Google - ADP - Instant Associate Data Practitioner Access

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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Pipeline Orchestration	18%	- Pipeline design and automation 1. Dataflow pipelines 2. Workflow orchestration concepts (e.g., Cloud Composer)
Data Analysis and Presentation	27%	- Data visualization 1. Looker / Looker Studio reporting - Querying and analyzing data 1. Data aggregation and transformation 2. BigQuery SQL usage
Data Preparation and Ingestion	30%	- Data ingestion into Google Cloud services 1. Cloud Storage ingestion patterns 2. Streaming ingestion with Pub/Sub concepts
Data Management	25%	- Storage and data organization 1. BigQuery datasets and tables management 2. Cloud Storage lifecycle policies - Data governance and security 1. IAM and access control 2. Data lifecycle management

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Quiz Updated ADP - Instant Associate Data Practitioner Access

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Google Associate Data Practitioner Sample Questions (Q56-Q61):

NEW QUESTION # 56
You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?

A. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
B. Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.
C. Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.
D. Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.

Answer: B

NEW QUESTION # 57
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?

A. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
B. Use Colab Enterprise to develop a custom model for purchase prediction.
C. Use Vertex Al Workbench to develop a custom model for purchase prediction.
D. Use BigQuery ML to create a logistic regression model for purchase prediction.

Answer: D

NEW QUESTION # 58
Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?

A. Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
B. Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.
C. Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.
D. Use authorized views to share query results with the payroll specialist.

Answer: D

NEW QUESTION # 59
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?

A. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
B. Use Dataflow to transform the data and write the transformed data to BigQuery.
C. Use Cloud Run functions to transform and load the data into BigQuery.
D. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.

Answer: B

NEW QUESTION # 60
You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

A. Use Dataproc to run the transformations on a cluster.
B. Use Cloud Data Fusion to visually design and manage the pipeline.
C. Use Dataflow with a custom template for the transformation logic.
D. Use Dataform to define the transformations in SQLX.

Answer: A

NEW QUESTION # 61
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