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Snowflake DEA-C01 Exam Syllabus Topics:
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NEW QUESTION # 40
A Data Engineer executes a complex query and wants to make use of Snowflake s query results caching capabilities to reuse the results.
Which conditions must be met? (Select THREE).
- A. The results must be reused within 72 hours.
- B. The USED_CACHED_RESULT parameter must be included in the query.
- C. The new query must have the same syntax as the previously executed query.
- D. The query must be executed using the same virtual warehouse.
- E. The table structure contributing to the query result cannot have changed
- F. The micro-partitions cannot have changed due to changes to other data in the table
Answer: A,C,E
Explanation:
Explanation
Snowflake's query results caching capabilities allow users to reuse the results of previously executed queries without re-executing them. For this to happen, the following conditions must be met:
The results must be reused within 24 hours (not 72 hours), which is the default time-to-live (TTL) for cached results.
The query must be executed using any virtual warehouse (not necessarily the same one), as long as it is in the same region and account as the original query.
The USED_CACHED_RESULT parameter does not need to be included in the query, as it is enabled by default at the account level. However, it can be disabled or overridden at the session or statement level.
The table structure contributing to the query result cannot have changed, such as adding or dropping columns, changing data types, or altering constraints.
The new query must have the same syntax as the previously executed query, including whitespace and case sensitivity.
The micro-partitions cannot have changed due to changes to other data in the table, such as inserting, updating, deleting, or merging rows.
NEW QUESTION # 41
Jonas, a Lead Performance Engineer,identifed that some of the operation of his query which func-tionally remove the duplicates from huge data set is spilling the data to remote disk. How can he alleviate spilling to a remote disk for better query performance?
- A. Jonas can recommend using a large warehouse which effectively increase the available memory/local disk space for the operations.
- B. He can Process data in smaller batches to manage workload.
- C. Data Sharing can be helpful to improve query performance.
- D. Spilling do not have a profound effect on query performance (especially if remote disk is used for spilling).
Answer: A,B
Explanation:
Explanation
For some operations (e.g. duplicate elimination for a huge data set), the amount of memory available for the compute resources used to execute the operation might not be sufficient to hold intermediate results. As a result, the query processing engine will start spilling the data to local disk. If the local disk space is not sufficient, the spilled data is then saved to remote disks.
This spilling can have a profound effect on query performance (especially if remote disk is used for spilling).
To alleviate this, It is recommend that:
Using a larger warehouse (effectively increasing the available memory/local disk space for the op-eration), and/or Processing data in smaller batches.
NEW QUESTION # 42
The following chart represents the performance of a virtual warehouse over time:
A DataEngineer notices that the warehouse is queueing queries The warehouse is size X-Smallthe minimum and maximum cluster counts are set to 1 the scaling policy is set to i and auto-suspend is set to 10 minutes.
How canthe performance be improved?
- A. Change the cluster settings
- B. Increase the size of the warehouse
- C. Change the scaling policy to economy
- D. Change auto-suspend to a longer time frame
Answer: B
Explanation:
Explanation
The performance can be improved by increasing the size of the warehouse. The chart shows that the warehouse is queueing queries, which means that there are more queries than the warehouse can handle at its current size. Increasing the size of the warehouse will increase its processing power and concurrency limit, which could reduce the queueing time and improve the performance. The other options are not likely to improve the performance significantly. Option A, changing the cluster settings, will not help unless the minimum and maximum cluster countsare increased to allow for multi-cluster scaling. Option C, changing the scaling policy to economy, will not help because it will reduce the responsiveness of the warehouse to scale up or down based on demand. Option D, changing auto-suspend to a longer time frame, will not help because it will only affect how long the warehouse stays idle before suspending itself.
NEW QUESTION # 43
A data engineer uses Amazon Redshift to run resource-intensive analytics processes once every month. Every month, the data engineer creates a new Redshift provisioned cluster. The data engineer deletes the Redshift provisioned cluster after the analytics processes are complete every month. Before the data engineer deletes the cluster each month, the data engineer unloads backup data from the cluster to an Amazon S3 bucket.
The data engineer needs a solution to run the monthly analytics processes that does not require the data engineer to manage the infrastructure manually.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month.
- B. Use the AWS CLI to automatically process the analytics workload.
- C. Use AWS CloudFormation templates to automatically process the analytics workload.
- D. Use Amazon Redshift Serverless to automatically process the analytics workload.
Answer: D
Explanation:
Use Amazon Redshift Serverless. This option allows the data engineer to focus on the analytics processes themselves without worrying about cluster provisioning, scaling, or management. It provides an on-demand, serverless solution that can handle variable workloads and is cost- effective for intermittent and irregular processing needs like those described.
NEW QUESTION # 44
Melissa, Senior Data Engineer, looking out to optimize query performance for one of the Critical Control Dashboard, she found that most of the searches by the users on the control dashboards are based on Equality search on all the underlying columns mostly. Which Best techniques she should consider here?
- A. A materialized view speeds both equality searches and range searches.
- B. She can go for clustering on underlying tables which can speedup Equality searches.
- C. Melissa can create Indexes & Hints on the searchable columns to speed up Equality search.
- D. The search optimization service would best fit here as it can be applied to all underlying columns & speeds up equality searches.
(Correct)
Answer: D
Explanation:
Explanation
Clustering a table can speed any of the following, as long as they are on the clustering key:
Range searches.
Equality searches.
However, a table can be clustered on only a single key (which can contain one or more columns or expressions).
The search optimization service speeds equality searches. However, this applies to all the columns of supported types in a table that has search optimization enabled. This is what required here& best fit for purpose.
A materialized view speeds both equality searches and range searches, as well as some sort opera-tions, but only for the subset of rows and columns included in the materialized view.
NEW QUESTION # 45
Can Masking policies be applied to virtual columns?
- A. FALSE
- B. TRUE
Answer: A
NEW QUESTION # 46
A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisioned capacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Amazon Athena to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
- B. Use AWS Lake Formation to create a data lake. Use Lake Formation jobs to transform the data from all data sources to Apache Parquet format. Store the transformed data in an S3 bucket. Use Amazon Athena or Redshift Spectrum to query the data.
- C. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format. Store the transformed data in an S3 bucket. Use Amazon Athena to query the original and transformed data from the S3 bucket.
- D. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Redshift Spectrum to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.
Answer: A
NEW QUESTION # 47
A company uses AWS Step Functions to orchestrate a data pipeline. The pipeline consists of Amazon EMR jobs that ingest data from data sources and store the data in an Amazon S3 bucket. The pipeline also includes EMR jobs that load the data to Amazon Redshift.
The company's cloud infrastructure team manually built a Step Functions state machine. The cloud infrastructure team launched an EMR cluster into a VPC to support the EMR jobs.
However, the deployed Step Functions state machine is not able to run the EMR jobs.
Which combination of steps should the company take to identify the reason the Step Functions state machine is not able to run the EMR jobs? (Choose two.)
- A. Check the retry scenarios that the company configured for the EMR jobs. Increase the number of seconds in the interval between each EMR task. Validate that each fallback state has the appropriate catch for each decision state. Configure an Amazon Simple Notification Service (Amazon SNS) topic to store the error messages.
- B. Use AWS CloudFormation to automate the Step Functions state machine deployment. Create a step to pause the state machine during the EMR jobs that fail. Configure the step to wait for a human user to send approval through an email message. Include details of the EMR task in the email message for further analysis.
- C. Query the flow logs for the VPC. Determine whether the traffic that originates from the EMR cluster can successfully reach the data providers. Determine whether any security group that might be attached to the Amazon EMR cluster allows connections to the data source servers on the informed ports.
- D. Verify that the Step Functions state machine code has all IAM permissions that are necessary to create and run the EMR jobs. Verify that the Step Functions state machine code also includes IAM permissions to access the Amazon S3 buckets that the EMR jobs use. Use Access Analyzer for S3 to check the S3 access properties.
- E. Check for entries in Amazon CloudWatch for the newly created EMR cluster. Change the AWS Step Functions state machine code to use Amazon EMR on EKS. Change the IAM access policies and the security group configuration for the Step Functions state machine code to reflect inclusion of Amazon Elastic Kubernetes Service (Amazon EKS).
Answer: C,D
Explanation:
https://docs.aws.amazon.com/step-functions/latest/dg/procedure-create-iam-role.html
https://docs.aws.amazon.com/step-functions/latest/dg/service-integration-iam-templates.html
NEW QUESTION # 48
You have been tasked with migrating an on-premises MySQL database to Amazon Aurora PostgreSQL using AWS Database Migration Service (DMS). The stakeholder emphasizes that the source database must remain fully operational during the migration process.
Which of the following statements about DMS is accurate with respect to this scenario?
- A. AWS DMS requires the source MySQL database to be version 5.7 or higher for migrating to Amazon Aurora PostgreSQL.
- B. When using DMS, the target Amazon Aurora PostgreSQL instance cannot be accessed or queried until the migration is complete.
- C. AWS DMS only supports full-load migrations, which would require downtime for the source database.
- D. AWS DMS supports both full-load and continuous replication, allowing the source MySQL database to remain operational during migration.
- E. AWS DMS can convert the MySQL database schema directly to PostgreSQL without any manual intervention.
Answer: D
NEW QUESTION # 49
How can the following relational data be transformed into semi-structured data using the LEAST amount of operational overhead?
- A. Use the OBJECT_CONSTRUCT function to return a Snowflake object
- B. Use the PAESE_JSON function to produce a variant value
- C. Use the TO_VARIANT function to convert each of the relational columns to VARIANT.
- D. Use the to_json function
Answer: A
Explanation:
Explanation
This option is the best way to transform relational data into semi-structured data using the least amount of operational overhead. The OBJECT_CONSTRUCT function takes a variable number of key-value pairs as arguments and returns a Snowflake object, which is a variant type that can store JSON data. The function can be used to convert each row of relational data into a JSON object with the column names as keys and the column values as values.
NEW QUESTION # 50
The following code is executed ina Snowflake environment with the default settings:
What will be the result of the select statement?
- A. SQL compilation error object CUSTOMER' does not exist or is not authorized.
- B. John
- C. 0
- D. 1John
Answer: C
NEW QUESTION # 51
A gaming company uses a NoSQL database to store customer information. The company is planning to migrate to AWS.
The company needs a fully managed AWS solution that will handle high online transaction processing (OLTP) workload, provide single-digit millisecond performance, and provide high availability around the world.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Amazon Keyspaces (for Apache Cassandra)
- B. Amazon DynamoDB
- C. Amazon DocumentDB (with MongoDB compatibility)
- D. Amazon Timestream
Answer: B
NEW QUESTION # 52
How Data Engineer can do Monitoring of Files which are Staged Internally during Continuous data pipelines loading process? [Select all that apply]
- A. She Can Monitor the files using Metadata maintained by Snowflake i.e. file-name,last_modified date etc.
- B. Snowflake retains historical data for COPY INTO commands executed within the pre-vious 14 days.
- C. She can use the DATA_LOAD_HISTORY Information Schema view to retrieve the history of data loaded into tables using the COPY INTO command.
- D. She can Monitor the status of each COPY INTO <table> command on the History tab page of the classic web interface.
- E. She can use the DATA_VALIDATE function to validate the data files She have loaded and can retrieve any errors encountered during the load.
Answer: A,B,D
Explanation:
Explanation
Monitoring Files Staged Internally
Snowflake maintains detailed metadata for each file uploaded into internal stage (for users, tables, and stages), including:
File name
File size (compressed, if compression was specified during upload)
LAST_MODIFIED date, i.e. the timestamp when the data file was initially staged or when it was last modified, whichever is later In addition, Snowflake retains historical data for COPY INTO commands executed within the pre-vious 14 days. The metadata can be used to monitor and manage the loading process, including de-leting files after upload completes:
Use the LIST command to view the status of data files that have been staged.
Monitor the status of each COPY INTO <table> command on the History tab page of the classic web interface.
Use the VALIDATE function to validate the data files you've loaded and retrieve any errors en-countered during the load.
Use the LOAD_HISTORY Information Schema view to retrieve the history of data loaded into tables using the COPY INTO command.
NEW QUESTION # 53
Which functions will compute a 'fingerprint' over an entire table, query result, or window to quickly detect changes to table contents or query results? (Select TWO).
- A. HASH_AGG(*)
- B. HASH_AGG_COMPARE (*)
- C. HASH (*)
- D. HASH_AGG(<expr>, <expr>)
- E. HASH COMPARE(*)
Answer: A,D
Explanation:
Explanation
The functions that will compute a 'fingerprint' over an entire table, query result, or window to quickly detect changes to table contents or query results are:
HASH_AGG(*): This function computes a hash value over all columns and rows in a table, query result, or window. The function returns a single value for each group defined by a GROUP BY clause, or a single value for the entire input if no GROUP BY clause is specified.
HASH_AGG(<expr>, <expr>): This function computes a hash value over two expressions in a table, query result, or window. The function returns a single value for each group defined by a GROUP BY clause, or a single value for the entire input if no GROUP BY clause is specified. The other functions are not correct because:
HASH (*): This function computes a hash value over all columns in a single row. The function returns one value per row, not one value per table, query result, or window.
HASH_AGG_COMPARE (): This function compares two hash values computed by HASH_AGG() over two tables or query results and returns true if they are equal or false if they are different. The function does not compute a hash value itself, but rather compares two existing hash values.
HASH COMPARE(): This function compares two hash values computed by HASH() over two rows and returns true if they are equal or false if they are different. The function does not compute a hash value itself, but rather compares two existing hash values.
NEW QUESTION # 54
Streams record the differences between two offsets. If a row is added and then updated in the cur-rent offset, what will be the value of METADATA$ISUPDATE Columns in this scenario?
- A. UPDATE
- B. INSERT
- C. FALSE
- D. TRUE
Answer: C
Explanation:
Explanation
Stream Columns
A stream stores an offset for the source object and not any actual table columns or data. When que-ried, a stream accesses and returns the historic data in the same shape as the source object (i.e. the same column names and ordering) with the following additional columns:
METADATA$ACTION
Indicates the DML operation (INSERT, DELETE) recorded.
METADATA$ISUPDATE
Indicates whether the operation was part of an UPDATE statement. Updates to rows in the source object are represented as a pair of DELETE and INSERT records in the stream with a metadata column METADATA$ISUPDATE values set to TRUE.
METADATA$ROW_ID
Specifies the unique and immutable ID for the row, which can be used to track changes to specific rows over time.
Note that streams record the differences between two offsets. If a row is added and then updated in the current offset, the delta change is a new row. The METADATA$ISUPDATE row records a FALSE value.
NEW QUESTION # 55
A company uses Apache Airflow to orchestrate the company's current on-premises data pipelines. The company runs SQL data quality check tasks as part of the pipelines. The company wants to migrate the pipelines to AWS and to use AWS managed services.
Which solution will meet these requirements with the LEAST amount of refactoring?
- A. Convert the pipelines to AWS Step Functions workflows. Recreate the data quality checks in SQL as Python based AWS Lambda functions.
- B. Create a custom Amazon Machine Image (AMI) that contains the Airflow application and the code that the company needs to migrate. Use the custom AMI to deploy Amazon EC2 instances.
Update the network connections to interact with the newly deployed EC2 instances. - C. Setup AWS Outposts in the AWS Region that is nearest to the location where the company uses Airflow. Migrate the servers into Outposts hosted Amazon EC2 instances. Update the pipelines to interact with the Outposts hosted EC2 instances instead of the on-premises pipelines.
- D. Migrate the existing Airflow orchestration configuration into Amazon Managed Workflows for Apache Airflow (Amazon MWAA). Create the data quality checks during the ingestion to validate the data quality by using SQL tasks in Airflow.
Answer: D
Explanation:
Amazon MWAA is a managed service for running Apache Airflow. It allows migrating existing Airflow configurations with minimal changes. Data quality checks can continue to be implemented as SQL tasks in Airflow, similar to the current setup.
NEW QUESTION # 56
A SQL UDF evaluates an arbitrary SQL expression and returns the result(s) of the expression. Which value type it can returns?
- A. Single Value
- B. A Set of Rows
- C. Scaler or Tabular depend on input SQL expression
- D. Regex
Answer: C
NEW QUESTION # 57
A company has used an Amazon Redshift table that is named Orders for 6 months. The company performs weekly updates and deletes on the table. The table has an interleaved sort key on a column that contains AWS Regions.
The company wants to reclaim disk space so that the company will not run out of storage space.
The company also wants to analyze the sort key column.
Which Amazon Redshift command will meet these requirements?
- A. VACUUM REINDEX Orders
- B. VACUUM SORT ONLY Orders
- C. VACUUM DELETE ONLY Orders
- D. VACUUM FULL Orders
Answer: D
NEW QUESTION # 58
A company is designing a data lake on Amazon S3. To ensure high performance when accessing the data, which best practice should the company adopt in organizing its data in the S3 bucket?
- A. Use a flat structure by avoiding the creation of any prefix or "folder" hierarchy.
- B. Store all data files as a single large file and use AWS Lambda to parse required data segments.
- C. Partition data based on commonly accessed attributes and use a consistent naming scheme for prefixes.
- D. Enable S3 Transfer Acceleration to ensure data is quickly accessible from any location.
Answer: C
NEW QUESTION # 59
A data engineer must orchestrate a data pipeline that consists of one AWS Lambda function and one AWS Glue job. The solution must integrate with AWS services.
Which solution will meet these requirements with the LEAST management overhead?
- A. Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job.
- B. Use an AWS Step Functions workflow that includes a state machine. Configure the state machine to run the Lambda function and then the AWS Glue job.
- C. Use an Apache Airflow workflow that is deployed on an Amazon EC2 instance. Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.
- D. Use an Apache Airflow workflow that is deployed on Amazon Elastic Kubernetes Service (Amazon EKS). Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.
Answer: B
NEW QUESTION # 60
Which system role is recommended for a custom role hierarchy to be ultimately assigned to?
- A. USERADMIN
- B. ACCOUNTADMIN
- C. SYSTEMADMIN
- D. SECURITYADMIN
Answer: D
Explanation:
Explanation
The system role that is recommended for a custom role hierarchy to be ultimately assigned to is SECURITYADMIN. This role has the manage grants privilege on all objects in an account, which allows it to grant access privileges to other roles or revoke them as needed. This role can also create or modify custom roles and assign them to users or other roles. By assigning custom roles to SECURITYADMIN, the role hierarchy can be managed centrally and securely. The other options are not recommended system roles for a custom role hierarchy to be ultimately assigned to. Option A is incorrect because ACCOUNTADMIN is the most powerful role in an account, which has full access to all objects and operations. Assigning custom roles to ACCOUNTADMIN can pose a security risk and should be avoided. Option C is incorrect because SYSTEMADMIN is a role that has full access to all objects in the public schema of the account, but not to other schemas or databases. Assigning custom roles to SYSTEMADMIN can limit the scope and flexibility of the role hierarchy. Option D is incorrect because USERADMIN is a role that can manage users and roles in an account, but not grant access privileges to other objects. Assigning custom roles to USERADMIN can prevent the role hierarchy from controlling access to data and resources.
NEW QUESTION # 61
SYSTEM$CLUSTERING_INFORMATION functions returns clustering information, including average clustering depth, for a table based on one or more columns in the table. The function returns a JSON object containing average_overlaps name/value pairs. Does High average_overlaps indicates well organized Clustering?
- A. YES
- B. NO
Answer: B
Explanation:
Explanation
Higher the avg_overlap indicates poorly organized clustering.
NEW QUESTION # 62
A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour.
Which combination of tasks will meet these requirements with the LEAST operational overhead?
(Choose two.)
- A. Use AWS Glue DataBrew to clean and prepare the data for analytics.
- B. Use the Redshift Data API to load transformed data into Amazon Redshift.
- C. Use AWS Lambda functions to schedule and run the ETL jobs every hour.
- D. Configure AWS Glue triggers to run the ETL jobs every hour.
- E. Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift.
Answer: D,E
Explanation:
AWS Glue triggers provide a simple and integrated way to schedule ETL jobs. By configuring these triggers to run hourly, the data engineer can ensure that the data processing and updates occur as required without the need for external scheduling tools or custom scripts. This approach is directly integrated with AWS Glue, reducing the complexity and operational overhead.
AWS Glue supports connections to various data sources, including Amazon RDS and MongoDB.
By using AWS Glue connections, the data engineer can easily configure and manage the connectivity between these data sources and Amazon Redshift. This method leverages AWS Glue's built-in capabilities for data source integration, thus minimizing operational complexity and ensuring a seamless data flow from the sources to the destination (Amazon Redshift).
NEW QUESTION # 63
Mark the incorrect statement in case Data engineer using the COPY INTO <table> command to load data from files into Snowflake tables?
- A. For Local environment, Files are first copied ("staged") to an internal (Snowflake) stage, then loaded into a table.
- B. For loading data from all semi-structured supported file formats (JSON, Avro, etc.), as well as unloading data, UTF-8 is the only supported character set.
- C. UTF-32 & UTF-16 both encoding character sets supported for loading data from de-limited files (CSV, TSV, etc.)
- D. For Data loading of files with semi-structured file formats (JSON, Avro, etc.), the only supported character set is UTF-16.
Answer: D
Explanation:
Explanation
For Data Loading of delimited files (CSV, TSV, etc.), the default character set is UTF-8. To use any other characters sets, you must explicitly specify the encoding to use for loading.
For semi-structured file formats (JSON, Avro, etc.), the only supported character set is UTF-8.
Rest of the statements are correct.
NEW QUESTION # 64
A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs.
The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.
Which solution will meet these requirements?
- A. Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.
- B. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.
- C. Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.
- D. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.
Answer: D
NEW QUESTION # 65
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