Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use Gemini to summarize the data.

D. Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the Gemini API to summarize the data.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제4

You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

A. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.

B. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.

C. Use Cloud Run functions to trigger the Spark job and email the report.

D. Use Cloud Composer to orchestrate the Spark job and email the report.

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제5

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.

B.

C.

D.

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제6

You are using your own data to demonstrate the capabilities of BigQuery to your organization's leadership team. You need to perform a one-time load of the files stored on your local machine into BigQuery using as little effort as possible. What should you do?

A. Execute the bq load command on your local machine.

B. Create a Dataproc cluster, copy the files to Cloud Storage, and write an Apache Spark job using the spark-bigquery-connector.

C. Write and execute a Python script using the BigQuery Storage Write API library.

D. Create a Dataflow job using the Apache Beam FileIO and BigQueryIO connectors with a local runner.

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제7

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 Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.

B. 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.

C. 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.

D. 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.

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

최신 Associate-Data-Practitioner 무료덤프 - Google Cloud Associate Data Practitioner

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