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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Processing and Analytics	20-30%	- Use BigQuery and SQL for analytics - Aggregate and summarize data - Build and maintain data pipelines - Apply statistical methods for analysis - Query and analyze datasets
Topic 2: Data Visualization and Insights	20-30%	- Create dashboards and reports - Interpret and communicate findings - Build visualizations using Looker Studio - Present data insights to stakeholders - Choose appropriate visualization types
Topic 3: Data Preparation and Exploration	20-30%	- Identify data quality issues - Explore data through visualization and queries - Transform and prepare data for analysis - Ingest and acquire data - Perform exploratory data analysis (EDA)
Topic 4: Data-Driven Decision Making	10-20%	- Assess data quality and completeness - Define success metrics - Identify stakeholders and requirements - Translate business requirements into data solutions

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Google Associate Data Practitioner Sample Questions (Q12-Q17):

NEW QUESTION # 12
Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?

A. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
B. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
C. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
D. Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.

Answer: A

NEW QUESTION # 13
Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

A. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
B. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
C. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
D. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.

Answer: A

NEW QUESTION # 14
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 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.
B. Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
C. Use authorized views to share query results with the payroll specialist.
D. 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.

Answer: C

NEW QUESTION # 15
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?

A. ETL
B. ELT
C. EL
D. ETLT

Answer: A

NEW QUESTION # 16
You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types.
You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?

A. Configure lifecycle management rules to transition objects to appropriate storage classes based on access patterns. Set up Object Versioning for all objects to meet immutability requirements.
B. Move objects to different storage classes based on their age and access patterns. Use Cloud Key Management Service (Cloud KMS) to encrypt specific objects with customer-managed encryption keys (CMEK) to meet immutability requirements.
C. Create a Cloud Run function to periodically check object metadata, and move objects to the appropriate storage class based on age and access patterns. Use object holds to enforce immutability for specific objects.
D. Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.

Answer: D

NEW QUESTION # 17
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