Google Cloud Associate Data Practitioner Exam Practice Test

Page: 1 / 14
Total 106 questions
Question 1

You are designing an application that will interact with several BigQuery datasets. You need to grant the application's service account permissions that allow it to query and update tables within the datasets, and list all datasets in a project within your application. You want to follow the principle of least privilege. Which pre-defined IAM role(s) should you apply to the service account?



Answer : A

roles/bigquery.jobUser:

This role allows a user or service account to run BigQuery jobs, including queries. This is necessary for the application to interact with and query the tables.

From Google Cloud documentation: 'BigQuery Job User can run BigQuery jobs, including queries, load jobs, export jobs, and copy jobs.'

roles/bigquery.dataOwner:

This role grants full control over BigQuery datasets and tables. It allows the service account to update tables, which is a requirement of the application.

From Google Cloud documentation: 'BigQuery Data Owner can create, delete, and modify BigQuery datasets and tables. BigQuery Data Owner can also view data and run queries.'

Why other options are incorrect:

B . roles/bigquery.connectionUser and roles/bigquery.dataViewer:

roles/bigquery.connectionUser is used for external connections, which is not required for this task. roles/bigquery.dataViewer only allows viewing data, not updating it.

C . roles/bigquery.admin:

roles/bigquery.admin grants excessive permissions. Following the principle of least privilege, this role is too broad.

D . roles/bigquery.user and roles/bigquery.filteredDataViewer:

roles/bigquery.user grants the ability to run queries, but not the ability to modify data. roles/bigquery.filteredDataViewer only provides permission to view filtered data, which is not sufficient for updating tables.

Principle of Least Privilege:

The principle of least privilege is a security concept that states that a user or service account should be granted only the permissions necessary to perform its intended tasks.

By assigning roles/bigquery.jobUser and roles/bigquery.dataOwner, we provide the application with the exact permissions it needs without granting unnecessary access.

Google Cloud Documentation Reference:

BigQuery IAM roles: https://cloud.google.com/bigquery/docs/access-control-basic-roles

IAM best practices: https://cloud.google.com/iam/docs/best-practices-for-using-iam


Question 2

You have a Cloud SQL for PostgreSQL database that stores sensitive historical financial dat

a. 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?



Answer : A

Comprehensive and Detailed In-Depth

The priorities are data integrity, recoverability after a regional disaster, low RPO (minimal data loss), and low latency for primary operations. Let's analyze:

Option A: Multi-region backups store point-in-time snapshots in a separate region. With automated backups and transaction logs, RPO can be near-zero (e.g., minutes), and recovery is possible post-disaster. Primary operations remain in one zone, minimizing latency.

Option B: Regional HA (failover to another zone) with hourly cross-region backups protects against zone failures, but hourly backups yield an RPO of up to 1 hour---too high for valuable data. Manual backup management adds overhead.

Option C: Synchronous replication to another zone ensures zero RPO within a region but doesn't protect against regional loss. Latency increases slightly due to sync writes across zones.

Option D: Asynchronous replication to another region reduces RPO (e.g., seconds) but increases primary write latency (waiting for async confirmation) and doesn't guarantee zero data loss in a crash. Why A is Best: Multi-region backups balance low RPO (continuous logs + frequent backups) with no latency impact on primary operations (single-zone instance). For example, enabling backups to us-east1 from a us-west1 instance ensures recoverability without affecting performance, prioritizing RPO as requested. Extract from Google Documentation: From 'Cloud SQL Backup and Recovery' (https://cloud.google.com/sql/docs/postgres/backup-recovery): 'Configure multi-region backups to store data in a different region, ensuring recoverability after a regional failure with minimal RPO using automated backups and transaction logs, while keeping primary operations low-latency.' Reference: Google Cloud Documentation - 'Cloud SQL Backups' (https://cloud.google.com/sql/docs/postgres/backup-recovery).

Why A is Best: Multi-region backups balance low RPO (continuous logs + frequent backups) with no latency impact on primary operations (single-zone instance). For example, enabling backups to us-east1 from a us-west1 instance ensures recoverability without affecting performance, prioritizing RPO as requested.

Extract from Google Documentation: From 'Cloud SQL Backup and Recovery' (https://cloud.google.com/sql/docs/postgres/backup-recovery): 'Configure multi-region backups to store data in a different region, ensuring recoverability after a regional failure with minimal RPO using automated backups and transaction logs, while keeping primary operations low-latency.'

Option D: Asynchronous replication to another region reduces RPO (e.g., seconds) but increases primary write latency (waiting for async confirmation) and doesn't guarantee zero data loss in a crash. Why A is Best: Multi-region backups balance low RPO (continuous logs + frequent backups) with no latency impact on primary operations (single-zone instance). For example, enabling backups to us-east1 from a us-west1 instance ensures recoverability without affecting performance, prioritizing RPO as requested. Extract from Google Documentation: From 'Cloud SQL Backup and Recovery' (https://cloud.google.com/sql/docs/postgres/backup-recovery): 'Configure multi-region backups to store data in a different region, ensuring recoverability after a regional failure with minimal RPO using automated backups and transaction logs, while keeping primary operations low-latency.' Reference: Google Cloud Documentation - 'Cloud SQL Backups' (https://cloud.google.com/sql/docs/postgres/backup-recovery).


Question 3

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?



Answer : C

Creating external tables over the Parquet files in Cloud Storage allows you to perform SQL-based analysis and joins with data already in BigQuery without needing to load the files into BigQuery. This approach is efficient for a one-time analysis as it avoids the time and cost associated with loading large volumes of data into BigQuery. External tables provide seamless integration with Cloud Storage, enabling quick and cost-effective analysis of data stored in Parquet format.


Question 4

You are constructing a data pipeline to process sensitive customer data stored in a Cloud Storage bucket. You need to ensure that this data remains accessible, even in the event of a single-zone outage. What should you do?



Answer : C

Storing the data in a multi-region bucket ensures high availability and durability, even in the event of a single-zone outage. Multi-region buckets replicate data across multiple locations within the selected region, providing resilience against zone-level failures and ensuring that the data remains accessible. This approach is particularly suitable for sensitive customer data that must remain available without interruptions.

A single-zone outage requires high availability across zones or regions. Cloud Storage offers location-based redundancy options:

Option A: Cloud CDN caches content for web delivery but doesn't protect against underlying storage outages---it's for performance, not availability of the source data.

Option B: Object Versioning retains old versions of objects, protecting against overwrites or deletions, but doesn't ensure availability during a zone failure (still tied to one location).

Option C: Multi-region buckets (e.g., us or eu) replicate data across multiple regions, ensuring accessibility even if a single zone or region fails. This provides the highest availability for sensitive data in a pipeline.

Option D: Nearline storage is a low-cost class with lower availability (single-region by default), not designed for outage resilience. Why C is Best: Multi-region storage offers geo-redundancy, guaranteeing data access during outages with no additional configuration. It's the simplest, most reliable choice for pipeline continuity. Extract from Google Documentation: From 'Cloud Storage Bucket Locations' (https://cloud.google.com/storage/docs/locations): 'Multi-region buckets replicate data across multiple regions within a geography, providing high availability and durability, ensuring access even if a single zone or region experiences an outage.' Reference: Google Cloud Documentation - 'Cloud Storage Locations' (https://cloud.google.com/storage/docs/locations).


Question 5

You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?



Answer : A

Using BigQuery ML is the best solution in this case because:

Ease of use: BigQuery ML allows users to build machine learning models using SQL, which requires minimal ML expertise.

Integrated platform: Since the data already exists in BigQuery, there's no need to move it to another service, saving time and engineering resources.

Logistic regression: This is an appropriate model for binary classification tasks like predicting the likelihood of a customer making a purchase in the next month.


Question 6

You are working on a project that requires analyzing daily social media dat

a. 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?



Answer : C

Comprehensive and Detailed in Depth

Why C is correct:Dataflow is a fully managed service for transforming and enriching data in both batch and streaming modes.

Dataflow is googles recomended way to transform large datasets.

It is designed for parallel processing, making it suitable for large datasets.

Why other options are incorrect:A: Manual downloading and scripting is not scalable or efficient.

B: Cloud Run functions are for stateless applications, not large data transformations.

D: While Cloud Data fusion could work, Dataflow is more optimized for large scale data transformation.


Dataflow: https://cloud.google.com/dataflow/docs

Query successful

Question 7

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?



Answer : D

Comprehensive and Detailed in Depth

Why D is correct:Creating a BigQuery external table directly from the Google Sheet allows for real-time updates.

Joining the external table with the actual cost table in BigQuery performs the calculation.

Connected Sheets allows the team to access and analyze the results directly in Google Sheets, with the data being updated.

Why other options are incorrect:A: Saving as a CSV file loses the live connection and daily updates.

B: Downloading and uploading as a CSV file adds unnecessary steps and loses the live connection.

C: Same issue as B, losing the live connection.


BigQuery External Tables: https://cloud.google.com/bigquery/docs/external-tables

Connected Sheets: https://support.google.com/sheets/answer/9054368?hl=en

Page:    1 / 14   
Total 106 questions