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

문제1

You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.
Which Google Cloud services should you recommend for the pipeline?

A. 1. Dataproc Serverless
2. Bigtable

B. 1. BigQuery
2. Analytics Hub

C. 1. Dataflow
2. BigQuery

D. 1. Cloud Composer
2. Cloud SQL for MySQL

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제2

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

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

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

정답: B

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문제3

Your company is adopting BigQuery as their data warehouse platform. Your team has experienced Python developers. You need to recommend a fully-managed tool to build batch ETL processes that extract data from various source systems, transform the data using a variety of Google Cloud services, and load the transformed data into BigQuery. You want this tool to leverage your team's Python skills. What should you do?

A. Use Dataform with assertions.

B. Use Dataflow and pre-built templates.

C. Deploy Cloud Data Fusion and included plugins.

D. Use Cloud Composer with pre-built operators.

정답: D

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문제4

You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

A. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.

B. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.

C. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.

D. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제5

You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

A. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.

B. Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.

C. Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.

D. Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제6

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. Create a BigQuery remote function that invokes the Cloud Translation API. Use a scheduled query to translate new reviews.

B. 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

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 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.

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제7

Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?

A. Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the --no- clobber option.

B. Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the -- daisy-chain option.

C. Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.

D. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제8

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.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제9

Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department. What should you do?

A. Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.

B. Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.

C. Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.

D. Create a custom quota for each analyst in BigQuery.

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제10

Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?

A. Create a BigQuery materialized view that performs a cross join across all of the regional sales tables.Use the row_number() window function to query the new materialized view.

B. Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.

C. Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.

D. Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

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

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