Google Cloud Certified Associate Data Practitioner (ADP) practice questions

Domain coverage 4 domains

• Data Preparation and Ingestion (~30%)
• Data Analysis and Presentation (~27%)
• Data Pipeline Orchestration (~18%)
• Data Management and Governance (~25%)

Sample questions 7 of 65 shown

1. Q1 Understanding Data and Data Types

   Your company has developed a website that allows users to upload and share video files. These files are most frequently accessed and shared when they are initially uploaded. Over time, the files are accessed and shared less frequently, although some old video files may remain very popular. You need to design a storage system that is simple and cost-effective. What should you do?

   1. Create a single-region bucket with Autoclass enabled.
   2. Create a single-region bucket. Configure a Cloud Scheduler job that runs every 24 hours and changes the storage class based on upload date.
   3. Create a single-region bucket with custom Object Lifecycle Management policies based on upload date.
   4. Create a single-region bucket with Archive as the default storage class.

   A. Cloud Storage Autoclass automatically moves objects between Standard, Nearline, Coldline, and Archive classes based on access patterns, providing optimal cost without manual lifecycle rules. Custom lifecycle (C) and Cloud Scheduler (B) require manual configuration. Archive default (D) would be expensive for frequently accessed new files.
2. Q2 Understanding Data and Data Types

   You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage. The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

   1. EL
   2. ELT
   3. ETL
   4. ETLT

   C. ETL (Extract, Transform, Load) transforms data before loading, allowing SQL injection removal during the transform phase before data reaches BigQuery. EL (A) has no transform step. ELT (B) loads raw data first - risky with SQL injection. ETLT (D) is non-standard and adds unnecessary complexity.
3. Q3 Understanding Data and Data Types

   You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need to clean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

   1. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
   2. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
   3. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
   4. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.

   A. Cloud Composer with PythonOperator provides a managed Apache Airflow environment for cleaning data with Python, ideal for complex data quality tasks before loading to BigQuery. BigQuery batch load (B) doesn't allow pre-load Python cleaning. Storage Transfer + Cloud Run (C) adds complexity. Cloud Run functions (D) lack orchestration for complex pipelines.
4. Q4 Understanding Data and Data Types

   Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud. You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

   1. Migrate the Spark jobs to Dataproc Serverless.
   2. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
   3. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
   4. Migrate the Spark jobs to Dataproc on Compute Engine.

   A. Dataproc Serverless runs Spark jobs without cluster provisioning or management, using auto-scaling infrastructure. GKE with Spark operators (B) requires cluster management. Dataproc on GKE (C) still needs K8s management. Dataproc on Compute Engine (D) requires cluster provisioning.
5. Q5 Understanding Data and Data Types

   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?

   1. Create a custom quota for each analyst in BigQuery.
   2. Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
   3. 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.
   4. 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.

   D. BigQuery authorized views allow sharing specific query results with teams without granting direct table access. Dataset-level IAM (A) is too broad. Data Catalog (B) is metadata management. BigQuery subscriptions (C) are for Pub/Sub integration, not access control.
6. Q6 Understanding Data and Data Types

   You manage a web application that stores data in a Cloud SQL database. You need to improve the read performance of the application by offloading read traffic from the primary database instance. You want to implement a solution that minimizes effort and cost. What should you do?

   1. Use Cloud CDN to cache frequently accessed data.
   2. Store frequently accessed data in a Memorystore instance.
   3. Migrate the database to a larger Cloud SQL instance.
   4. Enable automatic backups, and create a read replica of the Cloud SQL instance.

   D. Cloud SQL read replicas offload read queries from the primary, improving read performance without modifying the application database. Memorystore (A) requires cache code changes. Load balancing reads (B) needs application changes. Cloud SQL connection pooling (C) manages connections, not reads.
7. Q7 Understanding Data and Data Types

   Your organization sends IoT event data to a Pub/Sub topic. Subscriber applications read and perform transformations on the messages before storing them in the data warehouse. During particularly busy times when more data is being written to the topic, you notice that the subscriber applications are not acknowledging messages within the deadline. You need to modify your pipeline to handle these activity spikes and continue to process the messages. What should you do?

   1. Retry messages until they are acknowledged.
   2. Implement flow control on the subscribers.
   3. Forward unacknowledged messages to a dead-letter topic.
   4. Seek back to the last acknowledged message.

   B. Dataflow streaming with Pub/Sub subscription processes IoT events in real-time with auto-scaling, perfect for variable IoT workloads. Dataproc Spark Streaming (A) requires cluster management. Cloud Functions (C) has execution time limits unsuitable for sustained streaming. Cloud Composer (D) orchestrates but doesn't process streams.

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