Online Access Free Associate-Data-Practitioner Practice Test

You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

• A.Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
• B.Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
• C.Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
• D.Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.

Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?

• A.Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.
• B.Use the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write the results to BigQuery.
• C.Use a Pub/Sub to Cloud Storage subscription, write a Cloud Run service that is triggered when objects arrive in the bucket, performs the transformations, and writes the results to BigQuery.
• D.Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.

Your organization needs to implement near real-time analytics for thousands of events arriving each second in Pub/Sub. The incoming messages require transformations. You need to configure a pipelinethat processes, transforms, and loads the data into BigQuery while minimizing development time. What should you do?

• A.Create a Cloud Data Fusion instance and configure Pub/Sub as a source. Use Data Fusion to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.
• B.Use Cloud Run functions to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.
• C.Load the data from Pub/Sub into Cloud Storage using a Cloud Storage subscription. Create a Dataproc cluster, use PySpark to perform transformations in Cloud Storage, and write the results to BigQuery.
• D.Use a Google-provided Dataflow template to process the Pub/Sub messages, perform transformations, and write the results to BigQuery.

Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one-time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

• A.Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
• B.Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
• C.Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
• D.Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.

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.Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
• B.Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
• C.Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
• D.Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.