Associate Data Practitioner Free Practice Questions — Page 2
Question 5
Your team uses Google Sheets to track budget data that is updated daily. The team wants to compare budget data against actual cost data, which is stored in a BigQuery table. You need to create a solution that calculates the difference between each day's budget and actual costs. You want to ensure that your team has access to daily-updated results in Google Sheets. What should you do?
A. Download the budget data as a CSV file and upload the CSV file to a Cloud Storage bucket. Create a new BigQuery table from Cloud Storage, and join the actual cost table with it. Open the joined BigOuery table by using Connected Sheets.
B. Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table, and open it by using Connected Sheets.
C. Download the budget data as a CSV file, and upload the CSV file to create a new BigQuery table. Join the actual cost table with the new BigQuery table, and save the results as a CSV file. Open the CSV file in Google Sheets.
D. Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table as a CSV file and open the file in Google Sheets.
Show Answer
Correct Answer: B
Explanation: Create a BigQuery external table that references the Google Sheet, then join it with the actual-cost table. Connected Sheets lets the team access and refresh the BigQuery results from Google Sheets; exporting the results to CSV would create a static copy.
Question 6
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 --daisy-chain option.
B. Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
C. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
D. Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the --no-clobber option.
Show Answer
Correct Answer: B
Explanation: Use Storage Transfer Service with an agent-based transfer and set the agent pool’s bandwidth limit to 250 Mbps. This supports an online transfer from the on-premises file server to Cloud Storage while controlling bandwidth usage. Transfer Appliance is an offline method, and the `gcloud storage cp` options listed do not provide the required managed bandwidth limit.
Question 7
You manage an ecommerce website that has a diverse range of product. 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 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 the historical sales data to train and create a BigQueryML time series model. Use the ML.FORECAST function call to output the predictions into a new BigQuery table.
B. Use Colab Enterprise to create a Jupyter notebook. Use the historical sales data to train a custom prediction model in Python.
C. Use the historical sales data to train and create a BigQueryML 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 BigQueryML logistic regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table.
Show Answer
Correct Answer: A
Explanation: BigQuery ML time-series models are designed to forecast future values from historical data and can account for seasonality. Train the model on the sales data, then use ML.FORECAST to write demand predictions to a BigQuery table. Linear and logistic regression are not the appropriate choices for this time-series forecasting task.
Question 8
You have a Cloud SQL for PostgreSQL database that stores sensitive historical financial data. You need to ensure that the data is uncorrupted and recoverable in the event that the primary region is destroyed. The data is valuable, so you need to prioritize recovery point objective (RPO) over recovery time objective (RTO). You want to recommend a solution that minimizes latency for primary read and write operations. What should you do?
A. Configure the Cloud SQL for PostgreSQL instance for multi-region backup locations.
B. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with synchronous replication to a secondary instance in a different zone.
C. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with asynchronous replication to a secondary instance in a different region.
D. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA). Back up the Cloud SQL for PostgreSQL database hourly to a Cloud Storage bucket in a different region.
Show Answer
Correct Answer: C
Explanation: A cross-region asynchronous replica keeps a recent copy of the database outside the primary region without making primary writes wait for cross-region replication. If the primary region is destroyed, the replica can be promoted. Same-region HA does not survive a regional disaster, while periodic backups generally have a larger RPO.
Sources:
https://cloud.google.com/sql/docs/postgres/intro-to-cloud-sql-disaster-recovery
https://cloud.google.com/sql/docs/postgres/replication
Question 9
You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?
A. Use Cloud Client Libraries to transfer the data over the internet.
B. Compress the data, upload it to multiple cloud storage providers, and then transfer the data to Cloud Storage.
C. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
D. Use the gcloud storage command to transfer the data over the internet.
Show Answer
Correct Answer: C
Explanation: At 100 Mbps, transferring 300 TB over the internet would take many months, even before accounting for overhead. A Transfer Appliance is designed for moving large datasets when network bandwidth is constrained: copy the data to the appliance and ship it to Google for upload to Cloud Storage.
Question 10
You are working on a project that requires analyzing dally social media data. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing. You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?
A. Use Cloud Data Fusion to transfer the data into BigOuery raw tables, and use SQL to transform it.
B. Use Dataflow to transform the data and write the transformed data to BigQuery.
C. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
D. Use Cloud Run functions to transform and load the data into BigOuery.
Show Answer
Correct Answer: B
Explanation: Use Dataflow to process the growing JSON dataset at scale, transform the records, and write the results to BigQuery. This is a scalable managed approach for batch or streaming data pipelines.
Question 11
You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?
A. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
B. Grant each partner read access to the BigQuery dataset by using IAM roles.
C. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucketlevel access by using IAM roles.
D. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
Show Answer
Correct Answer: A
Explanation: Use Analytics Hub to share the dataset through subscriptions, avoiding copies and enabling governed, scalable partner access. Apply BigQuery row-level security and column-level controls (such as policy tags or authorized views) to limit what each partner can see.
Question 12
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. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
D. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
Show Answer
Correct Answer: D
Explanation: Export the raw feedback text from BigQuery and use AutoML Natural Language to train a custom sentiment analysis model. Keeping the text raw preserves useful cues such as emojis, slang, and punctuation that preprocessing could remove or alter.
Question 13
Your organization's website uses an on-premises MySQL as a backend database. You need to migrate the on-premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?
A. Use a Google-provided Dataflow template to replicate the MySQL database in BigOuery.
B. Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.
C. Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on-premises MySQL database as the source.
D. Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.
Show Answer
Correct Answer: C
Explanation: Use Database Migration Service to migrate the on-premises MySQL database to Cloud SQL for MySQL. This preserves MySQL compatibility, minimizes administration through managed services, and supports replication to reduce downtime during migration.
Question 14
Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse. You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?
A. Use Dataform to define the transformations in SQLX.
B. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
C. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryInsertJob operator.
D. Create BigQuery scheduled queries to define the transformations in SQL.
Show Answer
Correct Answer: A
Explanation: Use Dataform to define the BigQuery transformations in SQLX. It supports SQL and JavaScript, modular workflows, version control integration, and data quality checks through assertions.
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