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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q132-Q137):
NEW QUESTION # 132
A data engineer is writing a query to join two tables in Amazon Athena. The data engineer needs to choose the correct join order for the tables to optimize query performance. Which solution will meet these requirements?
- A. Use table statistics to automatically determine the join order.
- B. Specify the smaller table on the left side of the join and the larger table on the right side of the join.
- C. Specify the larger table on the left side of the join and the smaller table on the right side of the join.
- D. Use AWS Glue to pre-process the tables before performing the join.
Answer: B
Explanation:
Amazon Athena (based on Presto/Trino) performs joins more efficiently when the smaller table is placed on the left side because it is used as the build side of the join and can be loaded into memory. This minimizes data shuffling and improves performance when joining with a larger table on the right side.
NEW QUESTION # 133
Which methods can be used to create a DataFrame object in Snowpark? (Select THREE)
- A. session.jdbc_connection()
- B. session.builder()
- C. session,table()
- D. session.sql()
- E. session.read.json{)
- F. DataFraas.writeO
Answer: C,D,E
Explanation:
Explanation
The methods that can be used to create a DataFrame object in Snowpark are session.read.json(), session.table(), and session.sql(). These methods can create a DataFrame from different sources, such as JSON files, Snowflake tables, or SQL queries. The other options are not methods that can create a DataFrame object in Snowpark. Option A, session.jdbc_connection(), is a method that can create a JDBC connection object to connect to a database. Option D, DataFrame.write(), is a method that can write a DataFrame to a destination, such as a file or a table. Option E, session.builder(), is a method that can create a SessionBuilder object to configure and build a Snowpark session.
NEW QUESTION # 134
A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.
The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.
Which solution will meet these requirements with the LOWEST latency?
- A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
- B. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
- C. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard.
- D. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream database. Use the Timestream database as a source to create an Amazon QuickSight dashboard.
Answer: A
Explanation:
Amazon Managed Service for Apache Flink provides real-time stream processing capabilities, which can process sensor data with low latency. By using Apache Flink connectors, the processed data can be efficiently written to Amazon Timestream, which is optimized for time- series data storage and querying.
NEW QUESTION # 135
A company has several new datasets in CSV and JSON formats. A data engineer needs to make the data available to a team of data analysts who will analyze the data by using SQL queries.
Which solution will meet these requirements in the MOST cost-effective way?
- A. Create an Amazon RDS MySQL cluster. Use AWS Glue to transform and load the CSV and JSON files into database tables. Provide the data analysts access to the MySQL cluster.
- B. Create an AWS Glue DataBrew project that contains the new data. Make the DataBrew project available to the data analysts.
- C. Store the data in an Amazon S3 bucket. Use an AWS Glue crawler to catalog the S3 bucket as tables. Create an Amazon Athena workgroup that has a data usage threshold. Grant the data analysts access to the Athena workgroup.
- D. Load the data into Super-fast, Parallel, In-memory Calculation Engine (SPICE) in Amazon QuickSight. Allow the data analysts to create analyses and dashboards in QuickSight.
Answer: C
Explanation:
By storing the CSV and JSON files in Amazon S3 and running an AWS Glue crawler, you automatically catalog them as tables without upfront ETL.
Amazon Athena then lets analysts run SQL queries directly against those tables on a pay-per- query basis. Using an Athena workgroup with a usage threshold keeps costs under control, making this the most cost-effective, low-operational-overhead solution.
NEW QUESTION # 136
Which of the following System keeps the following characteristics?
a. It will keep in it all the raw data.
b. Generally, the users of it is data scientists and data developers.
c. Flat architecture
d. Highly agile
- A. Data Mart
- B. Data Warehouse
- C. Data Lake
- D. Data Hub
Answer: C
NEW QUESTION # 137
......
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