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The Associate Data Practitioner exam is one of the most valuable certification exams. The Google Associate Data Practitioner exam opens a door for beginners or experienced TestsDumps professionals to enhance in-demand skills and gain knowledge. ADP Exam credential is proof of candidates' expertise and knowledge. After getting success in the Google Associate Data Practitioner exam, candidates can put their careers on the fast route and achieve their goals in a short period of time.
| Section | Weight | Objectives |
|---|
| Data Visualization and Insights | 20-30% | - Create dashboards and reports - Build visualizations using Looker Studio - Present data insights to stakeholders - Interpret and communicate findings - Choose appropriate visualization types
|
| Data-Driven Decision Making | 10-20% | - Identify stakeholders and requirements - Define success metrics - Translate business requirements into data solutions - Assess data quality and completeness
|
| Data Preparation and Exploration | 20-30% | - Ingest and acquire data - Explore data through visualization and queries - Identify data quality issues - Perform exploratory data analysis (EDA) - Transform and prepare data for analysis
|
| Data Processing and Analytics | 20-30% | - Build and maintain data pipelines - Aggregate and summarize data - Use BigQuery and SQL for analytics - Apply statistical methods for analysis - Query and analyze datasets
|
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Google Associate Data Practitioner Sample Questions (Q42-Q47):
NEW QUESTION # 42
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?
Answer: A
NEW QUESTION # 43
You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users' personally identifiable information (PII) that will be analyzed with SQL.
3. A large volume of small document files that will power other applications.
You need to select the appropriate tool for each data type given the required use case, while following Google- recommended practices. Which should you choose?
- A. Filestore Bigtable BigQuery
- B. Cloud Storage BigQuery Firestore
- C. Filestore Cloud SQL for PostgreSQL Datastore
- D. Cloud Storage CloudSQL for PostgreSQL Bigtable
Answer: B
NEW QUESTION # 44
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?
- A. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
- B. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
- C. Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
- D. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
Answer: B
NEW QUESTION # 45
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?
- A. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
- B. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
- C. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
- D. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
Answer: A
NEW QUESTION # 46
Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?
- A. Set temporary object holds.
- B. Set a bucket retention policy.
- C. Enable Object Versioning.
- D. Change the bucket storage class to Archive.
Answer: B
NEW QUESTION # 47
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