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Google Associate Data Practitioner Exam Questions

Name: Google Associate Data Practitioner Exam
Brand: Pass4Success
SKU: Associate Data Practitioner
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Exam Name: Google Cloud Associate Data Practitioner Exam

Exam Code: Associate Data Practitioner

Related Certification(s):

Google Cloud Certified Certifications
Google Data Practitioner Certifications

Certification Provider: Google

Actual Exam Duration: 120 Minutes

Number of Associate Data Practitioner practice questions in our database: 106 (updated: Sep. 28, 2026)

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Zainab Hashmi

16 days ago

I saw questions on data formats and schema handling that asked which format to choose for analytics workloads, requiring you to weigh columnar versus row formats and schema evolution needs. Study Parquet and Avro tradeoffs, BigQuery ingestion behavior, and how compression and partitioning affect query performance practical examples help. I passed the exam.

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Sumayya Rizvi

26 days ago

I just passed the Google Cloud Associate Data Practitioner exam, and the biggest help was doing hands on practice with BigQuery loads and schema choices since the questions leaned into real ingestion tradeoffs. Make sure you can spot when to use batch versus streaming and how that affects cost and latency.

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Olivia Campbell

2 months ago

Questions about batch versus streaming ingestion were tricky because they presented data arrival patterns and asked which combination of services to use, such as Cloud Storage with scheduled loads or Pub/Sub with Dataflow. Focus on latency, ordering guarantees, and cost implications for each service when studying these scenarios. I managed to pass the exam and thanks Pass4Success for providing good collection of exam questions for preparation in short time.

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Free Google Associate Data Practitioner Exam Actual Questions

Note: Premium Questions for Associate Data Practitioner were last updated On Sep. 28, 2026 (see below)

Question #1

Your company has several retail locations. Your company tracks the total number of sales made at each location each day. You want to use SQL to calculate the weekly moving average of sales by location to identify trends for each store. Which query should you use?

AOption A

BOption B

COption C

DOption D

Reveal Solution Hide Solution

Correct Answer: C

To calculate the weekly moving average of sales by location:

The query must group by store_id (partitioning the calculation by each store).

The ORDER BY date ensures the sales are evaluated chronologically.

The ROWS BETWEEN 6 PRECEDING AND CURRENT ROW specifies a rolling window of 7 rows (1 week if each row represents daily data).

The AVG(total_sales) computes the average sales over the defined rolling window.

Chosen query meets these requirements:

Extract from Google Documentation: From 'Analytic Functions in BigQuery' (https://cloud.google.com/bigquery/docs/reference/standard-sql/analytic-function-concepts): 'Use ROWS BETWEEN n PRECEDING AND CURRENT ROW with ORDER BY a time column to compute moving averages over a fixed number of rows, such as a 7-day window, partitioned by a grouping key like store_id.' Reference: Google Cloud Documentation - 'BigQuery Window Functions' (https://cloud.google.com/bigquery/docs/reference/standard-sql/window-function-calls).

Question #2

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?

AManually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.

BUse Cloud Run functions to transform and load the data into BigQuery.

CUse Dataflow to transform the data and write the transformed data to BigQuery.

DUse Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.

Reveal Solution Hide Solution

Correct 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 #3

You need to create a data pipeline for a new application. Your application will stream data that needs to be enriched and cleaned. Eventually, the data will be used to train machine learning models. You need to determine the appropriate data manipulation methodology and which Google Cloud services to use in this pipeline. What should you choose?

AETL; Dataflow -> BigQuery

BETL; Cloud Data Fusion -> Cloud Storage

CELT; Cloud Storage -> Bigtable

DELT; Cloud SQL -> Analytics Hub

Reveal Solution Hide Solution

Correct Answer: A

Comprehensive and Detailed In-Depth

Streaming data requiring enrichment and cleaning before ML training suggests an ETL (Extract, Transform, Load) approach, with a focus on real-time processing and a data warehouse for ML.

Option A: ETL with Dataflow (streaming transformations) and BigQuery (storage/ML training) is Google's recommended pattern for streaming pipelines. Dataflow handles enrichment/cleaning, and BigQuery supports ML model training (BigQuery ML).

Option B: ETL with Cloud Data Fusion to Cloud Storage is batch-oriented and lacks streaming focus. Cloud Storage isn't ideal for ML training directly.

Option C: ELT (load then transform) with Cloud Storage to Bigtable is misaligned---Bigtable is for NoSQL, not ML training or post-load transformation.

Option D: ELT with Cloud SQL to Analytics Hub is for relational data and data sharing, not streaming or ML. Reference: Google Cloud Documentation - 'Dataflow: ETL Patterns' (https://cloud.google.com/dataflow/docs/guides), 'BigQuery ML' (https://cloud.google.com/bigquery-ml).

Question #4

You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

AUse Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.

BUse the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.

CUse BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.

DUse Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.

Reveal Solution Hide Solution

Correct Answer: A

Comprehensive and Detailed in Depth

Why A is correct:Cloud Monitoring is the recommended service for monitoring Google Cloud services, including Dataflow.

It allows you to track key metrics like system lag, element throughput, and error rates.

Alerting policies in Cloud Monitoring can trigger notifications based on metric thresholds.

Why other options are incorrect:B: The Dataflow job monitoring interface is useful for visualization, but Cloud Monitoring provides more comprehensive alerting.

C: BigQuery is for analyzing the processed data, not monitoring the pipeline itself. Also Cloud Storage is not where the data resides during processing.

D: Cloud Logging is useful for viewing logs, but Cloud Monitoring is better for metric-based alerting.

Cloud Monitoring for Dataflow: https://cloud.google.com/dataflow/docs/guides/using-monitoring

Cloud Monitoring: https://cloud.google.com/monitoring/docs

Question #5

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?

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

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

CCreate 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.

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

Reveal Solution Hide Solution

Correct Answer: B

Comprehensive and Detailed In-Depth

For a small dataset with simple transformations (filtering, aggregation), Google recommends leveraging BigQuery's native SQL capabilities to minimize cost and complexity.

Option A: Dataproc with Spark is overkill for a small dataset, incurring cluster management costs and setup time.

Option B: BigQuery can load data directly from Cloud Storage (e.g., CSV, JSON) and perform transformations using SQL in a serverless manner, avoiding additional service costs. This is the most efficient and cost-effective approach.

Option C: Cloud Data Fusion is suited for complex ETL but adds overhead (instance setup, UI design) unnecessary for simple tasks.

Option D: Dataflow is powerful for large-scale or streaming ETL but introduces unnecessary complexity and cost for a small, simple batch job. Extract from Google Documentation: From 'Loading Data into BigQuery from Cloud Storage' (https://cloud.google.com/bigquery/docs/loading-data-cloud-storage): 'You can load data directly from Cloud Storage into BigQuery and use SQL queries to transform it without needing additional processing tools, making it cost-effective for simple transformations.' Reference: Google Cloud Documentation - 'BigQuery Data Loading' (https://cloud.google.com/bigquery/docs/loading-data).

Extract from Google Documentation: From 'Loading Data into BigQuery from Cloud Storage' (https://cloud.google.com/bigquery/docs/loading-data-cloud-storage): 'You can load data directly from Cloud Storage into BigQuery and use SQL queries to transform it without needing additional processing tools, making it cost-effective for simple transformations.'

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